Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Yujie Hu, Yu Zhang, Kyle S. Shelton

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.

Original languageEnglish (US)
Pages (from-to)431-441
Number of pages11
JournalTransportation Research Part C: Emerging Technologies
Volume95
DOIs
StatePublished - Oct 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018

Keywords

  • Colocation
  • Houston
  • Safety countermeasures
  • Spatial patterns
  • Traffic safety

Fingerprint

Dive into the research topics of 'Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach'. Together they form a unique fingerprint.

Cite this