Reactive traffic management strategies such as incident detection are becoming less relevant with the advancement of mobile phone usage. Freeway management in the 21st century needs to shift focus toward proactive strategies that include anticipating incidents such as crashes. A simple approach to identify freeway locations with high probability of crashes through real-time traffic surveillance data is presented here. The crash and loop detector data for the study was collected from 36-mile corridor of Interstate-4 in Orlando, Florida. The analysis is based on simple (one covariate) logistic regression models developed under a matched study design. Individual traffic parameters obtained one at a time from series of loop detectors have been examined as potential covariates to these models. Hazard ratio for each individual covariate is the output from the models. Based on the hazard ratio and its statistical significance it was found that the log of coefficient of temporal variation in speed, standard deviation of volume, and average occupancy expressed as percentage are the parameters that are most critically associated with potential occurrence of multivehicle crashes. The univariate logistic regression models were validated based on their classification performance on an independent set of crash data. Using the relative location of loop detectors measuring these parameters with respect to the crash location contour maps depicting spatial-temporal distribution of crash risk was generated. Using the model outputs, a generic strategy to assess crash risk in real-time is also proposed. With this strategy one can identify the segment of freeway having high potential for crash occurrence within next 15-20 minutes. The crash mitigation and law enforcement set up can be prepared for dispatch to such locations based on the real-time assessment of crash potential.


Civil and Environmental Engineering



URL: https://digitalcommons.calpoly.edu/cenv_fac/226