Objective: To better capture the relationships between lane-changing collisions and explanatory variables, a microscopic model is developed for freeway lane-changing collisions based on the interactions between lane-changing vehicles.
Methods: The model applies an intervehicle interaction structure to account for the occurrence mechanism of lane-changing collisions. The occurrence mechanism can be described as the failure of a vehicle driver of an adjacent lane in avoiding the lane-changing vehicle, which disturbs the smooth movement of the adjacent lane vehicle and requires the driver's brake action to avoid an angle collision. This model is examined using data collected from freeways in Washington State during 2010 to 2011 and validated using lane-changing collision data for the SR 520 freeway.
Results: The findings of this study show that generalized truck percentage has a significant decreasing effect on lane-changing collision risk, whereas average spacing and several roadway characteristics have significant increasing effects. The frequency of slight collisions during peak hours is higher than that during off-peak hours. Young female drivers are more likely to be involved in collisions during lane-changing than young male drivers, but the result for senior drivers is opposite, with older male drivers having a higher probability of lane-changing collisions than female drivers in the same age group.
Conclusion: The process of lane-changing collisions is a complicated maneuver. Truck percentage, average spacing, and good roadway characteristics, such as straight and level segment, in the target lane have a significant effect on the occurrence of lane-changing collisions. Age and gender are also 2 important factors contributing to the relationship between lane-changing collisions and explanatory variables. 相似文献
This study aims to analyze the effects of environment, vehicle and driver characteristics on the risky driving behavior at work zones. A decision tree is developed using the classification and regression tree (CART) algorithm to graphically display the relationship between the risky driving behavior and its influencing factors. This approach could avoid the inherent problems occurred in the conventional logistic regression models and further improve the model prediction accuracy. Based on the Michigan M-94/I-94/I-94BL/I-94BR highway work zone driving behavior data, the decision tree comprising 33 leaf nodes is built. Bad weather, poor road and light conditions, partial/no access control, no traffic control devices, turning left/right and driving in an old vehicle are found to be associated with the risky driving behavior at work zones. The middle-aged drivers, who are going straight ahead in their vehicles with medium service time and equipped with an airbag system, are more likely to take risky behavior at lower work zone speed limits. Further, the middle-aged male drivers engage in risky driving behavior more frequently than the middle-aged female drivers. The number of lanes exhibits opposing effects on risky behavior under different traveling conditions. More specifically, the risky driving behavior is associated with the single-lane road under bad light or weather conditions while drivers are more likely to engage in risky behavior on the multi-lane road under good light conditions. 相似文献
