DeepAGS: Deep learning with activity, geography and sequential information in predicting an individual's next trip destination

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Intelligent Transport Systems Pub Date : 2024-08-19 DOI:10.1049/itr2.12554
Zhenlin Qin, Pengfei Zhang, Zhenliang Ma
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Abstract

Individual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting an individual's mobility. However, they are limited in modeling varied spatial mobility patterns driven by the same activity (e.g. an individual may travel to different locations for shopping). The paper proposes a deep learning model with activity, geographic and sequential (DeepAGS) information in predicting an individual's next trip destination in public transport. DeepAGS models the semantic features of activity and geography by using word embedding and graph convolutional network. An adaptive neural fusion gate mechanism is proposed to dynamically fuse the mobility activity and geographical information given the current trip information. Besides, DeepAGS uses the gated recurrent unit to capture the temporal mobility regularity. The approach is validated by using a real-world smartcard dataset in urban railway systems and comparing with state-of-the-art models. The results show that the proposed model outperforms its peers in terms of accuracy and robustness by effectively integrating the activity and geographical information relevant to a trip context. Also, we illustrate and verify the working mechanism of the DeepAGS model using the synthetic data constructed using real-world data. The DeepAGS model captures both the activity and geographic information of hidden mobility activities and thus could be potentially applicable to other mobility prediction tasks, such as bus trip destinations and individual GPS locations.

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DeepAGS:利用活动、地理和序列信息进行深度学习,预测个人的下一个旅行目的地
个人流动性受活动驱动,因此受到地理位置的限制,尤其是在公共交通的行程目的地预测方面。现有的基于统计学习的模型在预测个人流动性时侧重于提取流动性的规律性。然而,这些模型在模拟由同一活动驱动的不同空间移动模式(例如,个人可能会前往不同地点购物)方面存在局限性。本文提出了一种包含活动、地理和顺序信息(DeepAGS)的深度学习模型,用于预测个人在公共交通中的下一个出行目的地。DeepAGS 利用词嵌入和图卷积网络对活动和地理的语义特征进行建模。此外,DeepAGS 还提出了一种自适应神经融合门机制,可在当前行程信息的基础上动态融合移动活动和地理信息。此外,DeepAGS 还使用门控递归单元来捕捉时间移动规律性。该方法通过使用城市铁路系统中的真实智能卡数据集进行验证,并与最先进的模型进行比较。结果表明,通过有效整合与行程相关的活动和地理信息,所提出的模型在准确性和鲁棒性方面优于同类模型。此外,我们还利用使用真实世界数据构建的合成数据说明并验证了 DeepAGS 模型的工作机制。DeepAGS 模型同时捕捉了隐藏移动活动的活动信息和地理信息,因此有可能适用于其他移动预测任务,如公交车行程目的地和个人 GPS 位置。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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