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Rapid rescue response has the highest priority in case of emergency randomly happening on the freeway network, which allows rescue vehicles to have many trajectory options. Searching for the fastest way is not easy within a short time after traffic accident happens especially for the mountainous area with special characteristics such as limited traffic capacity, enclosed internal space and so on. Here, road segment model is proposed to determine smallest road segment covering possible rescue ways. Other than traditional optimal search methods, modified reinforcement-leaning is introduced to find the optimal road trajectory. The proposed methods are tested in the freeway of Qinling Tunnel group, Xihan Freeway of Shaanxi province, China as a case study. Compared with traditional shortest path method, the rescue vehicle arrival time to the accident location is shortened from 22.9 to 6.5 min and dissipation time is also shortened from 52.4 to 25.6 min. Both of them show the proposed road trajectory could improve the rescue effectiveness and reduce the influence to road network. Successful application of these case study shows they could probably extend to use to other scenarios and contribute to improve the intelligence transportation system.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The purpose of this study was to develop a driving behavior scale for professional drivers of heavy semi-trailer trucks in China, and study the causes of such driving behavior and its impact on traffic safety operation. Data was processed by IBM SPSS 25. In addition to principal component analysis, Promax rotation, Bartlett’s test, Cronbach’s alpha, correlation analysis and binary logistic regression were examined. A DBQ with 4 dimensions and 20 items, and a PDBQ with 1 dimension and 6 items were developed for professional drivers of heavy semi-trailer trucks in China. The KMO coefficients of PDBQ and DBQ were 0.822 and 0.852, respectively, and the significant level of Bartlett’s popularity test was p < 0.0001. The accident prediction model showed that the variables related to traffic accidents were negligence/lapses and driving time of heavy semi-trailer truck drivers. 1–5 a.m. was found to be the most dangerous period for drivers of medium and heavy semi-trailer trucks, during which accidents were most likely to happen. As negligence/lapses increased by one unit, the probability of traffic accidents increased by 2.293 times.

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