Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, PR China.
Future Human Habitats Division, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, PR China.
PLoS One. 2021 Mar 18;16(3):e0248064. doi: 10.1371/journal.pone.0248064. eCollection 2021.
Taxi order demand prediction is of tremendous importance for continuous upgrading of an intelligent transportation system to realise city-scale and personalised services. An accurate short-term taxi demand prediction model in both spatial and temporal relations can assist a city pre-allocate its resources and facilitate city-scale taxi operation management in a megacity. To address problems similar to the above, in this study, we proposed a multi-zone order demand prediction model to predict short-term taxi order demand in different zones at city-scale. A two-step methodology was developed, including order zone division and multi-zone order prediction. For the zone division step, the K-means++ spatial clustering algorithm was used, and its parameter k was estimated by the between-within proportion index. For the prediction step, six methods (backpropagation neural network, support vector regression, random forest, average fusion-based method, weighted fusion-based method, and k-nearest neighbour fusion-based method) were used for comparison. To demonstrate the performance, three multi-zone weighted accuracy indictors were proposed to evaluate the order prediction ability at city-scale. These models were implemented and validated on real-world taxi order demand data from a three-month consecutive collection in Shenzhen, China. Experiment on the city-scale taxi demand data demonstrated the superior prediction performance of the multi-zone order demand prediction model with the k-nearest neighbour fusion-based method based on the proposed accuracy indicator.
出租车订单需求预测对于不断升级智能交通系统以实现城市规模和个性化服务至关重要。一个准确的短期出租车需求预测模型，在空间和时间关系上，可以帮助城市预先分配资源，并促进特大城市的城市规模出租车运营管理。为了解决类似的问题，在本研究中，我们提出了一种多区域订单需求预测模型，以预测城市规模不同区域的短期出租车订单需求。该方法采用两步法，包括订单区域划分和多区域订单预测。在区域划分步骤中，使用了 K-means++空间聚类算法，其参数 k 通过组间组内比例指数进行估计。在预测步骤中，使用了六种方法（反向传播神经网络、支持向量回归、随机森林、基于平均融合的方法、基于加权融合的方法和基于 K-最近邻融合的方法）进行比较。为了验证性能，提出了三个多区域加权精度指标，以评估城市规模的订单预测能力。这些模型在中国深圳连续三个月的真实出租车订单需求数据上进行了实现和验证。基于所提出的精度指标，对城市规模出租车需求数据的实验表明，基于 K-最近邻融合的多区域订单需求预测模型具有优越的预测性能。
