Skip to content

Openstreetmap routing engine and route optimization in Go .

License

Notifications You must be signed in to change notification settings

lintang-b-s/navigatorX

Repository files navigation

navigatorx

Simple Openstreetmap routing engine in go. This project uses Contraction Hierarchies to speed up shortest path queries by preprocessing the road network graph (adding many shortcut edges) and Bidirectional Dijsktra for shortest path queries. H3 is used as a nearest neighbor query. For route optimization, currently only TSP is supported, in the future VRP and its variants will be added. Currently it is still not possible to customize graph based on traffic data,but customization features will be added in the future.

Quick Start

Docker

1. docker compose up  --build
2. wait for preprocessing contraction hierarchies to complete (about 3 minutes) [you can check it with 'docker logs navigatorx -f', wait until the text 'Contraction Hierarchies + Bidirectional Dijkstra Ready!!' appears  ]

Local (Preprocess + Server)

1. download the jogja & solo openstreetmap pbf file at: https://drive.google.com/file/d/1pEHN8wwUbB5XpuYMZm141fXQ_ZsIf4CO/view?usp=sharing
Note: or you can also use another openstreetmap file with the osm.pbf format (https://download.geofabrik.de/)
2.  put the download results into the root directory of this project
3.  go mod tidy &&  mkdir bin
4. CGO_ENABLED=1  go build -o ./bin/navigatorx ./cmd/auto
5. ./bin/navigatorx
(Minimum RAM 2.0 GB for the above openstreetmap data)
note: or you can also do it with "make run"
5.  wait for preprocessing contraction hierarchies to complete (about 3 minutes)
Note: if error "resource temporary unavailable" -> just delete navigatorXDB directory and restart the steps above

Only Preprocessing

1. download the jogja & solo openstreetmap pbf file at: https://drive.google.com/file/d/1pEHN8wwUbB5XpuYMZm141fXQ_ZsIf4CO/view?usp=sharing
Note: or you can also use another openstreetmap file with the osm.pbf format (https://download.geofabrik.de/)
2.  put the download results into the root directory of this project
3.  go mod tidy &&  mkdir bin
4. CGO_ENABLED=1  go build -o ./bin/navigatorx-preprocessing ./cmd/preprocessing
5. ./bin/navigatorx-preprocessing
Note: to replace the openstreetmap file, see the instructions below
(Minimum RAM 2.0 GB for the above openstreetmap data)
5.  wait for preprocessing contraction hierarchies to complete (about 3 minutes)
Note: if error "resource temporary unavailable" -> just delete navigatorXDB directory and restart the steps above

Only Server

Make sure you have done the preprocessing stage above!

1. CGO_ENABLED=1  go build -o ./bin/navigatorx-engine ./cmd/engine
2. ./bin/navigatorx-engine
(Minimum free RAM 1 GB for the above data)

Change Map Data

1. provide openstreetmap filename flag when running the program
 ./bin/navigatorx -f jakarta.osm.pbf
2. for docker setup, change the Filename args "MAP_FILE" and google drive file id "DRIVE_FILE_ID" in docker-compose
 args:
        MAP_FILE: solo_jogja
        DRIVE_FILE_ID: 1pEHN8wwUbB5XpuYMZm141fXQ_ZsIf4CO

Shortest Path Between 2 Place in Openstreetmap

1. wait until there is a log "server started at :5000". If you want the query to be >10x faster, wait for the Contraction Hierarchies preprocessing to complete.
2. request ke shortest path (source=surakarta , destination=pantai parangtritis) [untuk data openstreetmap pada step setup]
curl --location 'http://localhost:5000/api/navigations/shortest-path' \
--header 'Content-Type: application/json' \
--data '{
  "src_lat":  -7.550263588614922,
    "src_lon":     110.78206617571915,
    "dst_lat": -8.024167150460844,
    "dst_lon":   110.32986653162467
}'

Note: Source & Destination Coordinates must be around Yogyakarta Province/Surakarta City/Klaten if using OpenStreetMap data in the setup step
5. Copy the polyline string path of the response endpoint result to https://valhalla.github.io/demos/polyline . Check Unsescape '\'. The shortest route will appear on the map. :)

Map Matching Hidden Markov Model Decoding

find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs (gps).

1. wait until there is a log "server started at :5000". If you want the query to be >10x faster, wait for the Contraction Hierarchies preprocessing to complete.
2. request to the server with route list of gps coordinate data (or fake route coordinate data)
curl --location 'http://localhost:5000/api/navigations/map-matching' \
--header 'Content-Type: application/json' -d @gps_hmm_map_matching.json

Note:  GPS Coordinates must be around the Yogyakarta province/Surakarta city/Klaten if using OpenStreetMap data in the setup step
3. Copy the polyline string path of the response endpoint result to https://valhalla.github.io/demos/polyline . Centang Unsescape '\'. The map matching results in the form of a list of road network node coordinates will appear on the map. :)

Traveling Salesman Problem Using Simulated Annealing or Ant-Colony Optimization

What is the shortest (suboptimal) route to visit UGM, UNY, UPNV Jogja, UII Jogja, IAIN Surakarta, UNS, UMS, and ISI Surakarta campuses exactly once and return to the original campus location?

1. Wait until Contraction Hierarchies preprocessing is complete
2. request query traveling salesman problem
curl --location 'http://localhost:5000/api/navigations/tsp' \
--header 'Content-Type: application/json' \
--data '{
    "cities_coord": [
        {
            "lat": -7.773700556142326,
            "lon": 110.37927594982729
        },
        {
            "lat": -7.687798280189743,
            "lon": 110.41397147030537
        },
        {
            "lat": -7.773714842796234,
            "lon": 110.38625612460329
        },
        {
            "lat": -7.7620859704046135,
            "lon": 110.40928883503045
        },
        {
            "lat": -7.559256385020671,
            "lon":  110.85624887436603
        },
        {
            "lat": -7.558529640984029,
            "lon": 110.73442218529993
        },
        {
            "lat": -7.5579561088085665,
            "lon":  110.85233572375333
        },
        {
            "lat":  -7.557649260722883,
            "lon": 110.77068956586514
        }
    ]
}'

or (use ant-colony optimization)

curl --location 'http://localhost:5000/api/navigations/tsp_aco' \
--header 'Content-Type: application/json' \
--data '{
    "cities_coord": [
        {
            "lat": -7.773700556142326,
            "lon": 110.37927594982729
        },
        {
            "lat": -7.687798280189743,
            "lon": 110.41397147030537
        },
        {
            "lat": -7.773714842796234,
            "lon": 110.38625612460329
        },
        {
            "lat": -7.7620859704046135,
            "lon": 110.40928883503045
        },
        {
            "lat": -7.559256385020671,
            "lon":  110.85624887436603
        },
        {
            "lat": -7.558529640984029,
            "lon": 110.73442218529993
        },
        {
            "lat": -7.5579561088085665,
            "lon":  110.85233572375333
        },
        {
            "lat":  -7.557649260722883,
            "lon": 110.77068956586514
        }
    ]
}'
Note:  "cities_coord" must be a place around the province of Yogyakarta/Surakarta/Klaten if using OpenStreetMap data in the setup step
3.  Copy the polyline string path of the response endpoint result to https://valhalla.github.io/demos/polyline . Check Unsescape '\'. The shortest (suboptimal) TSP route will be displayed on the map. :)

Rider-Driver Matchmaking Using Hungarian Algorithm

1. Wait until Contraction Hierarchies preprocessing is complete
2. request to matchmaking api
curl --location 'http://localhost:5000/api/navigations/matching' \
--header 'Content-Type: application/json' \
--data '{
    "rider_lat_lon": [
        {
            "username": "rider1",
            "coord": {
                "lat": -7.767684016779731,
                "lon":  110.37649557875707
            }
        },
        {
            "username": "rider2",
            "coord": {
                "lat": -7.770534977253453,
                "lon":   110.38156022914536
            }
        },
        {
        "username":  "rider3",
        "coord": {
                "lat": -7.758553228167311,
                "lon":  110.39946726179075
            }
        },
        {
            "username": "rider4",
            "coord": {
                "lat": -7.801196956754633,
                "lon":  110.36672004587915
            }
        },
        {
            "username": "rider5",
            "coord": {
                "lat": -7.687706141646555,
                "lon": 110.41843469922163
            }
        },
       {
        "username": "rider6",
        "coord": {
                "lat": -7.556714132377571,
                "lon":  110.80520610633097
            }
       },
       {
        "username": "ridersolo7",
       "coord": {
            "lat": -7.561717618835495,
            "lon": 110.80992968611694
        }
       },
       {
         "username":  "rider8",
        "coord": {
                "lat": -7.5603675519267055,
                "lon": 110.76770911286172
            }
         },
         {
         "username":"rider9",
         "coord": {
            "lat": -7.740690926169796,
            "lon": 110.37411440130444
         }
        },
        {
            "username": "rider10",
            "coord": {
                "lat": -7.559722706161821,
                "lon": 110.85641658202763
            }
        } ,
        {
            "username":"rider11",
            "coord": {
                "lat": -7.516093248544381,
                "lon": 110.75452445432569
            }
        },
        {
            "username": "riderSolo",
            "coord": {
                "lat": -7.554605287475889,
                "lon": 110.82704286671313
            }
        },
        {
            "username": "riderSolo2",
            "coord": {
                "lat": -7.572505106627924,
                "lon": 110.84027742738219
            }
        }
    ],
     "driver_lat_lon": [
        {
            "username":    "driversolo3",
            "coord": {
                "lat": -7.573553087300021,
                "lon": 110.82073100556183
            }
        },
        {
            "username": "driver2",
            "coord": {
                "lat": -7.571130061786068,
            "lon":  110.80391906353825
            }
        },
        {
            "username":"driver3",
            "coord":{
                "lat": -7.782514997952533,
                "lon": 110.36659498380173
            }
        },
        {
            "username":  "driver4",
            "coord": {
                "lat": -7.781478644687624,
                "lon":  110.37267965620099
            }
        },
        {
            "username": "driver5",
            "coord": {
                "lat": -7.772515329567074,
                "lon": 110.37239634189628
            }
        },
        {
            "username": "driver6",
            "coord": {
                "lat": -7.755970087727186,
                "lon": 110.37634415191656
            }
        },
        {
            "username":   "driver7",
            "coord": {
                "lat": -7.764707027042284,
                "lon":  110.39259158173417
            }
        },
        {
            "username":  "driver8",
            "coord": {
                "lat": -7.565565153230303,
                "lon": 110.8079927641968
            }
        },
        {
            "username":  "driver9",
            "coord": {
            "lat": -7.751209845539939,
             "lon": 110.41778895149984
            }
        },
        {
            "username": "driverSolo",
            "coord": {
                "lat": -7.565093613983397,
                "lon": 110.81882158435778
            }
        },
        {
            "username": "driverSolo2",
            "coord": {
                "lat": -7.572505106632021,
                "lon":110.83008101439083
            }
        }


    ]
}'

3. The response is the most optimal rider-driver pairs based on proximity (estimated arrival time from driver to rider). Solved using the Hungarian algorithm.

Many to Many Shortest Path Query

1. wait until preprocessing contraction hierarchies is complete
2. request  query many to many
curl --location 'http://localhost:5000/api/navigations/many-to-many' \
--header 'Content-Type: application/json' \
--data '{
    "sources": [{
        "lat": -7.550248257898637,
        "lon": 110.78217903249168
    },
    {
        "lat": -7.560347382387681,
        "lon": 110.78879587509478
    },
    {
        "lat": -7.5623445763181945,
        "lon": 110.81010426983109
    }
    ],
    "targets": [{
        "lat": -7.553672205152498,
        "lon": 110.79784256968716
    },
    {
        "lat": -7.564559782091322,
        "lon":  110.80455609811008
    },
    {
        "lat": -7.570135257838102,
        "lon": 110.82292649269334
    },
    {
        "lat": -7.598393719179397,
        "lon": 110.81555588473815
    }


    ]
}'

Note:  "sources" and "targets" must be around the province of Yogyakarta/Surakarta/Klaten if using OpenStreetMap data in the setup step
3.  Copy the polyline string path of the response endpoint result to https://valhalla.github.io/demos/polyline . Centang Unsescape '\'. Check Unsescape '\'. The shortest route of many to many query will be displayed on the map. :)

Shortest Path with alternative street

1. wait until there is a log "server started at :5000". If you want the query to be >10x faster, wait for the Contraction Hierarchies preprocessing to complete.
2. request query shortest path w/ alternative street
curl --location 'http://localhost:5000/api/navigations/shortest-path-alternative-street' \
--header 'Content-Type: application/json' \
--data '{
    "src_lat": -7.550261232598317,
    "src_lon":    110.78210790296636,
    "street_alternative_lat": -7.8409667827395815,
    "street_alternative_lon":   110.3472473375829,
      "dst_lat": -8.024431446370416,
    "dst_lon":   110.32971396395838
}'

Note:  "sources" and "targets" must be around the province of Yogyakarta/Surakarta/Klaten if using OpenStreetMap data in the setup step
3. Copy the polyline string path of the response endpoint result to https://valhalla.github.io/demos/polyline . Check Unsescape '\'. The shortest route will appear on the map. :)

Theory / Ref

-  R. Geisberger, P. Sanders, D. Schultes, and D. Delling, “Contraction Hierarchies: Faster and Simpler Hierarchical Routing in Road Networks,” in Experimental Algorithms, C. C. McGeoch, Ed., Berlin, Heidelberg: Springer, 2008, pp. 319–333. doi: 10.1007/978-3-540-68552-4_24.
-   “Hidden Markov Map Matching Through Noise and Sparseness - Microsoft Research.” Accessed: Oct. 24, 2024. [Online]. Available: https://www.microsoft.com/en-us/research/publication/hidden-markov-map-matching-noise-sparseness/
-  “Ant colony optimization | IEEE Journals & Magazine | IEEE Xplore.” Accessed: Dec. 08, 2024. [Online]. Available: https://ieeexplore-ieee-org.ezproxy.ugm.ac.id/document/4129846
-  https://en.wikipedia.org/wiki/Hungarian_algorithm
-  https://en.wikipedia.org/wiki/Simulated_annealing#:~:text=Simulated%20annealing%20(SA)%20is%20a,can%20find%20the%20global%20optimum.
-  https://jlazarsfeld.github.io/ch.150.project/sections/7-ch-overview/
-  https://www.uber.com/en-ID/blog/engineering-routing-engine/
-  https://h3geo.org/
-  https://www.uber.com/blog/mapping-accuracy-with-catchme/
-  http://theory.stanford.edu/~amitp/GameProgramming/ImplementationNotes.html

go build -gcflags "-m -l" *.go

Releases

No releases published

Packages

No packages published

Languages