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A reliable traffic prediction approach for bike-sharing system by exploiting rich information with temporal link prediction strategy

机译：利用时间链路预测策略利用丰富的信息，一种可靠的交通预测方法

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摘要

Bike-sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike-sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike-sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non-negative tensor decomposition and time-series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across-clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike-sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow.
机译：自行车分享系统已广泛应用于全球的主要城市。随着在不同时期的不同站点的自行车借贷并返回不平衡，自行车共享系统中的自行车需要经常重新分配以重新平衡系统。因此，自行车共享系统的交通流预测是一个重要问题，因为这有利于实现自行车系统的重新平衡。在本文中，我们基于动态流量流网络中的时间链路提出了一种新的流量预测方法。首先将站聚类算法引入群集站组。然后提出了一种基于动态流量流网络方法（STW + M）的时间链路预测方法来预测站之间的业务流量。在我们的方法中，非负张量分解和时间序列分析模型捕获跨越群集转换的丰富的信息（时间可变性，空间特征和天气信息）。然后，时间链路预测策略用于通过研究网络结构和张量计算的结果来预测业务流网络中的潜在链路和权重。为了评估本文提出的方法，我们使用了纽约和华盛顿的自行车共享系统数据，直流进行自行车交通预测，实验结果表明我们的方法产生最低的根均方误差（ RMSE）和均方误差（MSE）。与来自文献的四种预测方法相比，我们的两个数据集的RMSE和MSE平均降低了2.55（华盛顿特区）和2.41（纽约）和3.35（华盛顿特区）和2.96（纽约），分别。结果表明，该方法提高了对交通流量的预测。

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  • 来源
    《Transactions in GIS: TG》 |2019年第5期|共27页
  • 作者

    Zhou Yan; Li Yanxi; Zhu Qing; Chen Fen; Shao Junming; Luo Yunxin; Zhang Yeting; Zhang Pengcheng; Yang Weijun;

  • 作者单位

    Univ Elect Sci &

    Technol China Sch Resources &

    Environm Qingshuihe Campus 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China;

    Univ Elect Sci &

    Technol China Sch Resources &

    Environm Qingshuihe Campus 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China;

    Southwest Jiaotong Univ Fac Geosci &

    Environm Engn Chengdu Sichuan Peoples R China;

    Univ Elect Sci &

    Technol China Sch Resources &

    Environm Qingshuihe Campus 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China;

    Univ Elect Sci &

    Technol China Sch Resources &

    Environm Qingshuihe Campus 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China;

    Univ Elect Sci &

    Technol China Sch Resources &

    Environm Qingshuihe Campus 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China;

    Wuhan Univ State Key Lab Informat Engn Surveying Mapping &

    R Wuhan Hubei Peoples R China;

    Guangzhou Urban Planning &

    Design Survey Res Inst Guangzhou Guangdong Peoples R China;

    Guangzhou Urban Planning &

    Design Survey Res Inst Guangzhou Guangdong Peoples R China;

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  • 正文语种 eng
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