Traffic volume and collisions involving transit and nontransit vehicles

Accid Anal Prev. 1992 Oct;24(5):547-58. doi: 10.1016/0001-4575(92)90063-o.

Abstract

This study reports an analysis of collisions occurring between public transit vehicles operated by the San Francisco Municipal Railway System (Muni), the public transit agency for the City of San Francisco, and nontransit vehicles. The analysis, focusing on weekday collisions during 1987, demonstrated a strong association between hourly transit collisions rates and hourly traffic volume. The collision rate varied from 0.01 per 1,000 Muni vehicle-hours of operation during the interval 5 A.M. to 6 A.M., a time of very low traffic volume, to 0.93 (approximately 1 collision per 1,000 Muni vehicle-hours of operation) during the interval 5 P.M. to 6 P.M., a time of very high traffic volume. Using a power function to predict either the total number of collisions, or the rate of collisions per 1,000 Muni vehicle-hours, almost 90% of total variation was accounted for by traffic volume. A very similar pattern was found for collisions judged either avoidable or unavoidable. A peak in the collision rate between 2 A.M. and 3 A.M. could not be accounted for by traffic volume alone. That peak occurred in the one-hour interval following the 2 A.M. closing of bars in San Francisco, and was composed entirely of a sharp increase in unavoidable collisions. Increasing traffic volume appeared to operate through two mechanisms: (i) an increase in the number of opportunities for a collision, defined as a quantity proportional to the product of the number of Muni and non-Muni vehicles; (ii) an increase in the probability of a collision occurring between any given pair of vehicles.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Occupational / mortality
  • Accidents, Occupational / prevention & control
  • Accidents, Occupational / statistics & numerical data*
  • Accidents, Traffic / mortality
  • Accidents, Traffic / prevention & control
  • Accidents, Traffic / statistics & numerical data*
  • Automobile Driving / statistics & numerical data
  • Humans
  • Information Systems
  • Models, Statistical
  • Poisson Distribution
  • San Francisco
  • Transportation / statistics & numerical data*
  • United States