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...
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| Format: | Journal Article |
| Language: | English |
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12-09-2023
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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. |
|---|---|
| AbstractList | 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. |
| Author | Okey, Erin White, Anna Nelson, Hala Rivas, Noe Reyes Bihler, Miranda Webb, John |
| Author_xml | – sequence: 1 givenname: Miranda surname: Bihler fullname: Bihler, Miranda – sequence: 2 givenname: Hala surname: Nelson fullname: Nelson, Hala – sequence: 3 givenname: Erin surname: Okey fullname: Okey, Erin – sequence: 4 givenname: Noe Reyes surname: Rivas fullname: Rivas, Noe Reyes – sequence: 5 givenname: John surname: Webb fullname: Webb, John – sequence: 6 givenname: Anna surname: White fullname: White, Anna |
| BackLink | https://doi.org/10.48550/arXiv.2309.06299$$DView paper in arXiv |
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| Snippet | We propose two neural network based and data-driven supply and demand models
to analyze the efficiency, identify service gaps, and determine the significant... |
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| SubjectTerms | Computer Science - Learning Statistics - Applications Statistics - Machine Learning |
| Title | Modeling Supply and Demand in Public Transportation Systems |
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