STAD: Spatio Temporal Adjustment of Travel Time Estimations.

This repo contains code and links to data to reproduce experiments conducted in the following paper:

• Sofiane Abbar, Rade Stanonevic, Mohamed Mokbel. STAD: Spatio-Temporal Adjustment of Traffic Agnostic Travel Time Estimates. Under submission at IEEE MDM 2020.

Get Started:

• First download data from this link (porto_nyc_data), uncompress it into this folder.
• Create a python 2.7 virtual environment and install dependencies: pip install -r requirements.txt
• Run one of the codes (*.py) as follows: python stad_st.py porto

Data preparation for new dataset:

• First, get your trip data in the following format (see sample in data/nyc_1k.csv): TripStartTime,PickupLon,PickupLat,DropLon,DropLat,GT_Distance,GT_Duration,OsrmDistance,OsrmDuration
• Next, download shapefile of administrative zones from here. You can use QGIS to crop/edit the shapefile. Make sure coordinate system is in WGS84.
• Now, you can run: python trip_location_to_zone.py to infer the zone id of each trip origin/destination location. A new file (*_zoned.csv) will be created in data/.