Exploring Spatial and Temporal Patterns of Large-Scale Smartphone-Based Dangerous Driving Event Data

Di Yang1, Kun Xie2, Kaan Ozbay1, Hong Yang3

  • 1New York University
  • 2University of Canterbury
  • 3Old Dominion University

Details

11:30 - 11:45 | Mon 28 October | Crystal Room I | MoC-T5.3

Session: Special Session on Big Data and Emerging Technologies for Traffic Safety Improvement (I)

Category: Special Session

Abstract

Dangerous driving events data are widely used as surrogates to traffic crashes. Large-scale dangerous driving events data collected from smartphones are explored in this study. Clustering analysis is performed on dangerous driving events counted in spatial cells. Spatial and temporal patterns of the cluster distributions are then explored. Both the existence of spatial autocorrelation and the similarity of cluster distributions for different time periods are uncovered.