Vehicle Routing Based on Floating Car Data
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摘要: 利用浮动车信息采集系统预测路段行程时间,实现对带时间窗的混合车辆配送路径选择的优化.提出了带时间窗的混合车辆路径选择优化问题的求解模型;设计了浮动车地图匹配和路段行程时间预测算法,以实现对路段行程时间的预测,并通过给出的成都市浮动车数据证明了所提出的算法比同类算法更有效——地图匹配率提高6%,路段行程时间预测值与实测值的拟合度更高,运输总费用节约24%.Abstract: A floating car information collection system was used to predict section travel time so as to realize the routing optimization of mixed traffic with time windows.A model for the routing optimization of mixed traffic with time windows was set up,and algorithms for floating car map matching and section travel time prediction were proposed to predict section travel time.The results based on floating car data from Chengdu City show that the proposed algorithms are more effective than the present algorithms.With the proposed algorithms,the map matching rate increases by 6%,a higher fitting degree between the predicted and measured values of section travel time is gained,and the total transportation cost decreases by 24%.
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Key words:
- floating car data /
- travel time prediction /
- vehicle routing
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