论文标题
探索时空粒度对动态乘车的需求预测的影响
Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing
论文作者
论文摘要
动态需求预测是乘车派遣的关键问题。已经开发了许多方法来提高需求响应性，乘车运输服务的需求预测准确性。然而，很少探索由于多尺度时空粒度以及由此产生的统计误差而导致的乘车需求的不确定性。本文试图填补这一空白，并通过使用中国成都的经验数据来研究时空粒度对乘车需求预测准确性的影响。提出了卷积长期的短期记忆模型与六角卷积操作（H-Convlstm）相结合，以探索复杂的空间和时间关系。实验分析结果表明，所提出的方法在预测准确性方面优于常规方法。比较36个时空粒度与出发需求和到达需求的比较表明，六角形空间分区的结合具有800 m的侧面长度，并且30分钟的时间间隔可实现最佳的综合预测准确性。但是，出发需求和到达需求揭示了各种时空粒度的预测错误的不同变化趋势。
Dynamic demand prediction is a key issue in ride-hailing dispatching. Many methods have been developed to improve the demand prediction accuracy of an increase in demand-responsive, ride-hailing transport services. However, the uncertainties in predicting ride-hailing demands due to multiscale spatiotemporal granularity, as well as the resulting statistical errors, are seldom explored. This paper attempts to fill this gap and to examine the spatiotemporal granularity effects on ride-hailing demand prediction accuracy by using empirical data for Chengdu, China. A convolutional, long short-term memory model combined with a hexagonal convolution operation (H-ConvLSTM) is proposed to explore the complex spatial and temporal relations. Experimental analysis results show that the proposed approach outperforms conventional methods in terms of prediction accuracy. A comparison of 36 spatiotemporal granularities with both departure demands and arrival demands shows that the combination of a hexagonal spatial partition with an 800 m side length and a 30 min time interval achieves the best comprehensive prediction accuracy. However, the departure demands and arrival demands reveal different variation trends in the prediction errors for various spatiotemporal granularities.
