利用条件自回归模型对宏观状态下的自行车碰撞事件进行空间分析

根据美国国家公路交通安全管理局(the nation-al highway traffic safety administration,NHTSA)资料显示,目前美国的自行车通勤量增加了60%以上。然而,近年来骑自行车者的安全风险也呈上升趋势。佛罗里达州2015年自行车事故死亡人数为150人,是2010年的1.8倍。总的来说,佛罗里达州在2010—2015年期间以755起行车死亡案例数居全美前列。这些严峻的统计数据强调需要对佛罗里达州的自行车碰撞事故进行深入调查分析[1]。

Optimization of Adaptive Transit Signal Priority Using Parallel Genetic Algorithm

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority （TSP） strategy that applies the parallel genetic algorithm （PGA） to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSIM （VISual SIMulation） simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.