Pricing and routing in electric mobility on demand: a stochastic optimization with decision-dependent data approach
Python code that implements a network flow model. Objective: maximize the profits of an AMoD operator.
Python: https://www.python.org/downloads/
cvxpy: https://www.cvxpy.org/install/
Data for the ride requests can be downloaded from the Manhattan Taxi and Limousine Commission website https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page, selecting 2022 >> March >> Yellow Taxi Trip Records (PARQUET).
The .parquet file must be saved in the same folder as the main file, under the name yellow_tripdata_2022-03.parquet.
Run the file mainNoElectric.py to consider the non-electric case.
The same folder should also contain:
- tripData.py: to extract a subsample of ride request data from the file yellow_tripdata_2022-03.parquet
- taxiZones.cvs: taxi zone lookup table