Assessing crash risk considering vehicle interactions with trucks using point detector data.

Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions.

A method for identifying rear-end collision risks using inductive loop detectors.

An innovative feature of this study is to firstly attempt to capture rear-end collision potentials from the analysis of inductive loop detector data. Signals collected from loops are applied for monitoring individual vehicle information on freeways to estimate safe stopping distances in car-following situations. An index to quantify the potential of rear-end collisions is derived, and further employed for developing criteria to evaluate levels of rear-end collision risks.