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March 3, 2026Transportation research procedia0 citationsOpen Access

Data-driven analysis of rideshare spatial travel patterns and ride metrics

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HWHao WangThe University of Texas at ArlingtonDADeema AlmaskatiThe University of Texas at ArlingtonSKSharareh KermanshachiPennsylvania State University

Key Points

  • Ride demand is route-specific and varies by time of day and day of the week, highlighting peak commuter patterns.
  • In-depth analysis conducted over two years focuses on origin-destination patterns and various ride metrics.
  • Certain routes show increased demand during peak hours, indicating important trends in commuter behavior.
  • Findings are relevant for urban planners and policymakers aiming to enhance transportation equity in underserved areas.

Abstract

Rideshare services offer an alternative transportation mode that has the potential to minimize transportation inequity, specifically in areas with limited transportation coverage. Previous studies have explored the spatial distribution of rideshare demand in high density areas with fixed-route public transportation; however, they lack comprehensive information about spatial variations in rideshare travel patterns in suburban areas without extensive public transit access. Thus, this study provides an in-depth analysis of spatial rideshare demand in Arlington, Texas over a two-year period through the assessment of origin-destination patterns and various ride metrics. The results revealed that ride demand is route specific and dependent on time of day and day of the week. Certain routes were also indicative of commuter patterns, demonstrating increased demand during peak hours. Other trip characteristics, such as wheelchair accessible vehicle requests, did not appear to be impacted by spatial variability. In addition to rideshare service providers, the findings of this study may also benefit urban planners and policymakers who can utilize these insights to improve transportation equity.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69a75bebc6e9836116a241dfhttps://doi.org/10.1016/j.trpro.2025.11.044
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