No abstract

Reliable travel time prediction enables both road users and system controllers to be well informed about future conditions on roadways so that pretrip plans and traffic control strategies can be made to reduce travel time and relieve traffic congestion. The objective of this research was to use traffic and weather data from multiple data sources to develop an integrated model that could predict travel times under various weather conditions, especially severe weather conditions. Prediction models are compared, and their performance in case studies is investigated.

In light of the recently emphasized studies on risk evaluation of crashes, accident counts under specific transportation facilities are adopted to reflect the chance of crash occurrence. The current study introduces more comprehensive measure with the supplement information of accidental harmfulness into the expression of accident risks which are also named Accident Hazard Index (AHI) in the following context. Before the statistical analysis, datasets from various sources are integrated under a GIS platform and the corresponding procedures are presented as an illustrated example for similar analysis. Then, a quasi-Poisson regression model is suggested for analyses and the results show that the model is appropriate for dealing with overdispersed count data and several key explanatory variables were found to have significant impact on the estimation of AHI. In addition, the effect of weight on different severity levels of accidents is examined and the selection of the weight is also discussed.