E-Ride-Minimax

Keywords: AI in smart cities, climate change, decarbonizing, energy, game theory, Minimax, ride-sharing EV, smart economy, smart environment, smart mobility, traffic, transportation

Paper publication: Enriching Smart Cities by Optimizing Electric Vehicle Ride-Sharing through Game Theory

This MiniMax E-Ride sharing game allows two players to compete for passengers in a grid adapting the Minimax algorithm, treating EV ride-sharing companies as players. These players can use different strategies as described below. We hypothesize that one player choosing its next move via total passenger-travel distance (longer the distance, larger the profit); and another player via battery usage (ratio of total passenger-travel distance to vehicle-passenger distance: optimizing this ratio enables more travel without recharging.
One can manipulate the number of passengers, the change in passenger numbers (decreasing, increasing or stable), the grid size (default is 5x10 cells), the player strategy mode, the player starting location, the number of simulations and whether to enable MiniMax. You can also select if you wish to save (as a pickle file) and/or plot the Scores and Battery usage (as JPEG).

Google Colab script

Ready to use Google Colab script

Quick Start Examples

bash git clone https://github.com/darkoradakovic/E-Ride-Minimax cd E-Ride-Minimax pip install -r requirements.txt # install

Inference

bash python e_ride_competition.py --sim 100 --save --ptot # number of passengers (minimum ±50) --size # size of matrix (x by y) --change # chose passengers numbers change: 'falling', 'stable', 'increasing' --minimax # decide if minimax algorithm is turned on for player2 --strategy1 # choose from: '1' or '3'. (see below for overview) --strategy2 # choose from: '2', '4', '5' or '6' --last_loc1 # Player1 starting location --last_loc2 # Player2 starting location --sim # Simulation total --save # save results to pickle file

STRATEGIES OVERVIEW

• [Strategy 1] 'basic' [For player 1] find cell with most passengers, regardless their distance, chooses first passenger from list in this cell (old taxis without app strategy, by going to busy areas)
• [Strategy 2] 'low' [For player 2] find closest passenger lowest distance, disregarding profit
• [Strategy 3] 'passenger'[For player 1] find longest passenger start end (highest profit)
• [Strategy 4] 'distance_ratio' [For player 2] choosing score of passengers over battery
• [Strategy 5] 'battery' [For player 2] This method explores a passenger combination of the highest value and the third highest value (assuming that the second highest value will be chosen by the other player in the next turn), it decides whether to go first to the highest value or to the third highest values for the most optimal route.
• [Strategy 6] 'passenger2' [For player 2] always choosing highest score, then look at closest if more than one -> BEST RESULTS

Copyright (c) 2022 Montclair State University

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