| 摘要: |
| 随着滴滴、Uber等出租车服务的日益普及，用户的乘车需求预测逐渐成为智慧城市、智慧交通的重要组成部分.准确的预测模型既可以满足用户的出行需求，也可以降低道路车辆空载率，有效地避免资源浪费，并缓解交通压力.车辆服务商可以收集到大量GPS数据及用户需求数据，然而，如何合理运用数据进行需求预测，是关键且实用的问题.提出一种结合城市POI的可变形卷积时空网络（DCSN）模型来预测区域乘车需求，模型包括两部分——可变形卷积时空模型与POI需求关联模型：前者即通过DCN与LSTM建模未来需求与时空之间的相关性，后者则通过区域POI差异化指数与需求差异化指数捕捉区域间的相似关系.最后使用全连接网络将两个模型整合起来，进而得出预测结果.使用滴滴出行的大型真实乘车需求数据进行实验，最终实验结果表明，所提出的方法在预测精度上优于现有的预测方法. |
| 关键词: 城市计算 时空相关性 可变型卷积网络 |
| DOI：10.13328/j.cnki.jos.006115 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金（62072094）；辽宁省兴辽英才计划（XLYC2005001）；辽宁省重点研发计划（2020JH2/10100046）；中央高校基本科研业务费专项资金（N182608004） |
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| Passenger Demand Forecast Model Based on Deformable Convolution Spatial-temporal Network |
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YU Rui-Yun1, LIN Fu-Yu1, GAO Ning-Wei1, LI Jie2
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1.Software College, Northeastern University, Shenyang 110169, China;2.School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
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| Abstract: |
| With the increasing popularity of taxi services such as Didi and Uber, passengers’ demand has gradually become an important part of smart cities and smart transportation. The accurate prediction model can not only meet the travel needs of users, but also reduce the no-load rate of road vehicles, which can effectively avoid waste of resources and relieve traffic pressure. Vehicle service providers can collect a large amount of GPS data and passenger demand data, but how to use this big data to forecast demand is a key and practical problem. This study proposes a deformable convolution spatial-temporal network (DCSN) model that combines urban POI to predict regional ride demand. Specifically, the model proposed in this study consists of two parts: the deformable convolution spatial-temporal model and the POI requirement correlation model. The former models the correlation between future demand and time and space through DCN and LSTM, while the latter captures the similar relationship among regions through the regional POI differentiation index and the demand differentiation index. Finally, the two models are integrated by a fully connected network. Then the prediction results are obtained. In this study, the large real ride demand data of Didi trips is used for experiments. The final experimental results show that the proposed method outperforms the existing forecasting methods in terms of prediction accuracy. |
| Key words: urban computing spatial-temporal correlation deformable convolutional network |
