This project aims to find the optimal route for Electric vehicle drivers based on the customized preferences, inclduing time, safety and energy consumption. The developed package has three main components to achieve this objective, including database and API, frontend and backend. The database and API was developed based on an open source project postgrest-starter-kit. The frontend is a react application hosted by node.js. It was developed based on the base project provided by facebook. The backend is a Flask applicaiton which also provides an API to provide the data processing and analysis support for the project. It would fetch the data from the database, generate the results and then visualize the results on the frontend.
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├── postgrest-starter-kit # PostgreSQL and postgrest REST API
│ └── db # Database
│ └── src # Schema and data definition
│ ├── api # Api setting for endpoints
│ ├── data # schema setting for tables
│ ├── libs #
│ ├── authorization # authorization setting for different roles
│ ├── sample_data # data for development version
│ └── init.sql # initial database based on the settings
├── frontend # React application hosted by node.js
│ ├── package-lock.json #
│ ├── package.json # Application settings
│ ├── public # Components face to public
│ ├── Dockerfile # Docker file for starting the frontend service
│ └── src # Code for component define and customization
├── backend # Flask backend
│ ├── app.py # API end point setting
│ ├── requirements.txt # Libraries required for Flask API
│ ├── demo # Save demo file
│ ├── testData # Used data in backend
│ ├── optimal_route_generate.py # Build in function to generate route plan
│ └── Dockerfile
├── docker-compose.yml # Define Docker services
└── .env # Project environment setting
- Docker
- [Python] (Install the packages in requirements.txt)
Download thos repo to the local drive and navigate to the root directory of the repo. Then,
docker-compose up -d cd backend
python app.pydocker-compose downThe applicaiton will become avaiable at :
http://localhost/Call backend to get the shortest and optimal route based on the user's input, including origin, destination, prefered factors and car model type.
Try simply request from here:
curl --header "Content-Type: application/json" \
--request POST \
--data '{
"O": {"lat": "33.767466", "lon": "-84.414590"},
"D": {"lat": "33.769994", "lon": "-84.414494"},
"timePrefer":5,
"energyPrefer":5,
"crimePrefer":5,
"carModel":"Tesla Model S"
}' \
http://localhost:5000 Sample return should be as follows:
{'Shortest':{
'geo': {"type": "FeatureCollection","features": [
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.41459, 33.767466], [-84.414522, 33.768145]]}, "properties": {"id": "way/9238877", "Distance(mile)": 0.1871916975276935, "Crime_count": 0.0, "Energy": 1.5370200432008292, "Time(minutes)": 0.5620446426629898}}},
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.414512, 33.768325], [-84.414494, 33.769994]]}, "properties": {"id": "way/337901434", "Distance(mile)": 0.11199452543000214, "Crime_count": 0.0, "Energy": 0.9195804759941976, "Time(minutes)": 0.1344054983556023}}
]},
'attr':{
'Time': '20 mins',
'Distance': '50 miles'
}
},
'Optimal':{
'geo':{"type": "FeatureCollection","features": [
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.41459, 33.767466], [-84.414522, 33.768145]]}, "properties": {"id": "way/9269255", "Distance(mile)": 0.05860383368133388, "Crime_count": 0.0, "Energy": 0.5857997347233885, "Time(minutes)": 0.08789725749344414}},
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.414512, 33.768325], [-84.414494, 33.769994]]}, "properties": {"id": "way/303454539", "Distance(mile)": 0.0766809582980148, "Crime_count": 2.0, "Energy": 0.7664973809319207, "Time(minutes)": 0.1150103246319098}}
]},
'attr':{
'Time': '20 mins',
'Distance': '50 miles'
}
}
}
Call backend to get the demo with details, including origin, destination, prefered factors, car model type and corresponding shortest and optimal route.
Try simply request from here:
curl --header "Content-Type: application/json" \
--request POST \
--data '{"demo1":0,"demo2":1}' \
http://localhost:5000 Sample return should be as follows:
{ "O": {"lat": "33.788279", "lon": "-84.374004"},
"D": {"lat": "33.788279", "lon": "-84.374004"},
"timePrefer":5,
"energyPrefer":5,
"crimePrefer":5,
"carModel":"Tesla Model S",
'Shortest':{
'geo': {"type": "FeatureCollection","features": [
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.41459, 33.767466], [-84.414522, 33.768145]]}, "properties": {"id": "way/9238877", "Distance(mile)": 0.1871916975276935, "Crime_count": 0.0, "Energy": 1.5370200432008292, "Time(minutes)": 0.5620446426629898}},
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.414512, 33.768325], [-84.414494, 33.769994]]}, "properties": {"id": "way/169925547", "Distance(mile)": 0.04949479898993022, "Crime_count": 1.0, "Energy": 0.4947464747126869, "Time(minutes)": 0.07423502556266785}}
]},
'attr':{
'Time': '20 mins',
'Distance': '50 miles'
}
},
'Optimal':{
'geo':{"type": "FeatureCollection","features": [
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.41459, 33.767466], [-84.414522, 33.768145]]}, "properties": {"id": "way/303454543", "Distance(mile)": 0.1495918103341036, "Crime_count": 7.67, "Energy": 1.4953090490122831, "Time(minutes)": 0.22436603624508625}},
{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-84.414512, 33.768325], [-84.414494, 33.769994]]}, "properties": {"id": "way/9270244", "Distance(mile)": 0.07119290915653441, "Crime_count": 0.0, "Energy": 0.8881698964343703, "Time(minutes)": 0.12201695062836394}}
]},
'attr':{
'Time': '20 mins',
'Distance': '50 miles'
}
}
}
Call postgrest API to get the crime data
Try simply request from here:
http://localhost:3000/evs?select=id,streetid,crime,distance Sample return should be as follows:
[{"id":1,"streetid":"way/101797829","crime":1,"distance":0.000191817},
{"id":2,"streetid":"way/101797968","crime":10.6,"distance":0.002257247},
{"id":3,"streetid":"way/107533616","crime":2,"distance":0.000766886},
{"id":4,"streetid":"way/107533620","crime":4,"distance":0.000452658},
{"id":5,"streetid":"way/111663207","crime":176.22,"distance":0.00042042},
{"id":6,"streetid":"way/111877312","crime":1,"distance":0.000192981}, ]Call postgrest API to get the list of the car model types
Try simply request from here:
http://localhost:3000/evtypes?select=id,evtype Sample return should be as follows:
[{"id":1,"evtype":"Volkswagen ID.3 Pure"},
{"id":2,"evtype":"Volkswagen ID.4 1st"},
{"id":3,"evtype":"Lucid Air "},
{"id":4,"evtype":"Skoda Enyaq iV 50"},
{"id":5,"evtype":"Tesla Model 3 Long Range Dual Motor"},
{"id":6,"evtype":"Honda e "}, ]