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Peak-Load Pricing and Demand Management for Ridesourcing Platforms

This repository contains material for analyzing a pricing mechanism that maximizes platform revenue while staggering peaks in the load process, where the load process describes the predicted demand-supply mismatch. The pricing mechanism is applied to ridesourcing data from Lyft operations in NYC. A csv file with the cleaned data is available in this repository.

Overview

  • network.py: main script for time-dependent implementation of the proposed mechanism and for maintaining the defined stochastic and deterministic processes across time
  • odpair.py: a class that represents functions needed per origin-destination pair. For previously observed and predicted (future) rides between the o-d pair, we evaluate the number of starts or ends that are anticipated within the upcoming time horizon
  • region.py: a class that aggregates info. across o-d pairs and implements the proposed convex optimization program using cvxpy
  • utils.py: contains utility functions for integrating/evaluating empirical distributions, computing the arrival rate maximum likelihood estimator, and processing the data.

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