基于公交车到站时间预测的动态滞站调度模型

    Dynamic Holding Strategy Based on Bus Arrival Time Prediction at Stops

    • 摘要: 提出了一个动态滞站调度策略.该策略通过一个基于支持向量机(SVM)和Kalman滤波的公交车辆到站时间预测模型来判断需要滯站的车辆,建立了一个以用户总费用最低为目标的数学模型来确定车辆在站点最优的滞留时间,并通过遗传算法对该模型进行求解.最后,以Paramics仿真数据对该动态滞站策略进行检验,结果表明,相比于传统滞站策略或无控制策略,动态滞站策略的效果更好.

       

      Abstract: Holding strategies are among the most commonly used operation control strategies.The author presents a new holding strategy.Firstly,the strategy determines a held bus by using a support vector machines (SVM) and Kalman filter-based prediction model.Then,in order to determine the optimal holding time,a model aiming to minimize the user costs is developed and a genetic algorithm (GA) is used to optimize the holding time of a held bus at a stop.Finally the presented holding strategy proposed in this study is illustrated with the microscopic simulation model Paramics,and results show that the dynamic holding strategy is effective compared with traditional holding strategy and no control strategy.

       

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