Modeling Supply and Demand in Public Transportation Systems

TL;DR

This paper introduces neural network-based models to analyze supply and demand in public bus systems, helping identify service gaps and predictors, with potential for application in other cities.

Contribution

It presents two novel data-driven neural network models, one temporal and one spatial, for analyzing supply and demand in public transportation systems.

Findings

Models effectively identify service gaps.

Predictors include demographic and operational data.

Framework generalizes to other urban bus systems.

Abstract

We propose two neural network based and data-driven supply and demand models to analyze the efficiency, identify service gaps, and determine the significant predictors of demand, in the bus system for the Department of Public Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to James Madison University (JMU). The supply and demand models, one temporal and one spatial, take many variables into account, including the demographic data surrounding the bus stops, the metrics that the HDPT reports to the federal government, and the drastic change in population between when JMU is on or off session. These direct and data-driven models to quantify supply and demand and identify service gaps can generalize to other cities' bus systems.