diff --git a/.gitignore b/.gitignore index 2c58eb0..9a52d3a 100644 --- a/.gitignore +++ b/.gitignore @@ -173,3 +173,4 @@ $RECYCLE.BIN/ .venv *.csv +*.ipynb_checkpoints diff --git a/README.md b/README.md index 88777db..6fa56a0 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,41 @@ -# TEMPLATE-base-repo +# WGBH Impact and Equity: Boston Bus Equity (Fall 2024 Project) -Create a new branch from dev, add changes on the new branch you just created. +## Overview +This repository contains all code, data, and documentation for the **Boston Bus Equity** project, conducted in partnership with **Paul Singer**, Senior Editor, Equity & Justice at **GBH News**. This project, part of BU’s DS701 practicum course, aims to explore the impact of MBTA bus performance on Boston residents, examining service disparities and trends across different neighborhoods. The analysis will help inform news stories on the accessibility and equity of Boston’s public transportation system. -Open a Pull Request to dev. Add your PM and TPM as reviewers. +## Project Summary +This project aims to deliver a detailed analysis of the Massachusetts Bay Transportation Authority (MBTA) bus performance in 2019 and 2022. The main focus is to compare the ridership and reliability data for each bus route between the two years. This study will identify any areas where bus service could be improved to better meet community needs. By analyzing the performance for each route, this report can inform data-driven decision-making for policymakers and MBTA to enhance overall transit system performance to better serve Boston residents and communities. -At the end of the semester during project wrap up open a final Pull Request to main from dev branch. +## Key Questions +- What is the ridership per bus route? +- What are the end-to-end travel times for each bus route in the city? +- On average, how long does an individual have to wait for a bus (on time vs. delayed)? +- What is the average delay time of all routes across the entire city? +- What is the average delay time of the target bus routes (22, 29, 15, 45, 28, 44, 42, 17, 23, 31, 26, 111, 24, 33, 14 - from Livable Streets report)? +- Are there disparities in the service levels of different routes (which lines are late more often than others)? + +## Repository Structure +- **dataset-documentation/**: Documentation and preliminary data cleaning notes. +- **notebooks/**: Jupyter notebooks for data analysis and visualization, including the latest work on answering base project questions. +- **utils/data_cleaning/**: Python scripts for data cleaning and pre-processing. +- **.github/workflows/**: GitHub Actions for CI/CD and code quality checks. +- **requirements.txt**: Python dependencies for project setup. +- **README.md**: Project overview and repository navigation guide (this file). + +## Datasets + +- The datasets for this project are listed below. Please followed the utils/data_cleaning for more about how datasets are cleaned for this project. + +### Ridership +- Fall 2019 https://mbta-massdot.opendata.arcgis.com/datasets/47bbf5047f0646fbae11ef3ed8ccea47_0/explore?filters=eyJzZWFzb24iOlsiRmFsbCAyMDE5Il19 +- Fall 2022 https://mbta-massdot.opendata.arcgis.com/datasets/47bbf5047f0646fbae11ef3ed8ccea47_0/explore?filters=eyJzZWFzb24iOlsiRmFsbCAyMDIyIl19 + +### Reliability +- 2019 Bus Departure/Arrival Times https://mbta-massdot.opendata.arcgis.com/datasets/1bd340b39942438685d8dcdfe3f26d1a/about +- 2022 Bus Departure/Arrival Times https://mbta-massdot.opendata.arcgis.com/datasets/ef464a75666349f481353f16514c06d0/about + + +## Getting Started +### Prerequisites +- Python 3.8 or above +- Required libraries are listed in `requirements.txt`. diff --git a/notebooks/Headway.ipynb b/notebooks/Headway.ipynb new file mode 100644 index 0000000..85ee4c0 --- /dev/null +++ b/notebooks/Headway.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "210383e7-6ed5-421d-9bb4-dc929c4a1944", + "metadata": {}, + "source": [ + "# Preliminary Analysis on Base Questions\n", + "\n", + "We used a updated clean dataset labeled 'arrdep_18augsep_simplecleaned.csv'." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "37f91dd2-c11b-4277-abaa-5274803af100", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "e5a04032-4da5-45be-ac57-0bebdbf3c074", + "metadata": {}, + "source": [ + "## Question 2.1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "934fda9d-2c85-4beb-8c27-52ab59daf282", + "metadata": {}, + "outputs": [], + "source": [ + "def wait_time(filename):\n", + "\n", + " # Loads the file\n", + " file = pd.read_csv(filename)\n", + " \n", + " # Drops all NaN values in headway/scheduled_headways columns.\n", + " no_nan_df = file.dropna(subset=['headway', 'scheduled_headway'])\n", + " # Calculates the average headway/scheduled_headway per bus route.\n", + " bus_time_wait = no_nan_df.groupby('route_id')[['headway', 'scheduled_headway']].mean()\n", + "\n", + " # Sorts the wait time in descending order\n", + " sorted_df = bus_time_wait.sort_values(by=['scheduled_headway'], ascending = False)\n", + " sorted_df[\"difference\"] = sorted_df[\"headway\"] - sorted_df[\"scheduled_headway\"]\n", + "\n", + " # Plots the comparison between actual and scheduled headway per route\n", + " plt.figure(figsize=(14, 6)) # Adjust figure size\n", + " plt.scatter(sorted_df.index.astype(str), sorted_df[\"scheduled_headway\"], color = \"red\", label = \"Scheduled Headway\") # Plots the scatterplots of route_id vs sceduled headway as \"red\"\n", + " plt.scatter(sorted_df.index.astype(str), sorted_df[\"headway\"], color = \"blue\", label='Actual Headway') # Plots the scatterplots of route_id vs actual headway as \"blue\"\n", + " plt.xticks(rotation=270) \n", + " plt.xlabel('Route ID') \n", + " plt.ylabel('Time (in seconds)') \n", + " plt.title('Comparison of Actual and Scheduled Headway for Each Route')\n", + " plt.legend()\n", + " plt.tight_layout()\n", + " plt.grid(True) # Adds grid lines for easier comparison\n", + " plt.show()\n", + "\n", + " # Returns dataframe of average headway/scheduled headway per bus route in descending order.\n", + " return sorted_df" + ] + }, + { + "cell_type": "markdown", + "id": "ed7a5226-3e8c-4bf7-94b0-4ac0e41055bd", + "metadata": {}, + "source": [ + "#### Output\n", + "\n", + "Scheduled wait time vs actual wait time per bus route" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c5a0beef-1d82-4d19-82bd-f72577cc1d7c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " headway scheduled_headway difference\n", + "route_id \n", + "117 1484.452265 1485.686800 -1.234535\n", + "116 1406.329471 1399.465649 6.863822\n", + "746_ 1160.808720 1148.045007 12.763713\n", + "15 989.232289 984.415452 4.816837\n", + "CT2 918.062914 900.000000 18.062914\n", + "... ... ... ...\n", + "501 534.247285 511.077694 23.169591\n", + "31 504.170023 504.890679 -0.720656\n", + "96 932.418605 498.837209 433.581395\n", + "7 418.370772 419.468258 -1.097486\n", + "441 589.833333 320.000000 269.833333\n", + "\n", + "[92 rows x 3 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wait_time(\"arrdep_18augsep_simplecleaned.csv\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Preliminary Analysis on Base Questions 1.1.ipynb b/notebooks/Preliminary Analysis on Base Questions 1.1.ipynb new file mode 100644 index 0000000..51ee086 --- /dev/null +++ b/notebooks/Preliminary Analysis on Base Questions 1.1.ipynb @@ -0,0 +1,658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "feabca32", + "metadata": {}, + "source": [ + "# Preliminary Analysis on Base Questions 1.1\n", + "\n", + "This 1.1 Version of analysis is based on updated cleaned ridership datasets, 'cleaned_2019-01-03_data' and 'cleaned_2022-01_data'." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "14bc321c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "id": "1e634a2f", + "metadata": {}, + "source": [ + "## File loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0b4a4bf2", + "metadata": {}, + "outputs": [], + "source": [ + "path_1 = 'C:\\\\Users\\\\Huihao Xing\\\\Documents\\\\临时文件\\\\G1\\\\DS 701\\\\Project\\\\Dataset\\\\cleaned_2019-01-03_data.csv'\n", + "path_2 = 'C:\\\\Users\\\\Huihao Xing\\\\Documents\\\\临时文件\\\\G1\\\\DS 701\\\\Project\\\\Dataset\\\\cleaned_2022-01_data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0ae5bbe6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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41/1/2019111Inbound419279835605belsq5MidpointHeadway06:33:0006:28:56360.0113.01/1/2019 06:331/1/2019 06:281/1/2019 06:43
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10485703/16/201924Outbound42792244574rvcen4MidpointSchedule01:08:0001:56:11NaNNaN3/17/2019 01:083/17/2019 01:563/17/2019 01:56
10485713/16/201924Outbound427922036368rivcm2MidpointSchedule05:51:0005:50:26NaNNaN3/16/2019 05:513/16/2019 05:503/16/2019 06:20
10485723/16/201924Outbound427927586368rivcm6MidpointSchedule06:15:0006:20:45NaNNaN3/16/2019 06:153/16/2019 06:203/16/2019 06:20
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1048575 rows × 16 columns

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" + ], + "text/plain": [ + " service_date route_id direction half_trip_id stop_id time_point_id \\\n", + "0 1/1/2019 111 Inbound 41928237 5605 belsq \n", + "1 1/1/2019 111 Inbound 41928270 5605 belsq \n", + "2 1/1/2019 111 Inbound 41928283 5605 belsq \n", + "3 1/1/2019 111 Inbound 41928251 5605 belsq \n", + "4 1/1/2019 111 Inbound 41927983 5605 belsq \n", + "... ... ... ... ... ... ... \n", + "1048570 3/16/2019 24 Outbound 42792244 574 rvcen \n", + "1048571 3/16/2019 24 Outbound 42792203 6368 rivcm \n", + "1048572 3/16/2019 24 Outbound 42792758 6368 rivcm \n", + "1048573 3/16/2019 24 Outbound 42792205 6368 rivcm \n", + "1048574 3/16/2019 24 Outbound 42792760 6368 rivcm \n", + "\n", + " time_point_order point_type standard_type scheduled actual \\\n", + "0 5 Midpoint Schedule 05:48:00 05:59:37 \n", + "1 5 Midpoint Headway 06:03:00 06:08:16 \n", + "2 5 Midpoint Headway 06:18:00 06:29:21 \n", + "3 2 Midpoint Headway 06:27:00 06:27:03 \n", + "4 5 Midpoint Headway 06:33:00 06:28:56 \n", + "... ... ... ... ... ... \n", + "1048570 4 Midpoint Schedule 01:08:00 01:56:11 \n", + "1048571 2 Midpoint Schedule 05:51:00 05:50:26 \n", + "1048572 6 Midpoint Schedule 06:15:00 06:20:45 \n", + "1048573 6 Midpoint Schedule 06:55:00 06:54:29 \n", + "1048574 6 Midpoint Schedule 07:35:00 07:40:57 \n", + "\n", + " scheduled_headway headway scheduled_datetime actual_datetime \\\n", + "0 NaN NaN 1/1/2019 05:48 1/1/2019 05:59 \n", + "1 900.0 519.0 1/1/2019 06:03 1/1/2019 06:08 \n", + "2 900.0 25.0 1/1/2019 06:18 1/1/2019 06:29 \n", + "3 540.0 1127.0 1/1/2019 06:27 1/1/2019 06:27 \n", + "4 360.0 113.0 1/1/2019 06:33 1/1/2019 06:28 \n", + "... ... ... ... ... \n", + "1048570 NaN NaN 3/17/2019 01:08 3/17/2019 01:56 \n", + "1048571 NaN NaN 3/16/2019 05:51 3/16/2019 05:50 \n", + "1048572 NaN NaN 3/16/2019 06:15 3/16/2019 06:20 \n", + "1048573 NaN NaN 3/16/2019 06:55 3/16/2019 06:54 \n", + "1048574 NaN NaN 3/16/2019 07:35 3/16/2019 07:40 \n", + "\n", + " available_bus_depart_time \n", + "0 1/1/2019 05:59 \n", + "1 1/1/2019 06:08 \n", + "2 1/1/2019 06:27 \n", + "3 1/1/2019 06:27 \n", + "4 1/1/2019 06:43 \n", + "... ... \n", + "1048570 3/17/2019 01:56 \n", + "1048571 3/16/2019 06:20 \n", + "1048572 3/16/2019 06:20 \n", + "1048573 3/16/2019 07:40 \n", + "1048574 3/16/2019 07:40 \n", + "\n", + "[1048575 rows x 16 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(path_1)\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "1c412f6f", + "metadata": {}, + "source": [ + "## Question 3" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "113996e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average waiting time for on-time buses: 643.8242727552334 seconds\n", + "Average waiting time for delayed buses: 335.79826366690526 seconds\n" + ] + } + ], + "source": [ + "# mean waiting time\n", + "df['scheduled'] = pd.to_datetime(df['scheduled'], format='%H:%M:%S', errors='coerce')\n", + "df['actual'] = pd.to_datetime(df['actual'], format='%H:%M:%S', errors='coerce')\n", + "\n", + "df['wait_time'] = (df['actual'] - df['scheduled']).dt.total_seconds()\n", + "\n", + "df_filtered = df.dropna(subset=['wait_time'])\n", + "\n", + "on_time = df_filtered[df_filtered['wait_time'] <= 0]['wait_time'].mean() \n", + "delayed = df_filtered[df_filtered['wait_time'] > 0]['wait_time'].mean() \n", + "\n", + "print(f\"Average waiting time for on-time buses: {abs(on_time)} seconds\")\n", + "print(f\"Average waiting time for delayed buses: {delayed} seconds\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "291a634c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6.005597276646684, 392.9192985161267)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['scheduled'] = pd.to_datetime(df['scheduled'], format='%H:%M:%S', errors='coerce')\n", + "df['actual'] = pd.to_datetime(df['actual'], format='%H:%M:%S', errors='coerce')\n", + "\n", + "# Calculating wait_time as the difference in seconds\n", + "df['wait_time'] = (df['actual'] - df['scheduled']).dt.total_seconds()\n", + "\n", + "# Filtering to remove any NaN wait_time values\n", + "df_filtered = df.dropna(subset=['wait_time'])\n", + "\n", + "# Redefining \"on-time\" as buses arriving within a 1-minute (60 seconds) window around scheduled time\n", + "on_time_refined = df_filtered[(df_filtered['wait_time'] >= -60) & (df_filtered['wait_time'] <= 60)]\n", + "delayed = df_filtered[df_filtered['wait_time'] > 60] # Delayed as strictly over 1 minute\n", + "\n", + "# Calculating mean waiting times\n", + "on_time_mean_wait = on_time_refined['wait_time'].mean()\n", + "delayed_mean_wait = delayed['wait_time'].mean()\n", + "\n", + "on_time_mean_wait, delayed_mean_wait" + ] + }, + { + "cell_type": "markdown", + "id": "220e0830", + "metadata": {}, + "source": [ + "## Question 4" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "478410a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "335.79826366690526" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mean delay - all routes\n", + "\n", + "average_delay_all_routes = df_filtered[df_filtered['wait_time'] > 0]['wait_time'].mean()\n", + "\n", + "average_delay_all_routes" + ] + }, + { + "cell_type": "markdown", + "id": "4b6d7daa", + "metadata": {}, + "source": [ + "## Question 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "12b5c311", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "335.79826366690526" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mean delay - taget routes\n", + "target_routes = ['22', '29', '15', '45', '28', '44', '42', '17', '23', '31', '26', '111', '24', '33', '14']\n", + "\n", + "df_filtered['route_id'] = df_filtered['route_id'].astype(str)\n", + "\n", + "target_route_data = df_filtered[df_filtered['route_id'].isin(target_routes)] \n", + "\n", + "average_delay_target_routes = target_route_data[target_route_data['wait_time'] > 0]['wait_time'].mean()\n", + "\n", + "average_delay_target_routes" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e23633cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111 244777\n", + "23 181247\n", + "28 178188\n", + "22 129631\n", + "31 102932\n", + "15 96619\n", + "44 66701\n", + "45 61908\n", + "29 42501\n", + "42 34475\n", + "17 33014\n", + "26 27291\n", + "24 26893\n", + "14 25490\n", + "33 15166\n", + "Name: route_id, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Detecting Reason\n", + "\n", + "df['route_id'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "f18a4b41", + "metadata": {}, + "source": [ + "routes 19 is missing from datasets" + ] + }, + { + "cell_type": "markdown", + "id": "2f2804f7", + "metadata": {}, + "source": [ + "## Question 6" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c5a613dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " route_id total_buses delayed_buses delay_percentage\n", + "6 26 27291 24331 89.153934\n", + "4 23 181247 158634 87.523656\n", + "10 33 15166 13227 87.214823\n", + "8 29 42501 37034 87.136773\n", + "5 24 26893 22913 85.200610\n", + "9 31 102932 86579 84.112812\n", + "3 22 129631 108264 83.517060\n", + "12 44 66701 55287 82.887813\n", + "13 45 61908 48032 77.586095\n", + "2 17 33014 25503 77.249046\n", + "1 15 96619 73922 76.508761\n", + "11 42 34475 26041 75.535896\n", + "7 28 178188 124740 70.004714\n", + "0 14 25490 16302 63.954492\n", + "14 111 244777 130824 53.446198\n", + "\n", + "\n", + " route_id total_buses delayed_buses delay_percentage\n", + "6 26 27291 24331 89.153934\n", + "4 23 181247 158634 87.523656\n", + "10 33 15166 13227 87.214823\n", + "8 29 42501 37034 87.136773\n", + "5 24 26893 22913 85.200610\n", + "9 31 102932 86579 84.112812\n", + "3 22 129631 108264 83.517060\n", + "12 44 66701 55287 82.887813\n", + "13 45 61908 48032 77.586095\n", + "2 17 33014 25503 77.249046\n", + "1 15 96619 73922 76.508761\n", + "11 42 34475 26041 75.535896\n", + "7 28 178188 124740 70.004714\n", + "0 14 25490 16302 63.954492\n", + "14 111 244777 130824 53.446198\n" + ] + } + ], + "source": [ + "# disparity in service level\n", + "route_delays = df_filtered.groupby('route_id').agg(\n", + " total_buses=('wait_time', 'count'), # Total number of buses per route\n", + " delayed_buses=('wait_time', lambda x: (x > 0).sum()) # Number of delayed buses per route\n", + ").reset_index()\n", + "\n", + "route_delays['delay_percentage'] = (route_delays['delayed_buses'] / route_delays['total_buses']) * 100\n", + "\n", + "route_delays_sorted = route_delays.sort_values(by='delay_percentage', ascending=False)\n", + "\n", + "print(route_delays_sorted.head(15)) \n", + "print('\\n')\n", + "print(route_delays_sorted.tail(15)) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Preliminary Analysis on Base Questions 1.2.ipynb b/notebooks/Preliminary Analysis on Base Questions 1.2.ipynb new file mode 100644 index 0000000..55116d7 --- /dev/null +++ b/notebooks/Preliminary Analysis on Base Questions 1.2.ipynb @@ -0,0 +1,950 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "14756042", + "metadata": {}, + "source": [ + "# Preliminary Analysis on Base Questions 1.2\n", + "\n", + "This 1.2 Version of preliminary analysis is based on updated cleaned ridership datasets, 'cleaned_2019-01-03_data' and 'cleaned_2022-01_data' with focus on Januray data of 2019 and 2022. \n", + "\n", + "This version improved upon some existing issues and concerns from first version.\n", + "\n", + " 1. Solving issues about the definition of 'delayed' by adding a buffer time of one minute.\n", + " \n", + " 2. Adding anlysis for early arrivals.\n", + " \n", + " 3. Strightforward charts are available for key Stats!\n", + " \n", + "Please note that the missing of certain routes(which causes the Q4 and Q5 to have identical chart) has been noticed. We will discussed this issue with the team member who is cleaning this datasets.\n", + "\n", + "More of the clients' concern will be addressed in next version." + ] + }, + { + "cell_type": "markdown", + "id": "44887d22", + "metadata": {}, + "source": [ + "# Package Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e03941a9", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "0ca71275", + "metadata": {}, + "source": [ + "# File Loading\n", + "\n", + "Make sure to replace this path with you local path where is stores 'cleaned_2019-01-03_data' and 'cleaned_2022-01_data'." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e0f7a4f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Load datasets\n", + "df_2019 = pd.read_csv('C:\\\\Users\\\\Huihao Xing\\\\Documents\\\\临时文件\\\\G1\\\\DS 701\\\\Project\\\\Dataset\\\\cleaned_2019-01-03_data.csv', \n", + " parse_dates = ['service_date'])\n", + "df_2022 = pd.read_csv('C:\\\\Users\\\\Huihao Xing\\\\Documents\\\\临时文件\\\\G1\\\\DS 701\\\\Project\\\\Dataset\\\\cleaned_2022-01_data.csv',\n", + " parse_dates = ['service_date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f2822a7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "service_date datetime64[ns]\n", + "route_id int64\n", + "direction object\n", + "half_trip_id int64\n", + "stop_id int64\n", + "time_point_id object\n", + "time_point_order int64\n", + "point_type object\n", + "standard_type object\n", + "scheduled object\n", + "actual object\n", + "scheduled_headway float64\n", + "headway float64\n", + "scheduled_datetime object\n", + "actual_datetime object\n", + "available_bus_depart_time object\n", + "dtype: object\n" + ] + } + ], + "source": [ + "print(df_2019.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "63ef8309", + "metadata": {}, + "outputs": [], + "source": [ + "# Add a column indicating the data source year for differentiation\n", + "df_2019['year'] = 2019\n", + "df_2022['year'] = 2022\n", + "\n", + "# Combine datasets\n", + "df = pd.concat([df_2019, df_2022], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f79d97be", + "metadata": {}, + "outputs": [], + "source": [ + "# Set a new col for month\n", + "df['month'] = df['service_date'].dt.month\n", + "\n", + "# Set month = January\n", + "df = df[df['month'] == 1]\n", + "\n", + "# Set year = X\n", + "# df = df[df['year'] == X]\n", + "\n", + "df['scheduled'] = pd.to_datetime(df['scheduled'], format='%H:%M:%S', errors='coerce')\n", + "df['actual'] = pd.to_datetime(df['actual'], format='%H:%M:%S', errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fe779e60", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate delay in seconds and on-time status with a 1-minute buffer\n", + "df['delay_seconds'] = (df['actual'] - df['scheduled']).dt.total_seconds()\n", + "df['on_time'] = df['delay_seconds'].abs() <= 60" + ] + }, + { + "cell_type": "markdown", + "id": "f394ced7", + "metadata": {}, + "source": [ + "# Question 3\n", + "\n", + "On average, how long does an individual have to wait for a bus (on time vs. delayed)? \n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5487587f", + "metadata": {}, + "outputs": [], + "source": [ + "# Group by route, year, month, and on-time status, then calculate the average wait time\n", + "monthly_route_waiting = df.groupby(['route_id', 'year', 'month', 'on_time']).agg(\n", + " avg_wait_time=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Print the results\n", + "# print(\"Monthly average wait times by route and on-time status (on time vs. delayed):\")\n", + "# print(monthly_route_waiting)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e641e229", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Group by year, route, and on-time status, then calculate average wait time\n", + "avg_wait_time = df.groupby(['year', 'route_id', 'on_time']).agg(\n", + " avg_wait_time=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Plot average wait time for 2019\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2019 = avg_wait_time[avg_wait_time['year'] == 2019]\n", + "for i, route in enumerate(subset_2019['route_id'].unique()):\n", + " route_data = subset_2019[subset_2019['route_id'] == route]\n", + " plt.bar([f\"{route} - {status}\" for status in ['On Time', 'Delayed']], \n", + " route_data['avg_wait_time'], width=0.4, alpha=0.7, label=f'Route {route}')\n", + "plt.xlabel('Route and Status')\n", + "plt.ylabel('Average Wait Time (seconds)')\n", + "plt.title('Average Wait Time in January 2019 (On-Time vs Delayed) by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1.05), ncol=2)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot average wait time for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2022 = avg_wait_time[avg_wait_time['year'] == 2022]\n", + "for i, route in enumerate(subset_2022['route_id'].unique()):\n", + " route_data = subset_2022[subset_2022['route_id'] == route]\n", + " plt.bar([f\"{route} - {status}\" for status in ['On Time', 'Delayed']], \n", + " route_data['avg_wait_time'], width=0.4, alpha=0.7, label=f'Route {route}')\n", + "plt.xlabel('Route and Status')\n", + "plt.ylabel('Average Wait Time (seconds)')\n", + "plt.title('Average Wait Time in January 2022 (On-Time vs Delayed) by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1.05), ncol=2)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "da147fce", + "metadata": {}, + "source": [ + "# Extra Question from Client \n", + "\n", + "Having a bus arrive early is “more harmful” than a bus that arrives a bit late. Because, generally speaking, a person will be more likely to miss the bus completely if it arrives early compared to waiting 2 extra minutes for a late bus but getting onto the same route.\n", + "\n", + "Following cells aim to detect those early arrivals." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7077998a", + "metadata": {}, + "outputs": [], + "source": [ + "# Identify early arrivals (more than 1 minute early)\n", + "df['early_arrival'] = df['delay_seconds'] < -60\n", + "\n", + "# Count early arrivals by route, year, and month\n", + "monthly_early_arrivals = df[df['early_arrival']].groupby(['route_id', 'year', 'month']).size().reset_index(name='early_arrival_count')\n", + "\n", + "# Count early arrivals by year and route\n", + "early_arrival_count = df[df['early_arrival']].groupby(['year', 'route_id']).size().reset_index(name='early_arrival_count')\n", + "\n", + "# Print the results\n", + "# print(\"Monthly count of early arrivals by route:\")\n", + "# print(monthly_early_arrivals)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "23700d57", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a color palette for the routes\n", + "colors = [\n", + " '#FF5733', '#33FF57', '#3357FF', '#F1C40F', '#9B59B6',\n", + " '#1ABC9C', '#E67E22', '#34495E', '#7F8C8D', '#FF1493',\n", + " '#FF6F61', '#6C5B7F', '#F67280', '#C06C84', '#6A0572',\n", + " '#AB83B4', '#F6B93B', '#4ECDC4', '#F45D01', '#D94F3A',\n", + " '#C9E4CA', '#4B86B4', '#6F8FAF', '#D9BF77', '#FFB6B9'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2e8d90f6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find the maximum count of early arrivals across both years\n", + "max_early_arrival_count = early_arrival_count['early_arrival_count'].max()\n", + "\n", + "# Plot early arrivals for 2019\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2019 = early_arrival_count[early_arrival_count['year'] == 2019]\n", + "plt.bar(subset_2019['route_id'].astype(str), \n", + " subset_2019['early_arrival_count'], \n", + " color=colors[:len(subset_2019)])\n", + "plt.ylim(0, 30000) \n", + "plt.xlabel('Route')\n", + "plt.ylabel('Count of Early Arrivals')\n", + "plt.title('Count of Early Arrivals in January 2019 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Repeat for 2022 using the same Y-axis limit\n", + "\n", + "\n", + "# Plot early arrivals categorized by routes for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2022 = early_arrival_count[early_arrival_count['year'] == 2022]\n", + "plt.bar(subset_2022['route_id'].astype(str), \n", + " subset_2022['early_arrival_count'], \n", + " color=colors[:len(subset_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Count of Early Arrivals')\n", + "plt.title('Count of Early Arrivals in January 2022 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0, 30000) \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9599b9c5", + "metadata": {}, + "source": [ + "# Extending - Percentage" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f2ecbccd", + "metadata": {}, + "outputs": [], + "source": [ + "# Count total arrivals by year and route\n", + "total_arrival_count = df.groupby(['year', 'route_id']).size().reset_index(name='total_arrival_count')\n", + "\n", + "# Merge early arrival counts with total arrivals for 2019\n", + "early_arrival_percentage_2019 = early_arrival_count[early_arrival_count['year'] == 2019].merge(total_arrival_count[total_arrival_count['year'] == 2019], on='route_id')\n", + "early_arrival_percentage_2019['early_arrival_percentage'] = (early_arrival_percentage_2019['early_arrival_count'] / early_arrival_percentage_2019['total_arrival_count']) * 100\n", + "\n", + "# Merge early arrival counts with total arrivals for 2022\n", + "early_arrival_percentage_2022 = early_arrival_count[early_arrival_count['year'] == 2022].merge(total_arrival_count[total_arrival_count['year'] == 2022], on='route_id')\n", + "early_arrival_percentage_2022['early_arrival_percentage'] = (early_arrival_percentage_2022['early_arrival_count'] / early_arrival_percentage_2022['total_arrival_count']) * 100\n", + "\n", + "# Print early arrival percentages for 2019\n", + "# print(\"Early Arrival Percentages for 2019:\")\n", + "# print(early_arrival_percentage_2019[['route_id', 'early_arrival_count', 'total_arrival_count', 'early_arrival_percentage']])\n", + "\n", + "# Print early arrival percentages for 2022\n", + "# print(\"\\nEarly Arrival Percentages for 2022:\")\n", + "# print(early_arrival_percentage_2022[['route_id', 'early_arrival_count', 'total_arrival_count', 'early_arrival_percentage']])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9517a2cd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find the maximum percentage of early arrivals across both years\n", + "max_early_arrival_percentage = max(early_arrival_percentage_2019['early_arrival_percentage'].max(), \n", + " early_arrival_percentage_2022['early_arrival_percentage'].max())\n", + "\n", + "# Plot early arrival percentages for 2019\n", + "plt.figure(figsize=(14, 7))\n", + "plt.bar(early_arrival_percentage_2019['route_id'].astype(str), \n", + " early_arrival_percentage_2019['early_arrival_percentage'], \n", + " color=colors[:len(early_arrival_percentage_2019)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Percentage of Early Arrivals (%)')\n", + "plt.title('Percentage of Early Arrivals in January 2019 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0, 100) \n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot early arrival percentages for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "plt.bar(early_arrival_percentage_2022['route_id'].astype(str), \n", + " early_arrival_percentage_2022['early_arrival_percentage'], \n", + " color=colors[:len(early_arrival_percentage_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Percentage of Early Arrivals (%)')\n", + "plt.title('Percentage of Early Arrivals in January 2022 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0, 100) \n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "a59eba6e", + "metadata": {}, + "source": [ + "# Extending\n", + "\n", + "What is the average time for those early arrival?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5d931525", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the average early arrival time by route, year, and month\n", + "monthly_avg_early_arrival = df[df['early_arrival']].groupby(['route_id', 'year', 'month']).agg(\n", + " avg_early_arrival_time=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Convert early arrival times from seconds to minutes for readability\n", + "monthly_avg_early_arrival['avg_early_arrival_time_minutes'] = monthly_avg_early_arrival['avg_early_arrival_time'] / 60\n", + "\n", + "\n", + "# Calculate average early arrival time by year and route\n", + "avg_early_arrival_time = df[df['early_arrival']].groupby(['year', 'route_id']).agg(\n", + " avg_early_arrival_time=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Convert early arrival time from seconds to minutes for readability\n", + "avg_early_arrival_time['avg_early_arrival_time_minutes'] = avg_early_arrival_time['avg_early_arrival_time'] / 60\n", + "\n", + "\n", + "# Print the results\n", + "# print(\"Monthly average early arrival time (in minutes) by route:\")\n", + "# print(monthly_avg_early_arrival)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1d77b393", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find the maximum average early arrival time across both years\n", + "max_avg_early_arrival_time = max(avg_early_arrival_time['avg_early_arrival_time_minutes'].max(), \n", + " avg_early_arrival_time['avg_early_arrival_time_minutes'].max())\n", + "\n", + "# Plot average early arrival time for 2019\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2019 = avg_early_arrival_time[avg_early_arrival_time['year'] == 2019]\n", + "plt.bar(subset_2019['route_id'].astype(str), \n", + " subset_2019['avg_early_arrival_time_minutes'], \n", + " color=colors[:len(subset_2019)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Average Early Arrival Time (minutes)')\n", + "plt.title('Average Early Arrival Time in January 2019 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(-80, 0) # Set y-axis limit\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot average early arrival time for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2022 = avg_early_arrival_time[avg_early_arrival_time['year'] == 2022]\n", + "plt.bar(subset_2022['route_id'].astype(str), \n", + " subset_2022['avg_early_arrival_time_minutes'], \n", + " color=colors[:len(subset_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Average Early Arrival Time (minutes)')\n", + "plt.title('Average Early Arrival Time in January 2022 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(-80,0) # Set y-axis limit\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "73f9f743", + "metadata": {}, + "source": [ + "# Question 4\n", + "\n", + "What is the average delay time of all routes across the entire city?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "82ab5082", + "metadata": {}, + "outputs": [], + "source": [ + "# Filter to delayed entries and calculate the average delay per month\n", + "monthly_avg_delay_citywide = df[~df['on_time']].groupby(['year', 'month']).agg(\n", + " avg_citywide_delay=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Convert delay time from seconds to minutes\n", + "monthly_avg_delay_citywide['avg_citywide_delay_minutes'] = monthly_avg_delay_citywide['avg_citywide_delay'] / 60\n", + "\n", + "\n", + "# Filter to delayed entries and calculate average delay by year and route\n", + "citywide_avg_delay = df[~df['on_time']].groupby(['year', 'route_id']).agg(\n", + " avg_citywide_delay=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Convert delay time from seconds to minutes\n", + "citywide_avg_delay['avg_citywide_delay_minutes'] = citywide_avg_delay['avg_citywide_delay'] / 60\n", + "\n", + "# Print the results\n", + "# print(\"Monthly average delay across all routes (city-wide):\")\n", + "# print(monthly_avg_delay_citywide)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5f89bc06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 7))\n", + "subset_2019 = citywide_avg_delay[citywide_avg_delay['year'] == 2019]\n", + "plt.bar(subset_2019['route_id'].astype(str), \n", + " subset_2019['avg_citywide_delay_minutes'], \n", + " color=colors[:len(subset_2019)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Average Delay Time (minutes)')\n", + "plt.title('City-Wide Average Delay in January 2019 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(-4,6)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot city-wide average delay categorized by routes for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2022 = citywide_avg_delay[citywide_avg_delay['year'] == 2022]\n", + "plt.bar(subset_2022['route_id'].astype(str), \n", + " subset_2022['avg_citywide_delay_minutes'], \n", + " color=colors[:len(subset_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Average Delay Time (minutes)')\n", + "plt.title('City-Wide Average Delay in January 2022 by Route')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(-4,6)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aea2c0d8", + "metadata": {}, + "source": [ + "# Question 5\n", + "\n", + "What is the average delay time of the target bus routes (22, 29, 15, 45, 28, 44, 42, 17, 23, 31, 26, 111, 24, 33, 14 - from Livable Streets report)?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4fe359f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Define target routes\n", + "target_routes = [22, 29, 15, 45, 28, 44, 42, 17, 23, 31, 26, 111, 24, 33, 14]\n", + "\n", + "# Filter data for target routes and calculate average delay for delayed trips\n", + "monthly_target_route_delay = df[(df['route_id'].isin(target_routes)) & (~df['on_time'])].groupby(\n", + " ['route_id', 'year', 'month']\n", + ").agg(\n", + " avg_delay_target=('delay_seconds', 'mean')\n", + ").reset_index()\n", + "\n", + "# Convert delay time from seconds to minutes\n", + "monthly_target_route_delay['avg_delay_target_minutes'] = monthly_target_route_delay['avg_delay_target'] / 60\n", + "\n", + "# Print the results\n", + "# print(\"Monthly average delay for target routes:\")\n", + "# print(monthly_target_route_delay)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8e9c50c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot average delay for target routes in 2019\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2019 = monthly_target_route_delay[monthly_target_route_delay['year'] == 2019]\n", + "plt.bar(subset_2019['route_id'].astype(str), \n", + " subset_2019['avg_delay_target_minutes'], \n", + " color=colors[:len(subset_2019)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Average Delay Time (minutes)')\n", + "plt.title('Average Delay Time for Target Routes in January 2019')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(-4,6)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot average delay for target routes in 2022\n", + "plt.figure(figsize=(14, 7))\n", + "subset_2022 = monthly_target_route_delay[monthly_target_route_delay['year'] == 2022]\n", + "plt.bar(subset_2022['route_id'].astype(str), \n", + " subset_2022['avg_delay_target_minutes'], \n", + " color=colors[:len(subset_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Average Delay Time (minutes)')\n", + "plt.title('Average Delay Time for Target Routes in January 2022')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(-4,6)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "21da8ed0", + "metadata": {}, + "source": [ + "# Question 6 \n", + "\n", + "Are there disparities in the service levels of different routes (which lines are late more often than others)?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "08c5517a", + "metadata": {}, + "outputs": [], + "source": [ + "# Count the number of delayed buses for each route\n", + "delayed_buses_count = df[~df['on_time']].groupby(['route_id']).size().reset_index(name='delayed_count')\n", + "\n", + "# Sort the results by delayed count in descending order\n", + "delayed_buses_count = delayed_buses_count.sort_values(by='delayed_count', ascending=False)\n", + "\n", + "# Print the results\n", + "# print(\"Count of Delayed Buses by Route:\")\n", + "# print(delayed_buses_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1810fc88", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Question 6: Count of delayed buses by route, separated by year\n", + "\n", + "# Count of delayed buses by route for 2019\n", + "plt.figure(figsize=(14, 7))\n", + "delayed_buses_count_2019 = df[(df['year'] == 2019) & (~df['on_time'])].groupby(['route_id']).size().reset_index(name='delayed_count')\n", + "plt.bar(delayed_buses_count_2019['route_id'].astype(str), \n", + " delayed_buses_count_2019['delayed_count'], \n", + " color=colors[:len(delayed_buses_count_2019)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Count of Delayed Buses')\n", + "plt.title('Count of Delayed Buses by Route in January 2019')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0,70000)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Count of delayed buses by route for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "delayed_buses_count_2022 = df[(df['year'] == 2022) & (~df['on_time'])].groupby(['route_id']).size().reset_index(name='delayed_count')\n", + "plt.bar(delayed_buses_count_2022['route_id'].astype(str), \n", + " delayed_buses_count_2022['delayed_count'], \n", + " color=colors[:len(delayed_buses_count_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Count of Delayed Buses')\n", + "plt.title('Count of Delayed Buses by Route in January 2022')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0,70000)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7d68108c", + "metadata": {}, + "source": [ + "# Extend Question 6: Percentage" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d6225e33", + "metadata": {}, + "outputs": [], + "source": [ + "# Count delayed buses for 2019\n", + "delayed_buses_count_2019 = df[(df['year'] == 2019) & (~df['on_time'])].groupby(['route_id']).size().reset_index(name='delayed_count')\n", + "\n", + "# Count total buses for 2019\n", + "total_buses_count_2019 = df[df['year'] == 2019].groupby(['route_id']).size().reset_index(name='total_count')\n", + "\n", + "# Count delayed buses for 2022\n", + "delayed_buses_count_2022 = df[(df['year'] == 2022) & (~df['on_time'])].groupby(['route_id']).size().reset_index(name='delayed_count')\n", + "\n", + "# Count total buses for 2022\n", + "total_buses_count_2022 = df[df['year'] == 2022].groupby(['route_id']).size().reset_index(name='total_count')\n", + "\n", + "# Merge counts for 2019 and calculate delay percentage\n", + "merged_counts_2019 = delayed_buses_count_2019.merge(total_buses_count_2019, on='route_id')\n", + "merged_counts_2019['delay_percentage'] = (merged_counts_2019['delayed_count'] / merged_counts_2019['total_count']) * 100\n", + "\n", + "# Merge counts for 2022 and calculate delay percentage\n", + "merged_counts_2022 = delayed_buses_count_2022.merge(total_buses_count_2022, on='route_id')\n", + "merged_counts_2022['delay_percentage'] = (merged_counts_2022['delayed_count'] / merged_counts_2022['total_count']) * 100\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b64d94c8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot percentage of delayed buses for 2019\n", + "plt.figure(figsize=(14, 7))\n", + "plt.bar(merged_counts_2019['route_id'].astype(str), \n", + " merged_counts_2019['delay_percentage'], \n", + " color=colors[:len(merged_counts_2019)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Percentage of Delayed Buses (%)')\n", + "plt.title('Percentage of Delayed Buses by Route in January 2019')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0,100)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot percentage of delayed buses for 2022\n", + "plt.figure(figsize=(14, 7))\n", + "plt.bar(merged_counts_2022['route_id'].astype(str), \n", + " merged_counts_2022['delay_percentage'], \n", + " color=colors[:len(merged_counts_2022)]) # Adjust colors based on number of routes\n", + "plt.xlabel('Route')\n", + "plt.ylabel('Percentage of Delayed Buses (%)')\n", + "plt.title('Percentage of Delayed Buses by Route in January 2022')\n", + "plt.xticks(rotation=90)\n", + "plt.ylim(0,100)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e69e7854", + "metadata": {}, + "source": [ + "# What can be optimized?\n", + " 1. Use + - to calculate early and delayed, it could be better reflects the reality\n", + " 2. wait time is not really reflecting the reality, since no matter what buffer time it sets, some marginal cases\n", + " will be counted into calculation and generates misleading stats." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Notebook/Preliminary Analysis on Base Questions.ipynb b/notebooks/Preliminary Analysis on Base Questions.ipynb similarity index 100% rename from Notebook/Preliminary Analysis on Base Questions.ipynb rename to notebooks/Preliminary Analysis on Base Questions.ipynb diff --git a/notebooks/Ridership.ipynb b/notebooks/Ridership.ipynb new file mode 100644 index 0000000..f096f96 --- /dev/null +++ b/notebooks/Ridership.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "00936da9-a096-441f-b5fd-6379d739e63c", + "metadata": {}, + "source": [ + "# Preliminary Analysis on Base Questions" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "79c2decb", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "pd.set_option('display.width', 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "e4cda814-b505-456a-8cc7-cdd4b7a2ca4d", + "metadata": {}, + "source": [ + "## Cleaning datasets\n", + "\n", + "We used the MBTA bus ridership dataset, 'MBTA_Bus_Ridership_Fall_2019.csv' and 'MBTA_Bus_Ridership_Fall_2022.csv'.\n", + "\n", + "This code will transform the ridership data by multiplying the average \"alightings/boarding\" with \"sample_size\" and return total \"alightings/boarding\". " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c97593f9-7fb1-4350-86b0-de983f037f4f", + "metadata": {}, + "outputs": [], + "source": [ + "def clean_data(filename):\n", + " \n", + " # Loads file\n", + " file = pd.read_csv(filename, low_memory=False)\n", + "\n", + " # Calculates total boarding and alightings for each stop\n", + " file['total_boardings'] = file['boardings'] * file['sample_size']\n", + " file['total_alightings'] = file['alightings'] * file['sample_size']\n", + "\n", + " return file.drop(['boardings', 'alightings', 'sample_size'], axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "20451e07-818e-48f9-8a88-a0d9ced57235", + "metadata": {}, + "source": [ + "## Question 1" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "31a648c4-12ed-44c9-97fb-1a3e0e990157", + "metadata": {}, + "outputs": [], + "source": [ + "def ridership(filename):\n", + " # This function will calculuate ridership per busline based on file\n", + "\n", + " # Loads file\n", + " file = clean_data(filename)\n", + "\n", + " # Calculates total ridership of bus routes\n", + " ridership = file['total_boardings'].sum()\n", + " # Calculates boardings and alightings by bus routes\n", + " ridership_by_stop = file.groupby('route_id')[['total_boardings', 'total_alightings']].sum()\n", + " # Calculates the differences of ridership per bus routes\n", + " ridership_by_stop[\"difference\"] = ridership_by_stop['total_boardings'] - ridership_by_stop['total_alightings']\n", + " \n", + " # Create a copy of the original dataframe with the specific columns\n", + " copy_df = file[['route_id', 'trip_start_time', 'stop_name', 'total_boardings', 'total_alightings']].copy()\n", + " # Gets the index of the peak boarding and alightings using 'idxmax()'\n", + " peak_ridership_idx = copy_df.groupby('route_id')[['total_boardings', 'total_alightings']].idxmax()\n", + " # Use the index from 'peak_ridership_idx' to get the rows with peak ridership\n", + " # Extract the rows corresponding to peak boardings\n", + " peak_boardings = copy_df.loc[peak_ridership_idx['total_boardings'], ['route_id', 'trip_start_time', 'stop_name', 'total_boardings']]\n", + " # Extract the rows corresponding to peak alightings\n", + " peak_alightings = copy_df.loc[peak_ridership_idx['total_alightings'], ['route_id', 'trip_start_time', 'stop_name', 'total_alightings']]\n", + " \n", + " # Plot total boarding and alightings by bus routes\n", + " plt.figure(figsize=(14, 6)) # Extends figure size\n", + " plt.scatter(ridership_by_stop.index.astype(str), (ridership_by_stop['total_boardings'] / 10000), color = 'blue', s = 10, alpha = 0.75, label='Boardings') # Assigns scatterplot of bus route vs boarding as \"blue\"\n", + " plt.scatter(ridership_by_stop.index.astype(str), (ridership_by_stop['total_alightings'] / 10000), color = 'red', s = 10, alpha = 0.5, label='Alightings') # Assigns scatterplot of bus route vs alightings as \"red\"\n", + " plt.title(\"Ridership by Bus Route\")\n", + " plt.xlabel(\"Bus Routes\")\n", + " plt.ylabel(\"Boarding and Alightings (in 10,000s)\")\n", + " plt.xticks(rotation=270, fontsize=6)\n", + " plt.tight_layout()\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.show()\n", + " \n", + "\n", + "\n", + " # Plot the difference between peak boarding and peak alightings.\n", + " plt.figure(figsize=(14, 6)) # Extends figure size\n", + " plt.scatter(ridership_by_stop.index.astype(str), ridership_by_stop['difference'] /1000, s = 10) # Plots scatterplot bus route vs difference between boarding and alightings\n", + " plt.title(\"Difference of Ridership by Bus Route\")\n", + " plt.xlabel(\"Bus Routes\")\n", + " plt.ylabel(\"Difference between Boarding and Alightings (in 1,000s)\")\n", + " plt.xticks(rotation=270, fontsize=6)\n", + " plt.tight_layout()\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + " print(f\"Total ridership is {ridership}. \\n\")\n", + " # Returns dataframe of ridership, peak boarding times, and peak alightings per bus route.\n", + " print(f\"This table shows the ridership by bus routes. \\n \\n {ridership_by_stop} \\n\")\n", + " print(f\"This table shows the peak boarding times per bus route. \\n \\n {peak_boardings} \\n\")\n", + " print(f\"This table shows the peak alightings times per bus route. \\n \\n {peak_alightings}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7bd4f81e-c8a4-4520-a7ae-8cac8b78a39c", + "metadata": {}, + "source": [ + "#### Ridership Information for 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "6c418820-e19b-4ad8-930a-15c2ddeaa613", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total ridership is 18764633.699969. \n", + "\n", + "This table shows the ridership by bus routes. \n", + " \n", + " total_boardings total_alightings difference\n", + "route_id \n", + "1 1070984.6 1071975.8 -991.2\n", + "10 210140.8 209644.5 496.3\n", + "100 11920.0 12015.6 -95.6\n", + "101 156308.9 166011.5 -9702.6\n", + "104 148331.6 149217.3 -885.7\n", + "... ... ... ...\n", + "94 18115.9 18145.6 -29.7\n", + "95 21452.3 22114.9 -662.6\n", + "96 27485.5 27529.4 -43.9\n", + "97 10136.0 10178.7 -42.7\n", + "99 12610.9 12587.3 23.6\n", + "\n", + "[165 rows x 3 columns] \n", + "\n", + "This table shows the peak boarding times per bus route. \n", + " \n", + " route_id trip_start_time stop_name total_boardings\n", + "1417 1 08:47:00 DUDLEY STATION 1392.3\n", + "16160 10 06:43:00 ANDREW STATION BUSWAY 1696.2\n", + "25505 100 20:07:00 WELLINGTON STATION BUSWAY 379.9\n", + "35285 101 14:25:00 WINTHROP ST @ BROOKS ST 2649.1\n", + "39568 104 16:11:00 SULLIVAN STATION BUSWAY 1740.0\n", + "... ... ... ... ...\n", + "842057 94 16:20:00 DAVIS BUSWAY 495.9\n", + "847955 95 20:12:00 SULLIVAN STATION BUSWAY 549.4\n", + "853645 96 21:25:00 DAVIS BUSWAY 545.3\n", + "858091 97 16:55:00 MALDEN CENTER EAST BUSWAY 2 139.8\n", + "860183 99 23:00:00 WELLINGTON STATION BUSWAY 552.0\n", + "\n", + "[165 rows x 4 columns] \n", + "\n", + "This table shows the peak alightings times per bus route. \n", + " \n", + " route_id trip_start_time stop_name total_alightings\n", + "8150 1 07:10:00 MASSACHUSETTS AVE @ HARRISON 1732.4\n", + "20414 10 06:25:00 WARREN ST @ TOWNSEND ST 1416.0\n", + "27269 100 21:52:00 WELLINGTON STATION BUSWAY 200.0\n", + "32693 101 07:00:00 SULLIVAN STATION BUSWAY 2531.5\n", + "43030 104 13:24:00 SULLIVAN STATION BUSWAY 2331.0\n", + "... ... ... ... ...\n", + "843298 94 07:15:00 COLLEGE AVE @ HIGHLAND AVE 263.9\n", + "848544 95 05:10:00 SULLIVAN STATION BUSWAY 1069.2\n", + "853356 96 21:25:00 BOSTON AVE @ WINTHROP ST 188.6\n", + "856602 97 07:25:00 MALDEN CENTER EAST BUSWAY 2 269.1\n", + "860833 99 06:22:00 WELLINGTON STATION BUSWAY 364.1\n", + "\n", + "[165 rows x 4 columns]\n" + ] + } + ], + "source": [ + "ridership('MBTA_Bus_Ridership_Fall_2019.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "03ba403a-d568-4737-a55c-8e828fb9491c", + "metadata": {}, + "source": [ + "#### Ridership Information for 2022" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "91251b7a-f3e4-473a-bcef-fd2be42dbe1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AAAAAAOBiiouLNWzYMJnNZoWEhFx4Z8NNSDJWrlxpXf/iiy8MSUZOTk6l/YYMGWIMHTr0vP2UlJQYZrPZesvOzjYkGfn5+UZZWVm1vxUVFRmrVq0yioqKLmm7I/pw9vbL7uOTT4yisWMt28eONco++cQjXycyuE8GT8hIBtv2efPNMuO66yzbr7uuyJg/38Neh59/NspGjzaKxoyxbB8zxigbPdrS7i4ZyXDFM/ywZp+xatUq44c1+5yS4cOJnxjvN7L8v/p+o7HGvyfZ//+qO7xOZCBDdcvgCRm9IYMtPyO94XWoLhnJ4Lg+Lvb7ycW2u8Pr4C3/Vu6Qobrd8vPzDUmG2Wy+aL3UbadKiIyMlCQdPny40ojbw4cPq02bNud9nL+/v/z9/au0+/r6ytfX1+E53dXFnq8tr8fl9uHs7ZfUx4ED0vLlOl5oOY24KK9M4cuXSy1bSjFV57rzhNeJDO6TwRMykuHC+4waJV1/vbRvn/TWW75q3drDXoe4OGnwYGnFCsv2igr5Dh5saXeXjGS44hmad4/Vjx9tV/PusU7JcMsrN2r3wBbKLMxQqwWPK6H3ud9vl/McLjcjGcjgzRk8IWN1zmDPz0hnZbBnuztk8ISMZLi8PlZPSFPp8hWq+UZ/7b7nRf00dLAGzk6qtI+tv7844nm46+tEhurLnufo0ouTXUjjxo0VGRmptWvXWttOnDihLVu2qEuXLi5MBo9WUKB935r1wZeWLwM++DJK+741SwVMdA7AwuMvepOUJE2ebLk/ebJlHXCypt2iKy0BAP9j08/InJzKS6Ca4qJ9gH1cWrg9efKkMjIylJGRIclyQbKMjAxlZWXJZDLp0Ucf1fPPP68PP/xQ27dv14gRIxQdHW2dBxew196jYcrYH6r65bmSpPrlucrYH6q9R8NcnAwAHCg6uvLSQ+3bmFNpCQBAtZSWpuNPTpckyzItzcWBAOfJ212ggDKzCkMsg6kKQ6IUUGZW3m4GUwHn4tLC7ddff622bduqbdu2kqRJkyapbdu2evrppyVJTzzxhMaNG6dRo0apY8eOOnnypD7++GMFBAS4MjY82E/lMVrplyy/AMtb3y/ARyv9kvVTedVpEgAArrN6Qpp2DLf8Ebtj+HStnsAfsQCAaujAAe2akqqN6yyjDzeuq9CuKamWKd6Aaig8IUwlfqEKPmEZTBV8IlclfqEKT2AwFXAuLi3c9uzZU4ZhVLktWrRIkmQymfTss8/q0KFDKikp0Zo1a9S8eXNXRoaHi42VMsKTNKe25TTiObUnKyM8SbGxLg4GALDiFDoAgLfYv7VAWdvNOlLDMvrwSI0oZW03a/9WRh+ieorvFSP/EcmS6ddylMlHASnJiu/FYCrgXNx2jlvAGRITpXHjpHx/y+nD+f7RGj/e0g4AcA/edAod00EAgHfLLg7TsYpQxdS0jD6MqZmrYxWhyi5m9CGqr4Gzk9RqqWUwVaulk3XzLK7JAJwPhVt4ndGjpcWLLfcXL7ZcRR6AA3BRDdjhQgXL6nQK3YWeJ9NBAAAi2sVoTViySkosf5qXlPhoTViyItox+hDVGxc2BWxD4RZeyeOvGg+4G3e5qAbFY49wsYJldTmF7kLPk+kgAACS5cy/jn9K0oIIy+jDBRGT1enPSZwRCACQROEWAHC53OWiGmlp0nRLkUzTuSKzu7K1YOnpp9Bd7Hl603QQ+A2+XAJwDqNHSy8ts4w6fGlZNGcEAgCsKNwCAC6LW1xU48ABKTVVx49ZimTHj1VIqVyR2R3ZU7D05FPoLvY8q9N0ELCRu5yZAMAtcUbg/7d359FRVGkbwJ9EICEsYVVAMqCDLLJDEkhkCySyCy4BiRvLsMmmCDPifEdAAUcH59MP9ajjMkF0TGTTAUUBhSAgMkoUMUCAAMlAgCQQIDFJJ3m/P3pSpLN0VVLV3VXdz+8cDtC3uuqt6lu37n1rIyKiqjBxS0REupjipRrZ2Th5KBcb99mTZBv3tcbJQ7lANq9eNBtfSViqrae3PA7Cl+h6kZxZ7kwgIiIiIkth4paIiHQxw0s1UnOaI/lUMFrY7EmyFrbzSD4VjNQc70oGKix8u7WvJCy1rKfVHwfhS/S+SM4UdyYQEREZSNcJTSLSjIlbIiLSxQwv1UiztcWmehNRL9B+WKsX6I9N9SYizeZdyUAAXvEsX19JWGpZTys/DsJXGPEiOVPcmUBERGQQvSc0iUg7Jm6JvJmFr8rzNVY/Y+3pl2qEhADJLWPwWkN7kuy1hkuQ3DIGISHujcPl/vssX5T+N4FUat1n+fpKwtJX1tPVPNlGGvEiOTPcmUBERGQEI05oEpF2TNwSuYjHE3F8CYpleMsZa0++VKNLF2DePCArwJ4cywpog/nz4darft0iOxvIzUVWPXsCKateayCXz/Il7+bpNtKI5zKb4c4EIiIiIxhxQpOItGPilsgFPD3I5EtQrINnrI0zcyYQH2//d3w83H7Vr1s0b46Uc8HYv8GeQNq/4TxSzgUDzXm7NXknM7SRRj2X2dN3JhARERnBV140S2QWTNyS+Vj89n4zDDL5EhTr4BlrY3nyql93SLnWFi9nTESp2A/fpeKPlzMmIuUab7cm72SWNtKo5zJ7extFRORpHr/r0U08uZ6+8qJZIrNg4pbMxQtu7zfDIJMvQbEOnrGmmkhPB3b6x2BDR3sCaUPHJdjpH4P0dA8HRuQiZmoj+bxi7XwlcUJE5uLxux7dxAzr6SsvmiUyAyZuyTy85PZ+Mwwy+RIU6+AZaw+w8FX9ISFAgwbA0Wv2xNHRa23QoAG87yVsRP/FNtJ6zJBQICLfY4a7Ht3BTOvJE5pE7sHELZmGt9zeb4ZBJl+CYi08Y+1GFr+qv+wlbP7/bV78/eGdL2EjKodtpHWYKaFARL7FDHc9uoOvrCcR3cDELZmGN93eb4ZBJl+CYi08Y+0GXnJVv0+8hI2oAraR1sCEAhF5ihnuenQHX1lPIrqBiVsyDW+7vd8Mg0y+BIXoBm+5qh/gvk1E5sSEAhF5ihnuenQHX1lPIrqBiVsyDd7eT0SuZKar+vniHiLyRkwoEJEnmeGuR3fwlfUkIjsmbslUeHs/EbmKWa7q54t7iMibMaFgHjxJSL7IDHc9uoOvrCcRMXFLJsRbgInIFcxwVT9f3ENEvoAJBc/jSUIiIjIKTwR6FhO3RORRPAiQO3n6qn6+uIeIiFyNJwlr4dw5x7+JiAgATwSaARO3ROQxPAiQJ3jyqn6+uIeIiFyNJwlraPt24AV7fxQvvGD/PxER8USgSTBxS0QewYMA+SK+uIeoGrzajcgwPElYAxkZQEICrly290evXC4FEhLsnxMR+TieCDQHJm6JyCOMPAjwcQtkJXxxD1EF27fjytP2q92uPM2r3XRjEtzn8SRhDWRn4+ShXGzcZ++PbtzXGicP5QLZTEqQ63DsQlbBE4HmwMQtEXmEUQcBPm6BrIgv7iH6r4wMpCxLwJ5d9qvd9uwqRcoyXu1Wa0yC03/xJKE2qTnNkXwqGC1s9v5oC9t5JJ8KRmoOkxKuwIQlxy5kLVpPBHLfdi0mbonII4y4GoSPWyAisrZTB7Nx9nAuLt5kv9rt4k2tcfZwLk4d5NVuNcYkOFXAk4Tq0mxtsaneRNQLtPdH6wX6Y1O9iUiz1ezqZCYt1DFhybELVcPkd8qonQjkvu16TNwSkcfovRqEz9whItLPkwmH9PzmuFwajLZ17Fe7ta1zHpdLg5Gez6vdaopJcKKaCwkBklvG4LWG9v7oaw2XILllDEJCtM+DSQt1TFjacexClRhwp4w7+nHVnQjkvu0eTNwSkUfpuRqEz9whItLH0wmHm/u0xY7mE1FQYO+SFhT4Y0fzibi5D5/FWVNMgnsfXsXpel26APPmAVkB9n5oVkAbzJ9v/1wLJi20YcLSjmMXcqD1ThknV+R6uh/H99a4BxO3RGRZfPkGEVHtmSHh0KULEPZMDN692X6127s3L0H4n2M0J03oBibBvYunB+O+ZOZMID7e/u/4eGDGDO3fZUJSGyYs7Th2ofI03Snj5IpcM/Tj+N4a92DilogsjS/fIPIMrzgrbvJnirmaWRIOM2cCf/3IfrXbXz9qU6OkCd3AJLj3MMNg3Nd06uT4t1ZMSGrDhOUNHLtQGdU7ZVSuyDVDP47vrXEPJm6pElcPxr1isE+mwpdvGEQtieXjSS66wSvOihvwTDGrM1PCobZJE3LEJLh3MMNgnLRhQlI7Jixv4NiFAPU7ZdSuyDVLP47vrXE9Jm7JgasH414x2CfyRmpJLCa5FL5+8skrzoprfaaYl2PCwTupJsF5Es5tanu8MMtgnLRhQlI7JiyJblC7U0btilwz9eP43hrXYuKWFK4ejHvFYJ/IG6klsZjkUvDkk3ecFdf0TDEfwYSDj+FJOLfRc7ww02CctGFCkohqw9mdMlqeXe8N/Tge89QxcUsKVw/GvWGwT+QprrzKUy2JxSSXnVtPPpn4ijhvOCuu+kwxH8OEg4/gSTi3MeJ44Q2DcdMw8TGViKi6O2W0PrveiH6cO+4odLYMHvOcY+KWFK4ejHvDYJ/IE1x9ladaEotJLju3nXwy+RVx3nBWXMsVDEQe4cIEE0/CuY9RxwueVDGAyY+pRETOuOPZ9e64o1DLMnjMqx4Tt6Rw9WDcGwb7RO7mjqs81ZJYTHLZueXkk0WuiLP6WXGtVzAQudX27cAL9kENXjA+wcSTcO7DixVMwiLHVCIiZ1z5Ald3jDX5yEz9mLglB64ejFt9sE/kbu64ylMticUkl507Tj5Z6Yo4q58Vd8cVDN7E11/K53IZGUBCAq5ctg9qrlwuBRKMTTDxJJz7aD1eHDvm+DcZy8hjKttAIvJG7hhr8pGZ+jFxS5W4ejBu9cE+kTu566odtSQWk1x2rj75xCvi3MuVVzB4E8u8lM/Kz7HMzsbJQ7nYuM8+qNm4rzVOHsoFst13ko6MpXa8eOstYHGcva4ujjuHt95ye4hez6hjqmXaQCKiGnLHWJN3oejHxC2Rh/DMPWnhzkeMqCWxmOSyc+XJJ14RR2ZjmdvbLP4cy9Sc5kg+FYwWNvugpoXtPJJPBSM1x3FQo7fvwJNw7lXd8SIlBTi4ajumXbTX2WkXX8DBVduRkuL2EL2aEcdUy7SBRES14I6xJh+ZqR8Tt0QewDP3VBN8xIjv4BVxZDaWuL3NC55jmWZri031JqJeoL1rXi/QH5vqTUSa7cagxqi+A0/Ced7FHzMQnZ2AwEB7nQ0MLEV0dgIu/midOmsFRhxTLdEG/hcvCiGi2nDHWJPjWX2YuCVyM565p9rgI0Z8B6+IIzOxwu1tVno2dHVCQoDkljF4raF9UPNawyVIbhmDkBB7OfsO3iUkKBtN/XORUWyvsxnFrdHUPxchQdaps1ah95hqhTYQ4EUhpmPlR/eQT3LHWJPj2dpj4pbIzax05t4U2PEhH8Qr4sgsrHB7mzc8G7pLF2DePCArwD6YyQpog/nzoVwZyL6Dd7k9rDl+1z0YN5fY6+zNJefRrkcwbg+zTp21Ej3HVCu0gVpP7PCKXDex+KN7iMh8mLglcjOrnLkHTNDBY8eHiMjjzH57m7c8G3rmTCA+3v7v+Hg4XBlopb4DadC2Lbosm4iBQ+x1duAQf3ReOhFoa6066yvM3gZqObHDK3LdxAse3UNE5sPELZGbWeHMPWCCDh47PkREpmHm29u86dnQ1V0ZaJW+A9VATAya/MVeZ5v8ZQkQY65kIDkycxuodmKHj1pxH0s9uod3NRJZBhO3RB5g9jP3hnbwatkpsFTHRwOPX71MROTFjHg2tNnbabP3HagW2rRx/LuGzF5nyT3UTuzwUSvuY5lH9/CuRiJLYeKWyEPMfObesA6ejk6BZTo+Gnj86mULOXbM8W8iIq30PMfSKu20mfsO5F5G1Vm9yV8mj83B2YkdPmrFfSzx6B7e1UhkOUzcElElhnTwdHYKLNHx0YC3p2n31lvA4jj7wG9x3Dm89ZaHAyIin8B2mqzGqDqrN/lrlRMevqK6Ezt81Ir7WOHRPd52VyORL2DilogqMaKDp7dTYIWOjxbuuj3N6le8pKQAB1dtx7SL9gHgtIsv4OCq7UhJ8XBgROT1eBsxWY0RdVZv8pcnPKyFj1pxHyMe3eNK3nRXI2D9MRCRFkzcElGV9HbwjOgUmL3jo4U7bk/zhiteLv6YgejsBAQG2geAgYGliM5OwMUfOQAkItfibcRkNUbUWb3JX57wsB4+asV99Dy6x9W85a5GwDvGQERaMHFLRNXS0sGr7iynUZ0CM3d8tHD17WnecsVLSFA2mvrnIqPYPgDMKG6Npv65CAniANBTeAUD+QreRuyI+76dmbeDEXVWb/KXJzyIrMlb7mr0ljEQkRZM3BJRrTk7y+ktnQIjuPL2NG+54uX2sOb4Xfdg3FxiHwDeXHIe7XoE4/YwDgA9gVcwkK/hbcR2XrXvnzvn+HcNWGE76K2zWpO/1SWw9X6fiDzHG+5q9JYxEJEWlkjcvv7662jfvj0CAwPRr18/fP/9954OicjnaTnL6Q2dAqO46vY0r7nipW1bdFk2EQOH2A9LA4f4o/PSiUBb37zizZN4BYP1WCExYoUYjWinrbCe1fGqfX/7dlx52p54vfL0C8B27YlXK20HvXVWLfmrlsDW+/0yavuNlfcrIrOy+l2NXjMGItLA9InbhIQELFy4EEuXLsWPP/6Inj17Yvjw4bh48aKnQyPyaVrPclq9U6CFJwcUXnWLb0wMmvzFPgBs8pclQIxvXvHmabyCwXyctTFWuDLQCjEawerr6TX7fkYGUpYlYM8ue+J1z65SpCxLADK0JV69ZjtoVF3yV2sCW+/31fYbq+9XROQaXjUGIlJh+sTt3/72N0yfPh1TpkzBnXfeiTfffBNBQUF47733PB0akU/jWU47MwwovOoW3zZtHP8mt+O+bS7O2hgrXBlohRiN4A3r6S37/qmD2Th7OBcXb7InXi/e1BpnD+fi1EFtiVdv2Q56uePlZWr7jTfsV0TkOl41BiJyoo6nA3CmqKgIP/zwA5YsWaJ85u/vj+joaOzfv7/K7xQWFqKwsFD5/9WrVwEANpsNNpvNtQGbQNk6VreuauVGzMPV5YzBHDG0H3ALjk6ZgKJNn9k/qF8PAQ/dg/YDbnGY3pu3w8k951CYuAEI+O85sAB/FCZuwNFxHStdeeLqGH/XvyWObbf/zX2XMXDf9o4Y1NqYC8cuIeCmfGS17ISmAK61vBUtzh/DhWOX0H7ALYbFqGceVojRiHJ3racrt4NR+74R66Gn/Gx+E1wOaI5bG1xCKYBbG1zC5cLmOJvfBCEa1sNbtoPeGJt1aoLzjZujUdElAECjoksoaNwczTo10bQdtHxfbb/xlfaDMVgrRsZgrhjUxkBmiJExGDcPb1KT9fQTEXFhLLqcO3cOt956K/bt24eIiAjl8z/+8Y/YvXs3Dhw4UOk7y5Ytw/Llyyt9/tFHHyEoKMil8RIRERERERERERFVJz8/H3FxccjNzUXjxo2dTywm9p///EcAyL59+xw+X7x4sYSHh1f5nYKCAsnNzVX+pKenCwDJysqSoqIir/+Tl5cnmzdvlry8vFqVGzEPV5czBsZglhhSdp6W9a1myvp2s2Xz5s2yvt1sWd9qpqTsPG2aGBkDY7BqjIxBWxvz2ZNfyvr2c+zl7efIvxZ+abrtYIUYjSh3x3paYTuYIYa33iqSsaEnZfPmzTI29KS8/bZvbgcjYvxlh307/rLjpEu+r7bf+Er7wRisEyNjYAxWi9FbYvC2P1lZWQJAcnNzVXOjpn5UQosWLXDTTTfhwoULDp9fuHABrVq1qvI7AQEBCAgIqPR53bp1UbduXZfEaUZq66tle+idh6vLGQNj8HQMnYe2w4kJ99tvZQaAwlIETrwfnYe2M02MjIExWD1GX45BSxsz9m934+iYTjh2LRld313s0vantvOwQoxGlLtzPc28HcwQw4wZQGRkCE6ePIxV74egWzff3A5GxNhxUAhOfH4YHQeF1GoZat9X2298pf1gDNaLkTEwBqvF6C0xeIuarKOpX05Wr1499O3bFzt37lQ+Ky0txc6dOx0enUBE5Cl8KD4RuZKWNqa6t7qbiRViNIKvrKcVdOrk+DeZl9p+w/2KiIh8makTtwCwcOFC/P3vf0d8fDxSUlIwe/Zs5OXlYcqUKZ4OjYgIAAcURORabGOIiIiIiHyTqR+VAAATJ07EpUuX8OyzzyIzMxO9evXCtm3bcMstt6h/mYiIiIiIiIiIiMiCTJ+4BYC5c+di7ty5ng6DiIiIiIiIiIiIyC1M/6gEIiIiIiIiIiIiIl/DxC0RERERERERERGRyTBxS0RERERERERERGQyTNwSERERERERERERmQwTt0REREREREREREQmw8QtERERERERERERkckwcUtERERERERERERkMkzcEhEREREREREREZkME7dEREREREREREREJsPELREREREREREREZHJMHFLREREREREREREZDJM3BIRERERERERERGZDBO3RERERERERERERCbDxC0RERERERERERGRyTBxS0RERERERERERGQydTwdgKuJCADg6tWrHo7EPWw2G/Lz83H16lXUrVu3xuVGzMPV5YyBMZgtBivEyBgYg9ViZAyMwWoxMgbGYLYYrBAjY2AMVouRMTAGq8XoLTF4m6tX7TnKspylM16fuL127RoAICQkxMOREBEREREREREREdlzlsHBwU6n8RMt6V0LKy0txblz59CoUSP4+fl5OhyXu3r1KkJCQpCeno7GjRvXuNyIebi6nDEwBrPFYIUYGQNjsFqMjIExWC1GxsAYzBaDFWJkDIzBajEyBsZgtRi9JQZvIyK4du0a2rRpA39/50+x9forbv39/dG2bVtPh+F2jRs3dlrZ1cqNmIeryxkDYzBbDFaIkTEwBqvFyBgYg9ViZAyMwWwxWCFGxsAYrBYjY2AMVovRW2LwJmpX2pbhy8mIiIiIiIiIiIiITIaJWyIiIiIiIiIiIiKTYeLWywQEBGDp0qUICAioVbkR83B1OWNgDGaLwQoxMgbGYLUYGQNjsFqMjIExmC0GK8TIGBiD1WJkDIzBajF6Swy+zOtfTkZERERERERERERkNbziloiIiIiIiIiIiMhkmLglIiIiIiIiIiIiMhkmbomIiIiIiIiIiIhMholbH1BSUoKrV6/qmsbV5YyBMVgtRnfHsG7dOpSUlDiUX7hwAQcPHlT+rzaNp8sZA2OoaQwHDx6EzWZzKBcRXLx40ZBydyyDMdxYxtNPP421a9eitLQUVdFb7o5lMAbvidEdy9i2bRuuX78OALDZbDhz5ozyxx3ljEF7jACQnZ2Nbdu24eOPP8Y333yDgoICt5ZbJYaqfPfddy4td8cyGIPxMZSUlGDbtm348ssvISK4dOkSDh06hLLXLOktd8cyjIjhwoULyrRbtmzBSy+9hA0bNijHDleXe0sMPk3Iq4waNarSZ1lZWRIeHq55GleXMwbGYLUYzRBDixYtZNSoUXL16lWlvKCgQCIiIpT/q03j6XLGwBhqGsOgQYOUz5OTk5V/R0ZGGlLujmUwhhvLGD58uHz44Ycybtw4WbdunZSUlEh5esvdsQzG4D0xumMZffv2Vf6dn58vrVu3ltjYWGnZsqVbyhmD9hjfeOMNiYmJkfnz50vfvn1lwoQJMnbsWNm1a5dbyq0SwxdffCE2m00qGjJkiCHl7lgGY3BfDBMnTpTFixfL1KlT5ZFHHpFJkyZJXFycTJ482ZBydyzDiBiioqJERGTRokUyd+5c2bhxozz77LPy8MMPu6XcW2LwZXU8nTgmY/3000+YMmWKw2d+fn5ISUnRPI2ryxkDY7BajGaIoXv37li0aBHGjh2L+Ph4tGvXDgEBAahXr54yvdo0ZeVjxozB2rVr3V7OGNwfgxH1xVX1Scs0derc6KbMnDlTuYKjbt26mspvuukmp+VapjFDDHrL1WJ013r6+fkhLi4OcXFxSEhIQGxsLB544AFMmjTJkHJ3LIMxeE+M7lhGgwYNlGXVr18fd9xxBxITEzF06FC3lDMG7TF+/PHH2L17NwCguLgYY8aMwZYtWzBixAgMHjzY5eVWiWHbtm344x//iNDQUMTGxiI6Otqhnd+2bRsWL16MsLCwWpWXTaO2DFeW+1IMRvwWzuaRmZmJjz/+GADQuXNnHD16FAAwaNAgQ8rdsQwjYijrIx06dAg7duwAANx7770YNmyYrvKyNkzt+0bMw5UxaN0OvoyJWy/Trl07LFq0CH5+fg6fL1q0yGGaxYsXV/pu2TSuLjdTDM62lavLzRSD3m3tDdvJHfVFz3YEgKioKISEhCAuLg4xMTGw2Wy49dZbHaZXm6ZieVFRkVvLGYN3x6BW32paZwMDA/HVV1/h6tWryM3NxWuvvYbGjRsrSUS18vr16zstr26aRo0aOV1GTcprG0NN1kOtXG071XY9axJDeHg4jh8/jrCwMAD2hFdpaSkWLFiASZMmITw8HKmpqU7LnX1f6zJcWe6LMZQds2pSXjFGtXlYdTsBwG233YZnnnkGAwcOxMaNGzFgwACU5+ryms5j06ZNquUDBw6sUbkR86hpeU3XEQD8/f1x4MAB9O7dG19//TUaNGiAOnXqKLc6ly/fuXOnrvKq5m/EPPTGqGUZr7zyCgDg22+/xSeffIJFixYhPDwc586dq7J88eLFNSqvzTKMLrdqDDXd1q74LSrOo6ioCKdPn0ZOTg4KCwvx2WefoVmzZsr8K5b/61//clpe8fu1mUdNl6H2fS3TDBs2DMuXL0fXrl2xZMkSREdH49ChQ7jtttt0ld9+++2avm/EPFwZg9bt4NM8dq0vuUR8fLymaWw2m/z666+yb98+SUlJkeLiYreVmykGPdtSb7lZYnj++ecrfVZQUCDXrl3TVP7WW285nb/eci3TxMfHy8aNG+XkyZNVlr/44otSVFTk9PsiIr/99pucP3++ylt+qtuWv/32m+by69evO9wSXt38s7Ky5MCBA5KZmal8lpiYqNTppKQkWblypaxfv96hXqtNk5iYqExbXFwsmzZtkg0bNijr6+pyI+ZRfjuVlX/44YdKfVQrN2IeauVr165V3Q5q0+gtT0xMVPaJ8uUV64uzabSUi9jreHp6uiQmJlaqb2V1vuz769evr7Y+ZGVlyUsvvSTr1q1TpsnMzJSFCxfKjBkz5MyZM/Lxxx/LihUrlH0jMzNTnnrqKZk1a5acPXtWNm/eLCtXrpTz58+LiMi5c+eclmuZR2ZmpgwdOlSmTJkiZ86cUcrLx6BWXnH+K1asUI2h/DRGlpfFWH47at0OCxcu1LyMxMREWb58ucN6LliwQJx5/PHH5ddff5X9+/dXedxW+77aNBkZGZXKCwsL5fr164aUGzGPwsJCmT9/frXroFZuxDy0LKNnz57VHnO1lKtNc/HiRZkzZ06139VbbtQ8qqpvBQUFyr9LSkpk7dq1MmHCBFm5cqVSr7OystxSXjbNBx98IEuXLpWNGzcqn5dNk56eXqm8sLBQ0tPTle1Q1fcvXbqkqdyIeaiVV7eOZetQvjwhIUHpY5XfTqdOnZLHHntMwsLCZMaMGUrbtXnzZqV88uTJMmjQIJk1a5ZcuHChxuWjRo2S3r17V1le1TzK2uhNmzY5lA8cONChvCYxPPbYY9V+v+Iyyo69JSUlDtNUlJSUJAsXLnT4LD8/X86dOyf5+fm1KteyDGflBw4ckNzc3FqXGzEPd8RQUVJSkjz55JO6yrX8FlrnsX//fhk5cqRMnDhR/vOf/8if//xnmTFjhhw7dkwpHzFihMTGxsrZs2eVPl/58qq+f/ToUWV5atOULWPChAmSkZFRKYbvvvtO0/e1xnD27Fl5/PHHZfr06Q7T7NmzRxYtWiQPP/ywzJs3Tz755BOHx+skJSXJfffdJ1FRUTJs2DBJTEx0KK/4/cTERKWflJ6eXql87dq1DuOfgoICSUpKqnYeIvZx/7333isPPfRQpRgvXLjgsIy5c+dW+v6ePXvkqaeekmHDhsmcOXMqrUN126F8nGXlo0ePlpEjR8ratWsr9Qd9ERO3PigxMVEiIyNl6tSp8uSTT8q0adMkIiJCGUC7utwsMSxZsqTStjl//rw8+uijbik3Swx169aVZcuWVZpm6NChmsqHDBlSKdFps9nk/vvvN6Rc6zSDBg2S2bNny6xZsyQtLc1h2r59+0phYaGIiOTk5Ejnzp0lNDRU7rjjDhGxH2yjoqKkR48eUr9+fYmIiJBJkyZJRkaGMo/ynabyyp7hpFb+17/+VSIiIqRnz57y5ptvyj333CNdu3aV1157TUREHnzwQRER+eCDDyQyMlLmz58vw4YNk5dffllEqq7TkZGRqvtV+WnMUN/0zkNvfTViHnr3CS3TGLHfONsntE7jrFxtv1Gr8yLV1/vVq1eLiL0TO3jwYBk2bJiMHz9ehg0bJoMHD5Zvv/1WREReffXVSjFfuHBBnn76aRERqVevnvzjH/9wKE9LS5OxY8cq/x8/frzSPpTflnPnzlX+HxERIbGxsfL8889XefLFWXloaKjyWU5OjgwbNkyioqIcniGnNo3e8mPHjskDDzwgd9xxhwQEBMigQYNkwYIFcuXKFSWG0tLSSuslIjJu3DgRsf9Gffr0ka5du8q//vUveeSRRyQ8PFzWr18vIiJPPPGEiIh8+eWX0rt3b7nnnnukb9++8tFHH1U534oSEhKcHreNaF/q1Kkjy5cvr1Retu/qLTdiHu44ZhrVvsyaNctp2+GsXG2a6o7bHTp0MKTciHmoHfdFbtTradOmVVmvXV0uon+/qGo7hIWFKX0otXIj5qFWrvZbqPWxROzJgrLjzb333uuS443asaRiWz506FAZMmRIlW15YGBgpbZcrR1ft26d9OnTR7p166a042FhYbJhwwZl2opt+dixY6VPnz7yz3/+s8p5V1S2HYcOHVrldlQrF1Hf1lqP2w888IDT47azciPm4eoYqqovzvoGFcuN6BuU1anyfYOKdcqZ8vtdWT9vyJAhSn3Q8lur9YGOHz/udD1rs47h4eFO95uyPpDW/aaqtrwmY7g6deqojn9qMnaYPXu26nG5S5cuDu3wvn37lHFBUFBQrcbTauMCX8bErQ+KjIysdPVhcXGx8gIRV5ebJYZu3brJP/7xD4c/8fHxyosKXF1ulhiioqLk+eefl8mTJzscGAcPHqypvPwApfzLaYwq1zrNiBEjRETkzJkzMnfuXJkzZ46cPn260nTl51f2APSBAwcqB5KMjAx58MEH5dy5c3LPPfco3wkLC5MxY8ZIfHy8Q4embB5q5f379xcR+1Usbdu2lWvXrklpaany8rHyg/by2/muu+4SEWP2KzPUN73z0FtfjZiH3n1CyzRG7DfO9gmt0zgrV9tv1Oq8iLZ6X35/ErF3+srqdN++fWXXrl2V/rRp00ZERAYMGCDTpk2TpUuXOsxjwIAByr/Lb8uyDmN123Lfvn0yadIkWblypcNVls7Ky/8+Zf/Py8tz+FxtGr3lUVFRcuLECRER+eGHH2T69OmSnJwskyZNUr7TvXt3mTt3ruzevdthEFPWhoWHh0tRUZFcvnxZ2rRpIxkZGXL9+nUJCwsTkRu/ZVRUlDKAKikpUeqBGr3tl5ZpXL3vG7WMMlXt22rlRsxDyzL0tB1aplE7bustN2Ieasd9EWv0d6urk2W/vRm2td7fQu1YI6LtePPNN9/oOt5UPFasWrXK4Viity3X245X3FZlx/iatuXOtqNauYj6sV3LcXvkyJEi4rity1/Vp1ZuxDxcHYMRfYOyux709A1sNpvTOuWMWn3Q8lubab/xVB9Ia/+kqm1W2/F0xT6DEeNpLW21r+Izbn1Q/fr1cfDgQURGRiqfHThwAEFBQW4pN0sMJSUluH79eqVnii5btswt5WaJQUTwP//zP1i/fj3uvvtuLFu2DEVFRcpzCasqt9lsSnlpaSlycnKQn5+P1NRUHD58GMHBwSgsLDSkXMs0U6dOxeHDh5WXe/n5+eHs2bPo1KkTCgoKEBAQgC+//BKDBg3Chg0bKj1j02azKQ9Dr1OnDi5duoTWrVvj+vXryjTff/89jh8/jsTERERHR+OWW25BbGws8vLyNJWLCIqLi1FYWIjS0lLk5eXhpptuUn6b9u3bY/fu3ejVqxe+/vprxMTE4NChQ2jUqBEAY/YrM9Q3I+bhrD6q1efqpqnpPPTsE1qm0Vuutk9omUatXG2/UavzgHq99/f3x9WrVxEcHKx8Jzc3V1ludnY2tmzZUqm+PPTQQ0pc77zzDv72t7/hgQcewOrVq2Gz2Rymt9lsKCwsREFBAXbt2oULFy6gYcOG+O233wAAzz33HE6cOIHly5cDsL904ttvv8Urr7yCixcvqpYDwK+//oo777wTSUlJaNq0KYKCghxe5qVlGj3lBQUFaN++PQCgU6dOOHHiBHr27KnEBwDJycnYsWMH1q5di9mzZyMqKgqxsbEoKSlRtmXdunWV5x1WXEbz5s2RmpqKdu3a4ezZs+jatSsyMzNRv359aKG3/dI6jbN914jy2hxTy5eXlJQ43bfVyo2Yh1q53rZDyzzUjtt6y2s6j/Xr11cqVzvuA9bo7wJV1+uyFwyZYVurfV/tt1A71gDajjdbt26t9fGmqmPFnj178L//+78ObbGetlxvOw7caMvbt2+P9PT0GrflattRrbxsWzs7tms5bqempla7rdXKjZiHO2JQqy9q5QUFBWjXrh0AfX2DOnXqOK1TzqjVB7XfWut6umu/8WQfSK1/ovfYXrEdbtu2rUMsRoyntbTVPstjKWPymIyMDJk1a5ZERkZKv379JCIiQmbOnCn/+c9/qiyPjIx0Wq72/YrltVmG0eUiovr8HleXmyWGZ555Rvn3yZMnZcGCBTJv3jw5e/ZspfITJ07IggULZO7cuUr5t99+K4MGDZLevXvL9u3b5cknn5To6GjZs2ePiIjs3bvXabna97VM8+uvv8rAgQPll19+kSNHjjj8EbHXh2nTpsngwYNl3rx5cvnyZRER2bFjh4iIbN26VUJDQyUyMlLCwsJk3759IiKVbjkp7+jRo/Lcc89Jnz59NJW//fbb0qNHD+nbt6988cUXEh0dLX379lVus/ntt99kxYoVEh0dLW3btpXu3bvL448/rjx/rDb7VcVpzFDf9M5DrT6q1eeazqOsvLp9omwZNdkntExTXXlSUpKm76vtE+WnqVheNo3aPNT2G7U6L6Je73/++WcZPXq09O/fXwYOHCgREREyatQo+eWXX0REJC4uzll1kalTpyr/3rNnj4wbN07GjBkjycnJyuebNm2S0NBQ6dixo6xdu1Yeeugh6dWrl/IIgLL13LJli2zdutXhj4jI559/LqGhoZXKysoPHz4sd999t4SGhso999wjZ86cERH7rXdlyqYJCwuTe+65R6lLZdOozaPi9yuWv/vuu9KvXz+ZNGmS9OnTR3lW4axZs6rcbsXFxbJt2zaZOnWqNGvWTEREli5dKlFRUTJgwAB5++23pX///tKjRw/l0RfZ2dkye/ZsGTBggNStW1c6d+4s48ePl5SUFKe/URm97ZeWaWqz76u1L+XLq5uHWvtRvry2bUP59qW2x11n7UtMTIzm9qUm7U9tj9vly+fPn++0vKrvG7GMiqrqF+jtM+vts4vo3y9qsp1cta21LMPZb6F2rBFx/fFG7Vgiot7W16Qtr007LqK/LVfbjmXlERERVZZr2dZ6j9tafouyY7vasV9tGdX1DbTEoDaN1r6BEfVJpPZ1yhm1+qL2Wxu9np7ab/T2gdTacRFrjKe1tNW+yk+k3GskiQxy7tw5tGrVCv7+/p4OxfS4rYxRVFSEevXq6ZpHaWkpfwfyGlr2icLCQgQEBOiah7v2m+LiYocrB9wpPT0dISEhtS43g4KCApw/fx633nprrdvKCxcuIDAw0OHKmKqUlJRovtrGbHhM1katbdDSdhhx3PYF3lInrbQerjreGHGs0NuWa23HAf1tudp2dOVx3Yjjtt55uCMGvdzZN1Djyvpgpf3GkzieNi9uUaokJSUF999/PyIiItCvXz9ERETg/vvvx7FjxwAACQkJlb6TlZWFNWvWKP8fP348xo0bhw8++AClpaWVph83bhyuXbsGALh8+TKmTp2KKVOmKJfmX7x4EYsWLUL//v3RuXNnxMTE4OWXX0ZRUREAYPv27Rg0aBAGDBiAgwcP4plnnsG4ceOwd+9eAMCLL74IAPjxxx8xaNAghIeHIzIyErt27arVdujfvz8iIyNrtB06dOiAHTt2OJSfPn0as2bNcvjM2bYq2w4RERFVbofqlC2jbDscOnSo2u2g9/fevXt3pfLLly9j/fr1AIDo6GgcPny40nZ47rnnlP/36tULDzzwAPbs2eN0vSoqf3uIEYM/PQcZte2gl5b9Tmudq47eOq+2XwP663RN1qGq+gyobyet7VNN16G8zMxMLFy4EAMGDEC/fv0wYMAALFy4EBcuXAAA5OfnY82aNXjwwQcxYsQIPPbYY9iwYYPy/eTkZEyaNAmTJk1CWloa3n77bSxYsMBhPdX2iZSUFMTFxVW7rcaNG6fcPlXd7wno229qwlNJWwBOB03Lly/He++9h+XLl1f6o5VafdDSvqi1o4GBgbjtttt0tZW33HKLpkGLpwYsWtpJNXqPyVr7J86Oy86o1RXA3l6WtWFXrlypVCfV2pf4+HgAQFpaGmJjYxEeHo6xY8fil19+UaZxVo8SEhIqlVd1vEpKSnKYprbHq9r2VfX2RY2gt0+t5bjrarNnz660TYqLi7Fy5UqHz7TsW7Xt9xvNVccbIxJwettyre04oL8tr2o7lu+/pKenV9l/MYKzbb179+5K5RWPqdHR0bhy5YrDNOXHL8uXL8ctt9ziUF5cXIz3338fgL2d/fTTT6ttZ6uLsfzYJj4+HiEhIU7bYme09B3c2TdQ48p+npX2G0/y9HianPD0Jb9kPgMHDpRTp045fHby5EnlIdYDBgyQtLQ0OX36tPLn1KlT0r59e2X6kSNHSklJiXz00Ucyfvx4+fDDDx0e9l3+Qf0i9jdmHjlyRHlpzciRI2Xnzp2Sk5MjGzZskMWLF8tnn30mM2fOFBGRfv36yeXLlyUtLU3atm0rBw4ckNTUVOnXr5+I3HjAdXR0tKSnp4uIyNWrVyUiIsJt2yE0NFRGjx4t7733nsM8yj98X21bqW2Hu+66S1avXl3phR9l669lO+hdz+jo6ErbzmazSadOnUTE/jD1gQMHyldffeUwTfmHtY8cOVKuXbsmL7zwgkycOFH27t3rMO2DDz4o69evl/z8fIfPyz+w3tPUtoNeWvY7rXWuOnrrgtp+LaK/TutdBxH17aS3fdIiJiZGvv76a4fPvv76a7n77rtFRCQ2Nlbeffdd+fHHH2X16tXy1FNPyf/93/8pb5W966675MiRI5KUlCS///3vJSEhQbZv316jh/dr+b2dbQey69Gjh3IbY/fu3R3+rZVafdDSvqi1o75ASzupRs8xWcT1/RO1uiJSuc3v3bu3Q51Ua1/KYhw9erQcPHhQREROnTolAwcO1BSjFY5XZdOU54q2Xo3ePrUZ2unyfbGyN6uLVH5hkJ59S22/Iuswov+il5Zjqtr4pXy9/8tf/qL8u6ydVGtntYxt9LbFrh6bEJH78OVkVElpaSlatGjh8FnLli2VB3SfOHECixcvrvRw7LCwMADA2rVrkZGRgQ8++ACA/WqAdevWYeXKlThy5IiyjIsXL+Lmm2/GsWPH0KhRI9x5551o0KABAODatWsYOnQoAPsZ+jVr1uCll17Cq6++CsB+NqhJkyZo0qQJAgMD0adPH+Xh6ADQoEEDZGdno2nTpsrLFW666Sbl3+7YDg0bNsRnn32GBQsW4KeffsKqVatQVFQEKfd0ErVtpbYdPv30U2zYsAF/+MMfkJeXh/vuuw+xsbHKMrRsB73refDgQYSFhVUqb9y4MQCgRYsW2Lp1Kx599FEcOXIETzzxhMPLGpKSkpCdnY0ffvgBERER6NGjB1asWAERwRdffAEA+NOf/oTExEQ8//zz6NSpE2JjYzF69OhqfztPUNsOeqn9DoC2OueM3rqgtl8D6vu2Wp3Wuw6A+nbS2z5pkZ+fj4EDBzp8NnjwYDz77LMAgEuXLmHq1KkAgN69eyMqKgqrV69GdHQ0AKBu3bq48847AdjbwwkTJgAAVqxYoTkGtW2l5fck+wspytqjv/71r8q/V69erXkeavVBrX3R0o76Ai3tpDN6j8mA6/snanUFsO+7+fn5CAoKwsWLFxEcHIxRo0YpdVJL+/Lbb7/Bz88PXbp0AQC0a9dO81U0Vjhelc3D1W29Gr19ajO00zabDSICm82GTz75BCtWrED9+vWVF9FpWQ+9/X6yDiP6L3pp6bOrjV/K2hoRwVtvvYWnnnoKderUQXFxMQD1dlbL2EZvW+zqsQkRuQ+PdlTJ8uXLMWrUKDRt2hTBwcG4evUqsrOzlVtDunfvjk8++aTa71+/fh2lpaUObz4cM2aMwzQrV67EqFGjULduXYgI3n77bQBQDlgxMTEYPXo0evbsiX379iEuLg4AlDcKdu7cGVOmTMGVK1cwceJE3H333fD398fgwYMBAGvWrMGcOXNw4cIFdO7cGd26dUOdOnVq1CnQux2CgoLg7++PNWvW4KOPPsKwYcNQWlqKVatWad5WatuhefPmmDFjBmbMmIGsrCxs2LAB06ZNw4EDBzRvB73rGR4ejq+++qracj8/PwQFBWH9+vVYtWoV+vTpg+LiYvzpT38CAGzZsgVZWVkOb4/t1q2bwzx69eqFXr16YdWqVTh06BASExPx3HPP4cyZM9Uu193UtoNear8DoK3OOaO3Lqjt14D+Oq13HbRsJ73tkxazZs3CkCFD0KdPHyXOgwcPYs6cOQDsb719/PHHlbeqRkVFAYBye2nTpk2xfPlyZGVloU+fPpgwYQIaN26M2267TXMMattKy+9JjioOjrRSqw9q7YuWdtQXaGknndF7TAZc3z9RqysAsHDhQkRGRqJTp05ISUlRkl99+/YFoN6+vPDCC4iLi4O/vz969eqF/v37IyMjA48//rimGK1wvALc09ar0dunNkM7/fDDDyMqKgp5eXmYM2cO4uLicOLECcyePVvzeujt95N1GNF/0UtLn11t/BIVFYW4uDjk5eVh+PDhmDhxInJycpT2VK2d1TK20dsWu3psQkTuw5eTUbVycnKQk5ODZs2aoVmzZjX67r///W+EhoaqTici1Q50T58+jbS0NHTq1Alt2rSpVP7999+jcePG6Ny5M65evYr8/Hy0atXKYZq8vDxkZmaiadOmNV6HMnq2gxZq20ptO1QlNzfX4Tk8WrZDbdczMzOz0nYv79q1a6qDnK1bt9ZqkHH48GF07969xt9zBbXtYCV667yz/Rowpk6rMWK/1dM+aVFYWIgjR44ocXbr1s3h2VK7d+/GqVOn0LVrV4SHhwO48dKAoqIibN68GcHBwRg+fDiOHz+OnJwc9O/fv8ZxqG0rtd/T123ZskVJQKSmpuKOO+4AAOzZs6fSlZHOOKsPWtqX2raj5MiIY7Kr+ydqbUfZNOfPn0fbtm2rvCrRWftS5siRI0qMVS3DDKzQ1uulpU/t6XY6NzcXBQUFyjM/q4pH776lZb8i8zOy/1JbWo6pWsYvv/zyC/Lz8xEeHo6CggLk5OQ41F0t7WxFVY1tatsWe9PYhMjXMXFLREREREREREREZDJ85RsRERERERERERGRyTBxS0RERERERERERGQyTNwSERERERERERERmQwTt0REREREREREREQmw8QtERERERERERERkckwcUtERERERERERERkMkzcEhEREZHpTJ48GX5+fsqf5s2bY8SIEfj5559dutwhQ4YoywwMDETHjh3xwgsvQEQMXc7kyZMxfvx4Q+dJRERERN6FiVsiIiIiMqURI0bg/PnzOH/+PHbu3Ik6depgzJgxLl/u9OnTcf78eRw7dgxLlizBs88+izfffNPlyyUiIiIiKo+JWyIiIiIypYCAALRq1QqtWrVCr1698PTTTyM9PR2XLl0CAOzatQt+fn64cuWK8p3k5GT4+fnh9OnTAIAzZ85g7NixaNq0KRo0aICuXbvi888/d7rcoKAgtGrVCu3atcOUKVPQo0cPbN++XSm/fPkyHn30UTRt2hRBQUEYOXIkUlNTlfJly5ahV69eDvN85ZVX0L59e6U8Pj4en376qXJ1765duwAA6enpmDBhApo0aYJmzZph3LhxyrqUrXN4eDgaNGiAJk2a4K677sKZM2dqtmGJiIiIyBKYuCUiIiIi07t+/TrWrVuHDh06oHnz5pq/N2fOHBQWFiIpKQmHDx/Giy++iIYNG2r6rohgz549OHr0KOrVq6d8PnnyZPz73//GZ599hv3790NEMGrUKNhsNk3zXbRoESZMmOBwRXFkZCRsNhuGDx+ORo0aYc+ePdi7dy8aNmyIESNGoKioCMXFxRg/fjwGDx6Mn3/+Gfv378eMGTPg5+eneXsQERERkXXU8XQARERERERV2bJli5JkzcvLQ+vWrbFlyxb4+2u/9uDs2bO4//770b17dwDA7bffrvqdN954A++88w6Kiopgs9kQGBiI+fPnAwBSU1Px2WefYe/evYiMjAQAfPjhhwgJCcHmzZsRGxurOv+GDRuifv36KCwsRKtWrZTP161bh9LSUrzzzjtKMvb9999HkyZNsGvXLoSGhiI3NxdjxozB73//ewBAly5dNG8LIiIiIrIWXnFLRERERKYUFRWF5ORkJCcn4/vvv8fw4cMxcuTIGj0aYP78+VixYgXuuusuLF26VNPLzR566CEkJydj7969GDlyJP785z8rSdqUlBTUqVMH/fr1U6Zv3rw5OnXqhJSUlJqvZDk//fQTTpw4gUaNGqFhw4Zo2LAhmjVrhoKCApw8eRLNmjXD5MmTMXz4cIwdOxavvvoqzp8/r2uZRERERGReTNwSERERkSk1aNAAHTp0QIcOHRAWFoZ33nkHeXl5+Pvf/w4AypW3IqJ8p+LjCv7whz/g1KlTeOSRR3D48GGEhoZizZo1TpcbHBysLDMxMRGvvfYaduzYoTluf39/h5iqiqsq169fR9++fZVkddmf48ePIy4uDoD9Ctz9+/cjMjISCQkJ6NixI7777jvNsRERERGRdTBxS0RERESW4OfnB39/f/z2228AgJYtWwKAw1WnycnJlb4XEhKCWbNmYePGjXjqqaeUxK8WDRs2xIIFC7Bo0SKICLp06YLi4mIcOHBAmSY7OxvHjh3DnXfeqcSVmZnpkLytGFe9evVQUlLi8FmfPn2QmpqKm2++WUlYl/0JDg5WpuvduzeWLFmCffv2oVu3bvjoo480rw8RERERWQcTt0RERERkSoWFhcjMzERmZiZSUlIwb948XL9+HWPHjgUAdOjQASEhIVi2bBlSU1OxdetWvPzyyw7zeOKJJ/Dll18iLS0NP/74I7755psaPxd25syZOH78ODZs2IA77rgD48aNw/Tp0/Htt9/ip59+wsMPP4xbb70V48aNAwAMGTIEly5dwksvvYSTJ0/i9ddfxxdffOEwz/bt2+Pnn3/GsWPHkJWVBZvNhoceeggtWrTAuHHjsGfPHqSlpWHXrl2YP38+MjIykJaWhiVLlmD//v04c+YMvvrqK6SmpvI5t0REREReiolbIiIiIjKlbdu2oXXr1mjdujX69euHgwcP4pNPPsGQIUMAAHXr1sU///lPHD16FD169MCLL76IFStWOMyjpKQEc+bMQZcuXTBixAh07NgRb7zxRo3iaNasGR599FEsW7YMpaWleP/999G3b1+MGTMGEREREBF8/vnnqFu3LgD7C8PeeOMNvP766+jZsye+//57LFq0yGGe06dPR6dOnRAaGoqWLVti7969CAoKQlJSEn73u9/hvvvuQ5cuXTBt2jQUFBSgcePGCAoKwtGjR3H//fejY8eOmDFjBubMmYOZM2fWfiMTERERkWn5ScUHcBERERERERERERGRR/GKWyIiIiIiIiIiIiKTYeKWiIiIiIiIiIiIyGSYuCUiIiIiIiIiIiIyGSZuiYiIiIiIiIiIiEyGiVsiIiIiIiIiIiIik2HiloiIiIiIiIiIiMhkmLglIiIiIiIiIiIiMhkmbomIiIiIiIiIiIhMholbIiIiIiIiIiIiIpNh4paIiIiIiIiIiIjIZJi4JSIiIiIiIiIiIjIZJm6JiIiIiIiIiIiITOb/ASUItDIXxRPrAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total ridership is 16763366.7. \n", + "\n", + "This table shows the ridership by bus routes. \n", + " \n", + " total_boardings total_alightings difference\n", + "route_id \n", + "1 595944.2 596192.5 -248.3\n", + "10 126724.7 126064.2 660.5\n", + "100 36417.1 36643.9 -226.8\n", + "101 150782.0 151046.8 -264.8\n", + "104 296804.2 298002.0 -1197.8\n", + "... ... ... ...\n", + "94 30530.2 30667.7 -137.5\n", + "95 53999.6 54253.3 -253.7\n", + "96 45616.1 45619.1 -3.0\n", + "97 31631.4 31571.4 60.0\n", + "99 40613.1 40772.3 -159.2\n", + "\n", + "[147 rows x 3 columns] \n", + "\n", + "This table shows the peak boarding times per bus route. \n", + " \n", + " route_id trip_start_time stop_name total_boardings\n", + "540 1 05:05:00 NUBIAN STATION 1157.0\n", + "17196 10 13:40:00 TOWNSEND ST @ WARREN ST 1419.8\n", + "23668 100 16:30:00 WELLINGTON STATION BUSWAY 970.2\n", + "28984 101 17:42:00 SULLIVAN STATION BUSWAY 1376.4\n", + "37909 104 23:30:00 SULLIVAN STATION BUSWAY 2704.8\n", + "... ... ... ... ...\n", + "791416 94 16:28:00 DAVIS BUSWAY 1476.6\n", + "796738 95 17:45:00 SULLIVAN STATION BUSWAY 958.1\n", + "801695 96 17:37:00 DAVIS BUSWAY 1360.8\n", + "805651 97 16:20:00 MALDEN CENTER EAST BUSWAY 2 783.9\n", + "806697 99 16:08:00 WELLINGTON STATION BUSWAY 1297.2\n", + "\n", + "[147 rows x 4 columns] \n", + "\n", + "This table shows the peak alightings times per bus route. \n", + " \n", + " route_id trip_start_time stop_name \\\n", + "11833 1 15:15:00 NUBIAN STATION \n", + "19191 10 06:15:00 WARREN ST @ TOWNSEND ST \n", + "24371 100 06:00:00 WELLINGTON STATION BUSWAY \n", + "26439 101 06:50:00 475 WINTHROP ST @ TEMPLE SHAL \n", + "38517 104 04:45:00 SULLIVAN STATION BUSWAY \n", + "... ... ... ... \n", + "792928 94 08:45:00 COLLEGE AVE @ HIGHLAND AVE \n", + "797783 95 06:55:00 SULLIVAN STATION BUSWAY \n", + "802553 96 07:30:00 COLLEGE AVE @ HIGHLAND AVE \n", + "804659 97 06:10:00 WELLINGTON STATION BUSWAY \n", + "808137 99 05:50:00 WELLINGTON STATION BUSWAY \n", + "\n", + " total_alightings \n", + "11833 1112.4 \n", + "19191 1186.8 \n", + "24371 958.5 \n", + "26439 2083.7 \n", + "38517 3201.0 \n", + "... ... \n", + "792928 957.0 \n", + "797783 1502.8 \n", + "802553 1145.4 \n", + "804659 1073.1 \n", + "808137 904.8 \n", + "\n", + "[147 rows x 4 columns]\n" + ] + } + ], + "source": [ + "ridership('MBTA_Bus_Ridership_Fall_2022.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/arrdep_data_cleaning.ipynb b/notebooks/arrdep_data_cleaning.ipynb index e272e02..caa722d 100644 --- a/notebooks/arrdep_data_cleaning.ipynb +++ b/notebooks/arrdep_data_cleaning.ipynb @@ -16,7 +16,12 @@ "outputs": [], "source": [ "import os\n", - "import pandas as pd" + "import numpy as np\n", + "import pandas as pd\n", + "from joblib import Parallel, delayed\n", + "from tqdm.notebook import tqdm\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=pd.errors.SettingWithCopyWarning)" ] }, { @@ -34,12 +39,12 @@ "metadata": {}, "outputs": [], "source": [ - "folder = os.path.join(\"..\", \"dataset-documentation\", \"raw_data\")" + "folder = os.path.join('..', 'dataset-documentation', 'raw_data')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "97daecc3-0ddd-4016-b001-b7bcf42589ae", "metadata": {}, "outputs": [], @@ -47,209 +52,190 @@ "data_18augsep = pd.read_csv(\n", " os.path.join(\n", " folder,\n", - " \"MBTA Bus Arrival Departure Times 2018\",\n", - " \"MBTA Bus Arrival Departure Aug-Sept 2018.csv\"\n", + " 'MBTA Bus Arrival Departure Times 2019',\n", + " 'MBTA Bus Arrival Departure Jan-Mar 2019.csv'\n", " )\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "04d8f506-2769-45b6-b89f-b366ef50f5d0", "metadata": {}, + "outputs": [], + "source": [ + "data_18augsep.head().to_csv(\"./presentation/origin_arrdep.csv\", index = False)" + ] + }, + { + "cell_type": "markdown", + "id": "ba71f5dd-139a-4169-b0b0-50c64e9ab0c6", + "metadata": {}, + "source": [ + "# Data cleaning" + ] + }, + { + "cell_type": "markdown", + "id": "10d5ff7b-6794-43db-b6f0-33703bad9322", + "metadata": {}, + "source": [ + "## drop 'earliness' variable & missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5054a291-b26e-4c59-beab-8b552d5e4ab9", + "metadata": {}, + "outputs": [], + "source": [ + "if 'earliness' in data_18augsep.columns:\n", + " data_18augsep = data_18augsep.drop('earliness', axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "21175add-16bc-496f-9b47-acd70fadc5e2", + "metadata": {}, "outputs": [ { "data": { - 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service_dateroute_iddirectionhalf_trip_idstop_idtime_point_idtime_point_orderpoint_typestandard_typescheduledactualearlinessscheduled_headwayheadway
02018-08-01T00:00:00Z01Inbound40121394.075mit4.0MidpointSchedule1900-01-01T05:19:00Z1900-01-01T05:19:34Z-10.0NaNNaN
12018-08-01T00:00:00Z01Inbound40121394.079hynes5.0MidpointSchedule1900-01-01T05:22:00Z1900-01-01T05:23:20Z3.0NaNNaN
22018-08-01T00:00:00Z01Inbound40121394.0187masta6.0MidpointSchedule1900-01-01T05:25:00Z1900-01-01T05:25:58Z-33.0NaNNaN
32018-08-01T00:00:00Z01Inbound40121394.059Wasma7.0MidpointSchedule1900-01-01T05:28:00Z1900-01-01T05:28:26Z7.0NaNNaN
42018-08-01T00:00:00Z01Inbound40121565.0110hhgat1.0StartpointHeadway1900-01-01T05:30:00Z1900-01-01T05:29:57ZNaN1200.01218.0
\n", - "
" - ], "text/plain": [ - " service_date route_id direction half_trip_id stop_id \\\n", - "0 2018-08-01T00:00:00Z 01 Inbound 40121394.0 75 \n", - "1 2018-08-01T00:00:00Z 01 Inbound 40121394.0 79 \n", - "2 2018-08-01T00:00:00Z 01 Inbound 40121394.0 187 \n", - "3 2018-08-01T00:00:00Z 01 Inbound 40121394.0 59 \n", - "4 2018-08-01T00:00:00Z 01 Inbound 40121565.0 110 \n", - "\n", - " time_point_id time_point_order point_type standard_type \\\n", - "0 mit 4.0 Midpoint Schedule \n", - "1 hynes 5.0 Midpoint Schedule \n", - "2 masta 6.0 Midpoint Schedule \n", - "3 Wasma 7.0 Midpoint Schedule \n", - "4 hhgat 1.0 Startpoint Headway \n", - "\n", - " scheduled actual earliness scheduled_headway \\\n", - "0 1900-01-01T05:19:00Z 1900-01-01T05:19:34Z -10.0 NaN \n", - "1 1900-01-01T05:22:00Z 1900-01-01T05:23:20Z 3.0 NaN \n", - "2 1900-01-01T05:25:00Z 1900-01-01T05:25:58Z -33.0 NaN \n", - "3 1900-01-01T05:28:00Z 1900-01-01T05:28:26Z 7.0 NaN \n", - "4 1900-01-01T05:30:00Z 1900-01-01T05:29:57Z NaN 1200.0 \n", - "\n", - " headway \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 1218.0 " + "service_date 0.000000\n", + "route_id 0.000000\n", + "direction 0.000000\n", + "half_trip_id 0.000021\n", + "stop_id 0.000000\n", + "time_point_id 0.000225\n", + "time_point_order 0.000225\n", + "point_type 0.000000\n", + "standard_type 0.000000\n", + "scheduled 0.000000\n", + "actual 0.063560\n", + "scheduled_headway 0.504160\n", + "headway 0.573335\n", + "dtype: float64" ] }, - "execution_count": 4, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data_18augsep.head()" + "data_18augsep.isna().sum() / data_18augsep.shape[0]" ] }, { - "cell_type": "markdown", - "id": "ba71f5dd-139a-4169-b0b0-50c64e9ab0c6", + "cell_type": "code", + "execution_count": 6, + "id": "43c3b9d0-151a-4aaf-84b6-0d52952cb6c2", "metadata": {}, + "outputs": [], "source": [ - "# Data cleaning" + "## 'half_trip_id' missing values (0.0030%)\n", + "data_18augsep = data_18augsep.dropna(subset=['half_trip_id'])" ] }, { - "cell_type": "markdown", - "id": "10d5ff7b-6794-43db-b6f0-33703bad9322", + "cell_type": "code", + "execution_count": 7, + "id": "93319c25-25ac-4dd9-b7f3-548e4bdf1eac", "metadata": {}, + "outputs": [], "source": [ - "## drop \"earliness\" variable" + "## 'time_point_id' & 'time_point_order' missing values (0.0234%)\n", + "## We can use others years data to interpolate, but the time_point_order might be different. \n", + "## So we just drop the missing vaue\n", + "data_18augsep = data_18augsep.dropna(subset=['time_point_id', 'time_point_order'])" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "5054a291-b26e-4c59-beab-8b552d5e4ab9", + "execution_count": 8, + "id": "c2bab8b9-ffde-4e4e-962d-ff83504dc7c7", + "metadata": {}, + "outputs": [], + "source": [ + "## 'actual' missing values (7.0866%)\n", + "data_18augsep = data_18augsep.dropna(subset=['actual'])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a27f0858-dec1-43d0-a5c3-ac13afc006d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num of Headway missing values, when standard_type is Headway: 166320\n" + ] + } + ], + "source": [ + "## 'headway' should not have missing value if the 'standard_type' is 'Headway', however, we found there are many reason will cause the headway missing.\n", + "## so we just keep the missing values first\n", + "num = data_18augsep.query(\"standard_type == 'Headway'\")['headway'].isna().sum()\n", + "print(f'Num of Headway missing values, when standard_type is Headway: {num}')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "afa9468a-7982-4a25-905e-291a5425ec8f", "metadata": {}, "outputs": [], "source": [ - "if \"earliness\" in data_18augsep.columns:\n", - " data_18augsep = data_18augsep.drop(\"earliness\", axis = 1)" + "data_18augsep = data_18augsep.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b5b4e0bd-0371-419a-9c3c-9767d75279ca", + "metadata": {}, + "source": [ + "## Missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8fe167ab-4b34-415f-bfa7-7f3f2c8fe536", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "service_date 0.000000\n", + "route_id 0.000000\n", + "direction 0.000000\n", + "half_trip_id 0.000000\n", + "stop_id 0.000000\n", + "time_point_id 0.000000\n", + "time_point_order 0.000000\n", + "point_type 0.000000\n", + "standard_type 0.000000\n", + "scheduled 0.000000\n", + "actual 0.000000\n", + "earliness 0.485503\n", + "scheduled_headway 0.514497\n", + "headway 0.549484\n", + "dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_18augsep.isna().sum() / data_18augsep.shape[0]" ] }, { @@ -257,50 +243,65 @@ "id": "2c210511-f4cb-40d1-9ad8-53f24b91f90a", "metadata": {}, "source": [ - "## reformat \"service_date\" variable" + "## reformat 'service_date' variable" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "id": "3e65577f-8972-4767-b40f-cb33417ef6f7", "metadata": {}, "outputs": [], "source": [ - "data_18augsep[\"service_date\"] = pd.to_datetime(data_18augsep[\"service_date\"])\n", "# keep Year-Month-Day\n", - "data_18augsep['service_date'] = data_18augsep['service_date'].apply(lambda x: x.strftime('%Y-%m-%d'))" + "data_18augsep['service_date'] = pd.to_datetime(data_18augsep['service_date'].str.slice(0, 10))" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "f32c20c8-66ba-41a4-b3d1-b9a6a115d574", + "execution_count": 18, + "id": "64d64011-f640-468e-91fa-67677b202894", + "metadata": {}, + "outputs": [], + "source": [ + "scheduled_is_next_day = (data_18augsep['actual'].notna() & data_18augsep['scheduled'].str.startswith('1900-01-02')).astype(int)\n", + "actual_is_next_day = (data_18augsep['actual'].notna() & data_18augsep['actual'].str.startswith('1900-01-02')).astype(int) # true -> if start_with 01-02, else start_with 01-01 or na" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8da471b4-52a8-40fb-8394-cb234463200a", "metadata": {}, "outputs": [], "source": [ "## reformat scheduled & actual variables\n", - "## also add a nextday boolean to indicate whether the scheduled and actual is on the next day\n", - "data_18augsep['scheduled'] = pd.to_datetime(data_18augsep['scheduled'])\n", - "data_18augsep['scheduled_nextday'] = data_18augsep['scheduled'].apply(lambda x: (x.strftime('%m-%d') if pd.notnull(x) else x) == '01-02')\n", - "data_18augsep['scheduled'] = data_18augsep['scheduled'].apply(lambda x: x.strftime('%H:%M:%S') if pd.notnull(x) else x)" + "data_18augsep['scheduled'] = pd.to_datetime(data_18augsep['scheduled'], format = '%Y-%m-%dT%H:%M:%SZ')\n", + "data_18augsep['scheduled'] = data_18augsep['scheduled'].dt.strftime('%H:%M:%S')\n", + "data_18augsep['scheduled'] = pd.to_datetime(data_18augsep['scheduled'], format='%H:%M:%S').dt.time.astype(str)\n", + "data_18augsep['scheduled_datetime'] = pd.to_datetime(data_18augsep['service_date'].astype(str) + ' ' + data_18augsep['scheduled'])\n", + "timedelta_adjustment = pd.to_timedelta(scheduled_is_next_day, unit='d')\n", + "data_18augsep['scheduled_datetime'] += timedelta_adjustment" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "c4691668-4520-4b8e-8fd2-b56b84724aef", + "execution_count": 20, + "id": "a4b116e8-7621-4fec-9b32-06839525eb02", "metadata": {}, "outputs": [], "source": [ - "data_18augsep['actual'] = pd.to_datetime(data_18augsep['actual'])\n", - "data_18augsep['actual_nextday'] = data_18augsep['actual'].apply(lambda x: (x.strftime('%m-%d') if pd.notnull(x) else x ) == '01-02')\n", - "data_18augsep['actual'] = data_18augsep['actual'].apply(lambda x: x.strftime('%H:%M:%S') if pd.notnull(x) else x)" + "data_18augsep['actual'] = pd.to_datetime(data_18augsep['actual'], format = '%Y-%m-%dT%H:%M:%SZ')\n", + "data_18augsep['actual'] = data_18augsep['actual'].dt.strftime('%H:%M:%S')\n", + "data_18augsep['actual'] = pd.to_datetime(data_18augsep['actual'], format='%H:%M:%S').dt.time.astype(str)\n", + "data_18augsep['actual_datetime'] = pd.to_datetime(data_18augsep['service_date'].astype(str) + ' ' + data_18augsep['actual'])\n", + "timedelta_adjustment = pd.to_timedelta(actual_is_next_day, unit='d')\n", + "data_18augsep['actual_datetime'] += timedelta_adjustment" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "id": "f4c07fe2-36d3-4fe2-9dfc-c51f9c703c38", "metadata": {}, "outputs": [ @@ -338,8 +339,8 @@ " actual\n", " scheduled_headway\n", " headway\n", - " scheduled_nextday\n", - " actual_nextday\n", + " scheduled_datetime\n", + " actual_datetime\n", " \n", " \n", " \n", @@ -358,8 +359,8 @@ " 05:19:34\n", " NaN\n", " NaN\n", - " False\n", - " False\n", + " 2018-08-01 05:19:00\n", + " 2018-08-01 05:19:34\n", " \n", " \n", " 1\n", @@ -376,8 +377,8 @@ " 05:23:20\n", " NaN\n", " NaN\n", - " False\n", - " False\n", + " 2018-08-01 05:22:00\n", + " 2018-08-01 05:23:20\n", " \n", " \n", " 2\n", @@ -394,8 +395,8 @@ " 05:25:58\n", " NaN\n", " NaN\n", - " False\n", - " False\n", + " 2018-08-01 05:25:00\n", + " 2018-08-01 05:25:58\n", " \n", " \n", " 3\n", @@ -412,8 +413,8 @@ " 05:28:26\n", " NaN\n", " NaN\n", - " False\n", - " False\n", + " 2018-08-01 05:28:00\n", + " 2018-08-01 05:28:26\n", " \n", " \n", " 4\n", @@ -430,8 +431,8 @@ " 05:29:57\n", " 1200.0\n", " 1218.0\n", - " False\n", - " False\n", + " 2018-08-01 05:30:00\n", + " 2018-08-01 05:29:57\n", " \n", " \n", "\n", @@ -452,15 +453,15 @@ "3 7.0 Midpoint Schedule 05:28:00 05:28:26 \n", "4 1.0 Startpoint Headway 05:30:00 05:29:57 \n", "\n", - " scheduled_headway headway scheduled_nextday actual_nextday \n", - "0 NaN NaN False False \n", - "1 NaN NaN False False \n", - "2 NaN NaN False False \n", - "3 NaN NaN False False \n", - "4 1200.0 1218.0 False False " + " scheduled_headway headway scheduled_datetime actual_datetime \n", + "0 NaN NaN 2018-08-01 05:19:00 2018-08-01 05:19:34 \n", + "1 NaN NaN 2018-08-01 05:22:00 2018-08-01 05:23:20 \n", + "2 NaN NaN 2018-08-01 05:25:00 2018-08-01 05:25:58 \n", + "3 NaN NaN 2018-08-01 05:28:00 2018-08-01 05:28:26 \n", + "4 1200.0 1218.0 2018-08-01 05:30:00 2018-08-01 05:29:57 " ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -470,131 +471,153 @@ ] }, { - "cell_type": "markdown", - "id": "b5b4e0bd-0371-419a-9c3c-9767d75279ca", + "cell_type": "code", + "execution_count": 2, + "id": "5a51a825-bb53-4c0a-adde-3ebca77f59b5", "metadata": {}, + "outputs": [], "source": [ - "# Missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8fe167ab-4b34-415f-bfa7-7f3f2c8fe536", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "service_date 0.000000\n", - "route_id 0.000000\n", - "direction 0.000000\n", - "half_trip_id 0.000030\n", - "stop_id 0.000000\n", - "time_point_id 0.000234\n", - "time_point_order 0.000234\n", - "point_type 0.000000\n", - "standard_type 0.000000\n", - "scheduled 0.000000\n", - "actual 0.070866\n", - "scheduled_headway 0.506128\n", - "headway 0.581525\n", - "scheduled_nextday 0.000000\n", - "actual_nextday 0.000000\n", - "dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_18augsep.isna().sum() / data_18augsep.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "43c3b9d0-151a-4aaf-84b6-0d52952cb6c2", - "metadata": {}, - "outputs": [], - "source": [ - "## \"half_trip_id\" missing values (0.0030%)\n", - "data_18augsep = data_18augsep.dropna(subset=[\"half_trip_id\"])" + "# data cleaning pipeline\n", + "def clean_data(df):\n", + " if 'earliness' in df.columns:\n", + " df = df.drop('earliness', axis = 1)\n", + " df['service_date'] = pd.to_datetime(df['service_date'])\n", + " df['service_date'] = df['service_date'].apply(lambda x: x.strftime('%Y-%m-%d')) \n", + "\n", + " df['scheduled'] = pd.to_datetime(df['scheduled'])\n", + " df['scheduled_nextday'] = df['scheduled'].apply(lambda x: x.strftime('%m-%d') == '01-02')\n", + " df['scheduled'] = df['scheduled'].apply(lambda x: x.strftime('%H:%M:%S') if pd.notnull(x) else x)\n", + " df['actual'] = pd.to_datetime(df['actual'])\n", + " df['actual_nextday'] = df['actual'].apply(lambda x: x.strftime('%m-%d') == '01-02')\n", + " df['actual'] = df['actual'].apply(lambda x: x.strftime('%H:%M:%S') if pd.notnull(x) else x)\n", + "\n", + " df = df.dropna(subset=['half_trip_id', 'time_point_id', 'time_point_order', 'actual'])\n", + " return df" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "93319c25-25ac-4dd9-b7f3-548e4bdf1eac", + "execution_count": 24, + "id": "c88a9b57-7b98-4261-aa16-74652c981a06", "metadata": {}, "outputs": [], "source": [ - "## \"time_point_id\" & \"time_point_order\" missing values (0.0234%)\n", - "## We can use others years data to interpolate, but the time_point_order might be different. \n", - "## So we just drop the missing vaue\n", - "data_18augsep = data_18augsep.dropna(subset=[\"time_point_id\", \"time_point_order\"])" + "data_18augsep.to_csv('arrdep_18augsep_simplecleaned.csv', index=False)" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "c2bab8b9-ffde-4e4e-962d-ff83504dc7c7", + "execution_count": 4, + "id": "d52fbd6c-c56b-401a-8759-18fece1b15cd", "metadata": {}, "outputs": [], "source": [ - "## \"actual\" missing values (7.0866%)\n", - "data_18augsep = data_18augsep.dropna(subset=[\"actual\"])" + "data = pd.read_csv(\"./old_version//cleaned_2019-01-03_data.csv\")" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "ddb4fbb9-1e9c-483f-8e54-d78e6489df95", + "execution_count": 11, + "id": "4ea2ddbb-5337-49c6-856e-a7aaf1cb3d03", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
service_dateroute_iddirectionhalf_trip_idstop_idtime_point_idtime_point_orderpoint_typestandard_typescheduledactualscheduled_headwayheadwayscheduled_datetimeactual_datetimeavailable_bus_depart_time
\n", + "
" + ], "text/plain": [ - "np.int64(0)" + "Empty DataFrame\n", + "Columns: [service_date, route_id, direction, half_trip_id, stop_id, time_point_id, time_point_order, point_type, standard_type, scheduled, actual, scheduled_headway, headway, scheduled_datetime, actual_datetime, available_bus_depart_time]\n", + "Index: []" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "## based on data desc \"scheduled_headway is NA when the \"standard_type\" is Headway, so we do not need to do anything to this var\n", - "data_18augsep.query(\"standard_type == 'Headway'\")[\"scheduled_headway\"].isna().sum()" + "data.iloc[:10000 :].query(\"route_id == '111'\").sort_values([\"service_date\", \"route_id\", \"direction\", \"time_point_order\", \"stop_id\", \"scheduled\"]).to_csv(\"test2.csv\", index = False)" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "a27f0858-dec1-43d0-a5c3-ac13afc006d4", + "execution_count": 14, + "id": "36edd5ab-9faf-43b2-ba36-5a74fa3c5845", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num of Headway missing values, when standard_type is Headway: 166320\n" - ] - } - ], + "outputs": [], "source": [ - "## \"headway\" should not have missing value if the \"standard_type\" is \"Headway\", however, we found there are many reason will cause the headway missing.\n", - "## so we just keep the missing values first\n", - "num = data_18augsep.query(\"standard_type == 'Headway'\")[\"headway\"].isna().sum()\n", - "print(f'Num of Headway missing values, when standard_type is Headway: {num}')" + "data.iloc[:10000, :].to_csv(\"test3.csv\", index = False)" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "afa9468a-7982-4a25-905e-291a5425ec8f", + "execution_count": 12, + "id": "23d49b63-2c4b-4023-9c1f-3262f6f38525", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"./arrdep_2019-Jan-Mar_simplecleaned.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "98039b0f-5336-4709-925a-9e53ae338897", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df.query(\"service_date == '2019-01-01' and route_id == '111' and time_point_order == 2 and direction == 'Inbound'\").drop(\n", + " ['service_date' , 'stop_id', 'point_type', 'standard_type', 'scheduled_headway', 'headway', 'scheduled_datetime', 'actual_datetime']\n", + ", axis = 1).sort_values(['time_point_id', 'scheduled']).to_csv('./presentation/wrong_departure_order.csv', index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c0529e57-8d82-4fc7-aff7-b9ab24711623", "metadata": {}, "outputs": [ { @@ -631,306 +654,499 @@ " actual\n", " scheduled_headway\n", " headway\n", - " scheduled_nextday\n", - " actual_nextday\n", + " scheduled_datetime\n", + " actual_datetime\n", + " available_bus_depart_time\n", " \n", " \n", " \n", " \n", - " 0\n", - " 2018-08-01\n", - " 01\n", + " 256\n", + " 2019-01-01\n", + " 111\n", " Inbound\n", - " 40121394.0\n", - " 75\n", - " mit\n", + " 41928289.0\n", + " 5547\n", + " woodc\n", + " 1.0\n", + " Startpoint\n", + " Headway\n", + " 08:42:00\n", + " 08:42:21\n", + " 960.0\n", + " 972.0\n", + " 2019-01-01 08:42:00\n", + " 2019-01-01 08:42:21\n", + " 2019-01-01 08:42:21\n", + " \n", + " \n", + " 567\n", + " 2019-01-01\n", + " 111\n", + " Inbound\n", + " 41928289.0\n", + " 5592\n", + " garfi\n", + " 2.0\n", + " Midpoint\n", + " Headway\n", + " 08:45:00\n", + " 08:45:17\n", + " 960.0\n", + " 995.0\n", + " 2019-01-01 08:45:00\n", + " 2019-01-01 08:45:17\n", + " 2019-01-01 08:45:17\n", + " \n", + " \n", + " 630\n", + " 2019-01-01\n", + " 111\n", + " Inbound\n", + " 41928289.0\n", + " 5595\n", + " smore\n", + " 3.0\n", + " Midpoint\n", + " Headway\n", + " 08:47:00\n", + " 08:47:19\n", + " 960.0\n", + " 984.0\n", + " 2019-01-01 08:47:00\n", + " 2019-01-01 08:47:19\n", + " 2019-01-01 08:47:19\n", + " \n", + " \n", + " 950\n", + " 2019-01-01\n", + " 111\n", + " Inbound\n", + " 41928289.0\n", + " 5601\n", + " wacry\n", " 4.0\n", " Midpoint\n", - " Schedule\n", - " 05:19:00\n", - " 05:19:34\n", - " NaN\n", - " NaN\n", - " False\n", - " False\n", + " Headway\n", + " 08:51:00\n", + " 08:51:20\n", + " 360.0\n", + " 406.0\n", + " 2019-01-01 08:51:00\n", + " 2019-01-01 08:51:20\n", + " 2019-01-01 08:51:20\n", " \n", " \n", - " 1\n", - " 2018-08-01\n", - " 01\n", + " 22\n", + " 2019-01-01\n", + " 111\n", " Inbound\n", - " 40121394.0\n", - " 79\n", - " hynes\n", + " 41928289.0\n", + " 5605\n", + " belsq\n", " 5.0\n", " Midpoint\n", - " Schedule\n", - " 05:22:00\n", - " 05:23:20\n", - " NaN\n", - " NaN\n", - " False\n", - " False\n", + " Headway\n", + " 08:55:00\n", + " 08:53:37\n", + " 360.0\n", + " 435.0\n", + " 2019-01-01 08:55:00\n", + " 2019-01-01 08:53:37\n", + " 2019-01-01 09:05:20\n", " \n", " \n", - " 2\n", - " 2018-08-01\n", - " 01\n", + " 144\n", + " 2019-01-01\n", + " 111\n", " Inbound\n", - " 40121394.0\n", - " 187\n", - " masta\n", + " 41928289.0\n", + " 5607\n", + " thche\n", " 6.0\n", " Midpoint\n", - " Schedule\n", - " 05:25:00\n", - " 05:25:58\n", - " NaN\n", - " NaN\n", - " False\n", - " False\n", + " Headway\n", + " 08:57:00\n", + " 08:55:16\n", + " 360.0\n", + " 432.0\n", + " 2019-01-01 08:57:00\n", + " 2019-01-01 08:55:16\n", + " 2019-01-01 09:07:05\n", " \n", " \n", - " 3\n", - " 2018-08-01\n", - " 01\n", + " 330\n", + " 2019-01-01\n", + " 111\n", " Inbound\n", - " 40121394.0\n", - " 59\n", - " Wasma\n", + " 41928289.0\n", + " 12001\n", + " tobin\n", " 7.0\n", " Midpoint\n", - " Schedule\n", - " 05:28:00\n", - " 05:28:26\n", - " NaN\n", - " NaN\n", - " False\n", - " False\n", + " Headway\n", + " 08:59:00\n", + " 08:56:42\n", + " 360.0\n", + " 438.0\n", + " 2019-01-01 08:59:00\n", + " 2019-01-01 08:56:42\n", + " 2019-01-01 09:08:20\n", " \n", " \n", - " 4\n", - " 2018-08-01\n", - " 01\n", + " 454\n", + " 2019-01-01\n", + " 111\n", " Inbound\n", - " 40121565.0\n", - " 110\n", - " hhgat\n", + " 41928289.0\n", + " 12003\n", + " ruthf\n", + " 8.0\n", + " Midpoint\n", + " Headway\n", + " 09:00:00\n", + " 08:59:59\n", + " 360.0\n", + " 498.0\n", + " 2019-01-01 09:00:00\n", + " 2019-01-01 08:59:59\n", + " 2019-01-01 09:10:24\n", + " \n", + " \n", + " 704\n", + " 2019-01-01\n", + " 111\n", + " Inbound\n", + " 41928289.0\n", + " 2829\n", + " cacom\n", + " 9.0\n", + " Midpoint\n", + " Headway\n", + " 09:02:00\n", + " 09:01:39\n", + " 360.0\n", + " 523.0\n", + " 2019-01-01 09:02:00\n", + " 2019-01-01 09:01:39\n", + " 2019-01-01 09:11:52\n", + " \n", + " \n", + " 828\n", + " 2019-01-01\n", + " 111\n", + " Inbound\n", + " 41928289.0\n", + " 8310\n", + " hayms\n", + " 10.0\n", + " Endpoint\n", + " Headway\n", + " 09:03:00\n", + " 09:01:45\n", + " 360.0\n", + " 477.0\n", + " 2019-01-01 09:03:00\n", + " 2019-01-01 09:01:45\n", + " 2019-01-01 09:11:56\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " service_date route_id direction half_trip_id stop_id time_point_id \\\n", + "256 2019-01-01 111 Inbound 41928289.0 5547 woodc \n", + "567 2019-01-01 111 Inbound 41928289.0 5592 garfi \n", + "630 2019-01-01 111 Inbound 41928289.0 5595 smore \n", + "950 2019-01-01 111 Inbound 41928289.0 5601 wacry \n", + "22 2019-01-01 111 Inbound 41928289.0 5605 belsq \n", + "144 2019-01-01 111 Inbound 41928289.0 5607 thche \n", + "330 2019-01-01 111 Inbound 41928289.0 12001 tobin \n", + "454 2019-01-01 111 Inbound 41928289.0 12003 ruthf \n", + "704 2019-01-01 111 Inbound 41928289.0 2829 cacom \n", + "828 2019-01-01 111 Inbound 41928289.0 8310 hayms \n", + "\n", + " time_point_order point_type standard_type scheduled actual \\\n", + "256 1.0 Startpoint Headway 08:42:00 08:42:21 \n", + "567 2.0 Midpoint Headway 08:45:00 08:45:17 \n", + "630 3.0 Midpoint Headway 08:47:00 08:47:19 \n", + "950 4.0 Midpoint Headway 08:51:00 08:51:20 \n", + "22 5.0 Midpoint Headway 08:55:00 08:53:37 \n", + "144 6.0 Midpoint Headway 08:57:00 08:55:16 \n", + "330 7.0 Midpoint Headway 08:59:00 08:56:42 \n", + "454 8.0 Midpoint Headway 09:00:00 08:59:59 \n", + "704 9.0 Midpoint Headway 09:02:00 09:01:39 \n", + "828 10.0 Endpoint Headway 09:03:00 09:01:45 \n", + "\n", + " scheduled_headway headway scheduled_datetime actual_datetime \\\n", + "256 960.0 972.0 2019-01-01 08:42:00 2019-01-01 08:42:21 \n", + "567 960.0 995.0 2019-01-01 08:45:00 2019-01-01 08:45:17 \n", + "630 960.0 984.0 2019-01-01 08:47:00 2019-01-01 08:47:19 \n", + "950 360.0 406.0 2019-01-01 08:51:00 2019-01-01 08:51:20 \n", + "22 360.0 435.0 2019-01-01 08:55:00 2019-01-01 08:53:37 \n", + "144 360.0 432.0 2019-01-01 08:57:00 2019-01-01 08:55:16 \n", + "330 360.0 438.0 2019-01-01 08:59:00 2019-01-01 08:56:42 \n", + "454 360.0 498.0 2019-01-01 09:00:00 2019-01-01 08:59:59 \n", + "704 360.0 523.0 2019-01-01 09:02:00 2019-01-01 09:01:39 \n", + "828 360.0 477.0 2019-01-01 09:03:00 2019-01-01 09:01:45 \n", + "\n", + " available_bus_depart_time \n", + "256 2019-01-01 08:42:21 \n", + "567 2019-01-01 08:45:17 \n", + "630 2019-01-01 08:47:19 \n", + "950 2019-01-01 08:51:20 \n", + "22 2019-01-01 09:05:20 \n", + "144 2019-01-01 09:07:05 \n", + "330 2019-01-01 09:08:20 \n", + "454 2019-01-01 09:10:24 \n", + "704 2019-01-01 09:11:52 \n", + "828 2019-01-01 09:11:56 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.query(\"half_trip_id == 41928289.0\").sort_values(\"time_point_order\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3d23634d-bb24-43fa-9379-5bcad3e851c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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service_dateroute_iddirectionhalf_trip_idstop_idtime_point_idtime_point_orderpoint_typestandard_typescheduledactualscheduled_headwayheadwayscheduled_datetimeactual_datetimeavailable_bus_depart_time
9512019-01-01111Inbound41928261.05601wacry1.0StartpointHeadway05:30:0005:29:571200.01218.0FalseFalse09:01:0009:01:17600.0597.02019-01-01 09:01:002019-01-01 09:01:172019-01-01 09:01:17
................................................232019-01-01111Inbound41928261.05605belsq2.0MidpointHeadway09:05:0009:05:20600.0703.02019-01-01 09:05:002019-01-01 09:05:202019-01-01 09:05:20
47537952018-09-30SL5Outbound40832424.015176mawor8.01452019-01-01111Inbound41928261.05607thche3.0MidpointHeadway00:54:0001:27:08900.02609.0TrueTrue09:07:0009:07:05600.0709.02019-01-01 09:07:002019-01-01 09:07:052019-01-01 09:07:05
47537962018-09-30SL5Outbound40832424.055Wasma9.03312019-01-01111Inbound41928261.012001tobin4.0MidpointHeadway00:55:0001:27:46900.02611.0TrueTrue09:09:0009:08:20600.0698.02019-01-01 09:09:002019-01-01 09:08:202019-01-01 09:16:16
47537972018-09-30SL5Outbound40832424.060Walen10.04552019-01-01111Inbound41928261.012003ruthf5.0MidpointHeadway00:56:0001:28:07900.02610.0TrueTrue09:10:0009:10:24600.0625.02019-01-01 09:10:002019-01-01 09:10:242019-01-01 09:10:24
47537982018-09-30SL5Outbound40832424.061Melwa11.07052019-01-01111Inbound41928261.02829cacom6.0MidpointHeadway00:57:0001:29:13900.02615.0TrueTrue09:12:0009:11:52600.0613.02019-01-01 09:12:002019-01-01 09:11:522019-01-01 09:19:19
47537992018-09-30SL5Outbound40832424.064Dudly12.08292019-01-01111Inbound41928261.08310hayms7.0EndpointHeadway00:58:0001:30:03900.02627.0TrueTrue09:13:0009:11:56600.0611.02019-01-01 09:13:002019-01-01 09:11:562019-01-01 09:19:47
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4753800 rows × 15 columns

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" ], "text/plain": [ - " service_date route_id direction half_trip_id stop_id time_point_id \\\n", - "0 2018-08-01 01 Inbound 40121394.0 75 mit \n", - "1 2018-08-01 01 Inbound 40121394.0 79 hynes \n", - "2 2018-08-01 01 Inbound 40121394.0 187 masta \n", - "3 2018-08-01 01 Inbound 40121394.0 59 Wasma \n", - "4 2018-08-01 01 Inbound 40121565.0 110 hhgat \n", - "... ... ... ... ... ... ... \n", - "4753795 2018-09-30 SL5 Outbound 40832424.0 15176 mawor \n", - "4753796 2018-09-30 SL5 Outbound 40832424.0 55 Wasma \n", - "4753797 2018-09-30 SL5 Outbound 40832424.0 60 Walen \n", - "4753798 2018-09-30 SL5 Outbound 40832424.0 61 Melwa \n", - "4753799 2018-09-30 SL5 Outbound 40832424.0 64 Dudly \n", + " service_date route_id direction half_trip_id stop_id time_point_id \\\n", + "951 2019-01-01 111 Inbound 41928261.0 5601 wacry \n", + "23 2019-01-01 111 Inbound 41928261.0 5605 belsq \n", + "145 2019-01-01 111 Inbound 41928261.0 5607 thche \n", + "331 2019-01-01 111 Inbound 41928261.0 12001 tobin \n", + "455 2019-01-01 111 Inbound 41928261.0 12003 ruthf \n", + "705 2019-01-01 111 Inbound 41928261.0 2829 cacom \n", + "829 2019-01-01 111 Inbound 41928261.0 8310 hayms \n", "\n", - " time_point_order point_type standard_type scheduled actual \\\n", - "0 4.0 Midpoint Schedule 05:19:00 05:19:34 \n", - "1 5.0 Midpoint Schedule 05:22:00 05:23:20 \n", - "2 6.0 Midpoint Schedule 05:25:00 05:25:58 \n", - "3 7.0 Midpoint Schedule 05:28:00 05:28:26 \n", - "4 1.0 Startpoint Headway 05:30:00 05:29:57 \n", - "... ... ... ... ... ... \n", - "4753795 8.0 Midpoint Headway 00:54:00 01:27:08 \n", - "4753796 9.0 Midpoint Headway 00:55:00 01:27:46 \n", - "4753797 10.0 Midpoint Headway 00:56:00 01:28:07 \n", - "4753798 11.0 Midpoint Headway 00:57:00 01:29:13 \n", - "4753799 12.0 Endpoint Headway 00:58:00 01:30:03 \n", + " time_point_order point_type standard_type scheduled actual \\\n", + "951 1.0 Startpoint Headway 09:01:00 09:01:17 \n", + "23 2.0 Midpoint Headway 09:05:00 09:05:20 \n", + "145 3.0 Midpoint Headway 09:07:00 09:07:05 \n", + "331 4.0 Midpoint Headway 09:09:00 09:08:20 \n", + "455 5.0 Midpoint Headway 09:10:00 09:10:24 \n", + "705 6.0 Midpoint Headway 09:12:00 09:11:52 \n", + "829 7.0 Endpoint Headway 09:13:00 09:11:56 \n", "\n", - " scheduled_headway headway scheduled_nextday actual_nextday \n", - "0 NaN NaN False False \n", - "1 NaN NaN False False \n", - "2 NaN NaN False False \n", - "3 NaN NaN False False \n", - "4 1200.0 1218.0 False False \n", - "... ... ... ... ... \n", - "4753795 900.0 2609.0 True True \n", - "4753796 900.0 2611.0 True True \n", - "4753797 900.0 2610.0 True True \n", - "4753798 900.0 2615.0 True True \n", - "4753799 900.0 2627.0 True True \n", + " scheduled_headway headway scheduled_datetime actual_datetime \\\n", + "951 600.0 597.0 2019-01-01 09:01:00 2019-01-01 09:01:17 \n", + "23 600.0 703.0 2019-01-01 09:05:00 2019-01-01 09:05:20 \n", + "145 600.0 709.0 2019-01-01 09:07:00 2019-01-01 09:07:05 \n", + "331 600.0 698.0 2019-01-01 09:09:00 2019-01-01 09:08:20 \n", + "455 600.0 625.0 2019-01-01 09:10:00 2019-01-01 09:10:24 \n", + "705 600.0 613.0 2019-01-01 09:12:00 2019-01-01 09:11:52 \n", + "829 600.0 611.0 2019-01-01 09:13:00 2019-01-01 09:11:56 \n", "\n", - "[4753800 rows x 15 columns]" + " available_bus_depart_time \n", + "951 2019-01-01 09:01:17 \n", + "23 2019-01-01 09:05:20 \n", + "145 2019-01-01 09:07:05 \n", + "331 2019-01-01 09:16:16 \n", + "455 2019-01-01 09:10:24 \n", + "705 2019-01-01 09:19:19 \n", + "829 2019-01-01 09:19:47 " ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data_18augsep = data_18augsep.reset_index(drop=True)\n", - "data_18augsep" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c88a9b57-7b98-4261-aa16-74652c981a06", - "metadata": {}, - "outputs": [], - "source": [ - "data_18augsep.to_csv(\"arrdep_18augsep_simplecleaned.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "5a51a825-bb53-4c0a-adde-3ebca77f59b5", - "metadata": {}, - "outputs": [], - "source": [ - "# data cleaning pipeline\n", - "def clean_data(df):\n", - " if 'earliness' in df.columns:\n", - " df = df.drop('earliness', axis = 1)\n", - " df['service_date'] = pd.to_datetime(df['service_date'])\n", - " df['service_date'] = df['service_date'].apply(lambda x: x.strftime('%Y-%m-%d')) \n", - "\n", - " df['scheduled'] = pd.to_datetime(df['scheduled'])\n", - " df['scheduled_nextday'] = df['scheduled'].apply(lambda x: x.strftime('%m-%d') == '01-02')\n", - " df['scheduled'] = df['scheduled'].apply(lambda x: x.strftime('%H:%M:%S') if pd.notnull(x) else x)\n", - " df['actual'] = pd.to_datetime(df['actual'])\n", - " df['actual_nextday'] = df['actual'].apply(lambda x: x.strftime('%m-%d') == '01-02')\n", - " df['actual'] = df['actual'].apply(lambda x: x.strftime('%H:%M:%S') if pd.notnull(x) else x)\n", - "\n", - " df = df.dropna(subset=['half_trip_id', 'time_point_id', 'time_point_order', 'actual'])\n", - " return df" + "data.query(\"half_trip_id == 41928261.0\").sort_values(\"time_point_order\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "944a899d-baec-4e29-9ccc-6ae158a34be9", + "id": "c6be4255-9154-4462-ada6-edefe5800557", "metadata": {}, "outputs": [], "source": [] diff --git a/utils/data_cleaning/README.md b/utils/data_cleaning/README.md new file mode 100644 index 0000000..38d8bf6 --- /dev/null +++ b/utils/data_cleaning/README.md @@ -0,0 +1,29 @@ +# Arrival and Departure Data Cleaning Pipeline + +This README provides an overview of the `arrival_depart_data_cleaning_pipeline.py` script, which is designed to process and clean bus arrival and departure data. + +## Overview + +The script performs the following main tasks: +1. Loads raw arrival and departure data +2. Cleans and reformats the data +3. Processes the data for specific route IDs +4. Calculates available bus departure times +5. Saves the cleaned data to a CSV file + +## Key Features + +- Handles missing values +- Reformats date and time variables +- Filters data for specific route IDs +- Calculates available bus departure times using parallel processing +- Utilizes multi-threading for improved performance + +## Usage + +To run the script, use the following command: +```python +python arrivale_depar_data_cleaning_pipeline.py \ + --data_path ../raw_data/MBTA_Bus_Arrival_Departure_Times_2022/MBTA-Bus-Arrival-Departure-Times_2022-08.csv \ + --output_path ../cleaned_data/cleaned_MBTA-Bus-Arrival-Departure-Times_2022-08.csv +``` diff --git a/utils/data_cleaning/arrival_depart_data_cleaning_pipeline.py b/utils/data_cleaning/arrival_depart_data_cleaning_pipeline.py new file mode 100644 index 0000000..13e931d --- /dev/null +++ b/utils/data_cleaning/arrival_depart_data_cleaning_pipeline.py @@ -0,0 +1,119 @@ +import argparse +import concurrent.futures +import itertools +import os +import warnings +import pandas as pd +from tqdm import tqdm +warnings.filterwarnings("ignore", category=pd.errors.SettingWithCopyWarning) + +FOLDER = os.path.join('..', 'dataset-documentation', 'raw_data') +FILTE_ROUTE_IDS = [ + '22', '29', '15', '45', '28', '44', + '42', '17', '23', '31', '26', '111', + '24', '33', '14' +] + + +def drop_missing_values(df): + print(df.isna().sum() / df.shape[0]) + df = df.dropna(subset=['half_trip_id', 'time_point_id', 'time_point_order', 'actual']) + df = df.reset_index(drop=True) + + return df + + +def reformat_date_vars(df): + df['service_date'] = pd.to_datetime(df['service_date'].str.slice(0, 10)) + scheduled_is_next_day = (df['actual'].notna() & df['scheduled'].str.startswith('1900-01-02')).astype(int) + actual_is_next_day = (df['actual'].notna() & df['actual'].str.startswith('1900-01-02')).astype(int) + + df['scheduled'] = pd.to_datetime(df['scheduled'], format = '%Y-%m-%d %H:%M:%S.000') + df['scheduled'] = df['scheduled'].dt.strftime('%H:%M:%S') + df['scheduled'] = pd.to_datetime(df['scheduled'], format='%H:%M:%S').dt.time.astype(str) + df['scheduled_datetime'] = pd.to_datetime(df['service_date'].astype(str) + ' ' + df['scheduled']) + timedelta_adjustment = pd.to_timedelta(scheduled_is_next_day, unit='d') + df['scheduled_datetime'] += timedelta_adjustment + + df['actual'] = pd.to_datetime(df['actual'], format = '%Y-%m-%d %H:%M:%S.000') + df['actual'] = df['actual'].dt.strftime('%H:%M:%S') + df['actual'] = pd.to_datetime(df['actual'], format='%H:%M:%S').dt.time.astype(str) + df['actual_datetime'] = pd.to_datetime(df['service_date'].astype(str) + ' ' + df['actual']) + timedelta_adjustment = pd.to_timedelta(actual_is_next_day, unit='d') + df['actual_datetime'] += timedelta_adjustment + + return df + + +def get_available_bus_departure_time(df): + act_depart_time = df.sort_values("actual_datetime").actual_datetime + available_bus_departure_time = [] + for i in range(df.shape[0]): + try: + available_bus_departure_time.append( + # search for the first actual departure time later than scheduled departure time + act_depart_time[act_depart_time >= df["scheduled_datetime"].iloc[i]].iloc[0] + ) + except: # when there is no bus actual departure time later than the scheduled departure time + available_bus_departure_time.append(None) + return available_bus_departure_time + + +def process_combination(args): + s_date, r_id, dire, s_id, df = args + temp = df.query("service_date == @s_date and route_id == @r_id and direction == @dire and stop_id == @s_id") + + if temp.empty: + return pd.DataFrame() + temp["available_bus_depart_time"] = get_available_bus_departure_time(temp) + return temp + + +def main(file_path, output_path): + data = pd.read_csv( + file_path + ) + + if 'earliness' in data.columns: + data = data.drop('earliness', axis = 1) + data = drop_missing_values(data) + # print("Drop missing values") + data = reformat_date_vars(data) + # print("Reformat date vars") + # data.to_csv(f"arrdep_{output_name}_simplecleaned.csv", index = False) + + data = data.query("route_id in @FILTER_ROUTE_IDS") + + service_dates = data.service_date.unique() + route_ids = data.route_id.unique() + directions = data.direction.unique() + stop_ids = data.stop_id.unique() + combinations = list(itertools.product(service_dates, route_ids, directions, stop_ids)) + + results = [] + with tqdm(total=len(combinations), desc="Processing Combinations") as pbar: + with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor: + future_results = executor.map( + process_combination, + [(s_date, r_id, dire, s_id, data) for s_date, r_id, dire, s_id in combinations] + ) + for result in future_results: + results.append(result) + pbar.update(1) + data = pd.concat(results, axis=0) + + data.to_csv(output_path, index=False) + print("Save cleaned data") + +if __name__ == "__main__": + + parser = argparse.ArgumentParser(description="Process bus arrival and departure data.") + parser.add_argument("--data_path", "-d", help="Path to the data to be cleaned") + parser.add_argument("--output_path", "-o", help="Path to save the cleaned data") + args = parser.parse_args() + + OUTPUT_FOLDER = args.output_path + assert os.path.exists(args.data_path), f"{args.data_path} does not exist" + assert args.output_path.endwith(".csv"), f"{args.output_path} is invalid" + main(args.data_path, args.output_path) + \ No newline at end of file