Modeling Supply and Demand in Public Transportation Systems
This work addresses service optimization for public transportation agencies, but it is incremental as it applies existing neural network methods to a new dataset.
The authors tackled the problem of analyzing efficiency and identifying service gaps in public bus systems by developing neural network-based supply and demand models, which were applied to the Harrisonburg City bus system and can generalize to other cities.
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.