Ride-hailing, as a popular shared-transportation method, has been operated in many areas all over the world. Researchers conducted various researches based on global cases. They argued on whether car-hailing is an effective travel mode for emission reduction and drew different conclusions. The detailed emission performance of the ride-hailing system depends on the cases. Therefore, there is an urgent demand to reduce the overall picking up distance during the dispatch. In this study, we try to satisfy this demand by proposing an optimization method combined with a prediction model to minimize the global void cruising distance when solving the dispatch problem. We use Didi ride-hailing data on one day for simulation and found that our method can reduce the picking up distance by 7.51% compared with the baseline greedy algorithm. The proposed algorithm additionally makes the average waiting time of passengers more than 4 minutes shorter. The statistical results also show that the performance of our method is stable. Almost the metric in all cases can be kept in a low interval. What is more, we did a day-to-day comparison. We found that, despite the different spatial-temporal distribution of orders and drivers on different day conditions, there are little differences in the performance of the method. We also provide temporal analysis on the changing pattern of void cruising distance and quantity of orders on weekdays and weekends. Our findings show that our method can averagely reduce more void cruising distance when ride-hailing is active compared with the traditional greedy algorithm. The result also shows that the method can stably reduce void cruising distance by about 4000 to 5000 m per order across one day. We believe that our findings can improve deeper insight into the mechanism of the ride-hailing system and contribute to further studies.

中文翻译：

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城市在线行车中的GPS数据：减少空行巡航距离的优化与预测模拟

乘搭叫车作为一种流行的共享运输方式，已经在世界许多地区得到了应用。研究人员根据全球案例进行了各种研究。他们争论了汽车叫车是否是一种有效的减少排放的出行方式，并得出了不同的结论。乘车系统的详细排放性能取决于具体情况。因此，迫切需要减少派遣期间的总接送距离。在这项研究中，我们试图通过提出一种优化方法并结合一个预测模型来满足这一需求，以在解决调度问题时最小化整体空域巡航距离。我们在一天中使用滴滴叫车数据进行仿真，发现与基线贪婪算法相比，我们的方法可以将拾取距离减少7.51％。所提出的算法还使乘客的平均等待时间缩短了4分钟以上。统计结果也表明我们的方法的性能是稳定的。在所有情况下，几乎可以将度量标准保持在较低的间隔中。而且，我们进行了日常比较。我们发现，尽管在不同的白天条件下订单和驱动程序的时空分布不同，但该方法的性能几乎没有差异。我们还提供工作日和周末的空运距离和订单数量变化模式的时间分析。我们的发现表明，与传统的贪婪算法相比，当打车服务活跃时，我们的方法可以平均减少更多的空巡航距离。结果还表明，该方法可以在一天的时间内稳定地将空巡航距离减少约4000到5000 m。我们相信，我们的发现可以提高对乘车系统机制的更深入的了解，并有助于进一步的研究。