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An automatic real-time bus schedule redesign method based on bus arrival time prediction

机译:基于总线到达时间预测的自动实时总线计划重新设计方法

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Bus travel time often fluctuates greatly due to disturbance factors such as traffic conditions and passenger flow, which causes a reduction in the transit service as it cannot be provided as planned. Bus dispatching is necessary to handle this problem. Two crucial factors for real-time bus dispatching have been analysed in this study namely the bus arrival time prediction and timetable redesign. First, a bus arrival time prediction model combining Support Vector Regression (SVR) and Kalman Filter (K-SVR) was proposed. The headway is selected as an intermediate variable to improve the prediction accuracy by limiting the disturbance factors between two adjacent bus stops. Besides, by adjusting the parameters of the Kalman Filter, the predictive results are more suitable for practice to avoid the frequency of unnecessary timetable adjustment. Furthermore, an automatic timetable redesign method is given based on the proposed circle search algorithm, which can minimise the impact on the initial schedule by finding the minimum adjustment range in consideration of the normal working order. A case study in Shenzhen, China was conducted and the results verify that the K-SVR method could improve the prediction accuracy especially in peak hours. On this basis, in comparison with the traditional method, the timetable redesign algorithm could allocate the transit resources appropriately and perform well on indicators such as the headway fluctuation, compliance rate of headways, and schedule completeness. Moreover, it is more operable and valuable as a result of sending the departure instructions appropriately and maintaining the normal working order of drivers realistically.
机译:总线旅行时间通常由于交通状况和客流等干扰因素而波动,这导致运输服务的减少,因为它不能按计划提供。公共汽车调度是处理此问题的必要条件。在本研究中分析了实时总线调度的两个关键因素,即总线到达时间预测和时间表重新设计。首先,提出了组合支持向量回归(SVR)和卡尔曼滤波器(K-SVR)的总线到达时间预测模型。通过限制两个相邻总线停止之间的干扰因素,选择始终作为中间变量来提高预测精度。此外,通过调整卡尔曼滤波器的参数,预测结果更适合于做法,以避免不必要的时间表调整的频率。此外,基于所提出的圆形搜索算法给出自动时间表重新设计方法,其可以通过考虑正常工作顺序找到最小调整范围来最小化对初始调度的影响。在深圳进行了一个案例研究,并进行了结果,结果验证了K-SVR方法可以提高预测准确性,特别是在高峰时段。在此基础上,与传统方法相比,时间表重新设计算法可以适当地分配交通资源,并对指标进行良好的指标,以及前往的符合性率和进度完整性。此外,由于恰当地发送了驾驶员的正常工作顺序,它更为可操作且更有价值。

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