Development of Freeway Traffic Incident Clearance Time Prediction Model by Accident Level

The Impact of Rapid Incident Detection on Freeway Accident Fatalities

Incident detection systems have the potential of reducing the time between the occurrence of accidents and the notification of emergency medical services, called the accident notification time. Reductions in this time, in turn, may affect the numbers of fatalities. A statistical analysis is conducted to determine the quantitative relationship between fatalities and the accident notification tim...

Application of Finite Mixture Models for Analyzing Freeway Incident Clearance Time

A number of approaches have been developed for analyzing incident clearance time data and investigating the effects of different explanatory variables on clearance time. Among these methods, hazard-based duration models (i.e., proportional hazard and accelerated failure time models) have been extensively used. The finite mixture model is an alternative approach in survival data analysis, and of...

Real-Time Crash Prediction Model for the Application to Crash Prevention in Freeway Traffic

The likelihood of a crash or crash potential is significantly affected by 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 which lead to crash occurrence, or “c...

A Compositional Stochastic Model for Real-Time Freeway Traffic Simulation

Traffic flow on freeways is a nonlinear, many-particle phenomenon, with complex interactions between vehicles. This paper presents a stochastic model of freeway traffic at a time scale and of a level of detail suitable for on-line estimation, routing and ramp metering control. The freeway is considered as a network of interconnected components, corresponding to one-way road links consisting of ...

Freeway Incident Likelihood Prediction and Response Decision - Making