Traffic flow characteristics that lead to crashes on urban freeways are examined. Since these characteristics are observed prior to crash occurrence, they are referred to as “crash precursors.” The objectives are ( a) to explore factors contributing to changes in crash rate for individual vehicles traveling over an urban freeway and ( b) to develop a probabilistic model relating significant crash precursors to changes in crash potential. The data used to examine crash precursors were extracted from 38 loop detector stations on a 10-km stretch of the Gardiner Expressway in Toronto for a 13-month period. An aggregate log-linear model was developed relating crash rates to the selected crash precursors observed upstream of the crash site. The results of this analysis suggest that the variation of speed and traffic density are statistically significant predictors of crash frequency after controlling for road geometry, weather, and time of day. With the model, crash potential can be established based on the precursors obtained from real-time traffic data.

No abstract

The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives. Therefore, the objectives of the present study were to ( a) suggest the rational methods by which the crash precursors included in the model can be determined on the basis of experimental results and ( b) test the performance of the modified crash prediction model. The study found that crash precursors can be determined in an objective manner, eliminating a characteristic of the previous model, in which the model results were dependent on analysts’ subjective categorization of crash precursors.

No abstract

A method is suggested for evaluating the effectiveness of variable speed limits in reducing freeway crash potential. The real-time crash prediction model that was developed in earlier studies was used to estimate crash potential for different control strategies of variable speed limits. To mimic realistic responses of drivers to changes in speed limits, a microscopic traffic simulation model was used. The simulation results indicate that total crash potential over the entire freeway segment could be significantly reduced under variable speed limit control with a minimal increase in travel time compared to the fixed speed limit. The methodology for assessing safety benefits of variable speed limits is illustrated, and findings from the experiment that used a simple freeway segment are presented.