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Taxicab crashes modeling with informative spatial autocorrelation.

Qingyu Ma1, Hong Yang1, Kun Xie2

  • 1Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, VA 23529, United States.

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|July 28, 2019
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Summary

This study developed advanced models to analyze taxi-involved crashes, using taxi trip data to improve spatial analysis. The findings reveal key factors influencing taxi safety in urban environments.

Keywords:
Bayesian estimationConditional autoregressive modelSpatial autocorrelationSpatial weightTaxi crashesTaxi trips

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Area of Science:

  • Urban transportation safety
  • Traffic accident analysis
  • Spatial statistics

Background:

  • Taxi safety is crucial in urban transport systems, with taxis facing higher crash risks due to extensive exposure in complex traffic.
  • Existing research has not fully explored the multifaceted factors contributing to taxi-involved crashes.

Purpose of the Study:

  • To develop and evaluate crash frequency models for analyzing taxi-involved accidents.
  • To investigate spatial autocorrelations and introduce novel spatial weight matrices using taxi trip data.

Main Methods:

  • Development of Poisson conditional autoregressive (Poisson-CAR) models for taxi-involved crashes.
  • Construction of a spatial weight matrix utilizing massive taxi trip data, moving beyond traditional distance-based methods.
  • Testing models with 2016 Washington D.C. taxi trip data.

Main Results:

  • Identified key explanatory factors for taxi crashes: road density, taxi activity, bus stop proximity, and land use.
  • Demonstrated superior performance of Poisson-CAR models with taxi trip-based weights over non-spatial and distance-based models.
  • Confirmed effective accounting for spatial autocorrelation in residuals through Moran's I tests.

Conclusions:

  • Informative spatial weight matrices derived from taxi trip data significantly enhance traffic safety models.
  • The proposed Poisson-CAR models offer a more accurate approach to understanding and mitigating taxi-involved crashes.
  • Future traffic safety studies should consider incorporating rich spatial data for improved analytical accuracy.