Taxicab crashes modeling with informative spatial autocorrelation

Qingyu Ma, Hong Yang, Kun Xie, Zhenyu Wang, Xianbiao Hu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Maintaining taxi safety is one of the important goals of operating urban transportation systems. Taxicabs are often prone to higher crash risk due to their long-time exposure to the complicated and dynamic traffic environments in urban areas. Despite existing efforts in understanding the safety issues associated with these vehicles, there were still few attempts that have specifically examined the relationship between taxi-involved crashes and other multifaceted contributing factors. To this end, this paper aims to develop crash frequency models for analyzing taxi-involved crashes. In particular, the spatial autocorrelations between variables were explored and the Poisson conditional autoregressive (Poisson-CAR) models for taxi-involved crashes were proposed. Unlike previous safety studies that mainly consider distance as the key indicator of spatial correlation, the present paper introduced the use of massive taxi trip data for constructing a more informative spatial weight matrix. The developed models with the taxi trip-based weight matrix were tested by using the 2016 taxi trip data collected in Washington D.C. The modeling results highlight the key explanatory factors such as road density, taxi activity, number of bus stops, and land use. More importantly, it demonstrates that the proposed Poisson-CAR models with the taxi trip-based weight matrix outperformed both the non-spatial Poisson model and the Poisson-CAR models using conventional distance-based weight matrix. Moran's I tests further indicate that our proposed models have sufficiently accounted for the spatial autocorrelation of the residuals. Thus, it deserves to consider informative spatial weight matrices when applying spatial models in traffic safety studies.

Original languageEnglish (US)
Pages (from-to)297-307
Number of pages11
JournalAccident Analysis and Prevention
StatePublished - Oct 2019

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health
  • Safety, Risk, Reliability and Quality
  • Law
  • Human Factors and Ergonomics


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