Modeling Supply and Demand in Public Transportation Systems

Miranda Bihler, Hala Nelson, Erin Okey, Noe Reyes Rivas, John Webb +1 more
9/12/2023
cs.LGstat.APstat.ML

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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.

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Cite this paper

@article{bihler2023modeling,
  title  = {Modeling Supply and Demand in Public Transportation Systems},
  author = {Miranda Bihler and Hala Nelson and Erin Okey and Noe Reyes Rivas and John Webb and Anna White},
  year   = {2023},
  eprint = {2309.06299},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.06299v2}
}

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