Modeling Supply and Demand in Public Transportation Systems

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. Keywords-- transportation systems, bus systems, public transportation, direct ridership models, data driven models, mathematical modeling, neural networks, machine learning, supply models, demand models, machine learning, service gaps, social vulnerability, public transportation access, GIS data, data science, data quality.