道路交通事故多因素分析

利用皮尔逊相关系数对影响道路交通事故的机动车驾驶员各违法行为与道路交通事故等4项指标相关性进行了分析。结果表明:机动车驾驶员各违法行为与道路交通事故总数、死亡人数及受伤人数具有显著的相关性。为进一步了解机动车驾驶员各违法行为与道路交通事故4项指标的相关程度的大小,找到与4项指标相关度最高的机动车驾驶员违法行为,并提出了一种改进灰色综合关联度模型对其关联度进行了分析。结果表明:驾驶员无证驾驶违法行为与道路交通事故总数、受伤人数及直接经济损失的灰色综合关联度最高;未按规定让行违法行为与交通事故死亡人数的灰色综合关联度最高;逆向行驶、违法超车及超速行驶的与道路交通事故4项指标的灰色综合关联度也都很高。因此,在进行道路交通事故防治时,应重点减少驾驶员无证驾驶、未按规定让行、逆向行驶、违法超车及超速行驶违法行为的发生,从而有效地减少道路交通事故的发生及其带来的损失。

A geospatial service composition approach based on MCTS with temporal-difference learning

With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.