Effect evaluation of traffic guidance in urban underground road diverging and merging areas: A simulator study.

Traffic accidents occur frequently in urban underground road diverging and merging areas due to the limited vision and complex traffic. Well-designed traffic visual guidance is one of the effective measures to alleviate the traffic safety problems in the diverging and merging areas of urban underground roads. In this study, four different integrated traffic guidance schemes (including signs, marking lines and sidewall guidance) were proposed and their effects on drivers' behaviour were investigated through driving simulator experiments and questionnaire survey. To investigate the influence of different schemes, eight variables about driving behaviour and guidance efficiency were assessed for analysis. Finally, a fuzzy comprehensive evaluation model based on analytic hierarchy process (FCE + AHP) was constructed to evaluate the effect of guidance schemes. Vehicle running state, driver operation behaviour and guidance efficiency were mainly considered. The guidance evaluation results obtained by the model were consistent with the conclusions of the driver subjective questionnaire. The results show that reasonable setting of white dotted lines and colour guidance can help drivers find exits quickly and improve driving stability. However, excessive combination of traffic guidance leads to information overload and produces the opposite effect. This study can provide a generic framework for the design and evaluation of traffic guidance facilities of urban underground roads.

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions.

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.