Weather, environment, and loop data conditions are promising indicators for real-time freeway incident prediction. The ability to predict the likelihood of selected incident types by using weather and environment data was examined. Loop detector data were analyzed for conditions useful for in-lane incident prediction. Nonnested and nested multinomial logit models were estimated with data from selected freeways in Austin, Texas. The estimation results revealed that factors such as visibility, time of day, and lighting condition are significant determinants of incident type, whereas 5-min average occupancy and coefficient of variation in speed are strong predictors of in-lane freeway accidents.


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    Title :

    Assessing Weather, Environment, and Loop Data for Real-Time Freeway Incident Prediction


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2006-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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