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SmartTransfer: Modeling the Spatiotemporal Dynamics of Passenger Transfers for Crowdedness-Aware Route Recommendations

Authors:

Hui XiongAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 6

Article No.: 70, Pages 1 - 26

https://doi.org/10.1145/3232229

Published: 08 November 2018 Publication History

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Abstract

In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.

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Cited By

Zhang LZhu XMa J(2024)IoT Route Planning Based on Spatiotemporal Interactive Attention Neural NetworkIEEE Internet of Things Journal10.1109/JIOT.2023.331898411:5(7697-7709)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3318984
Halder SLim KChan JZhang X(2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
https://doi.org/10.1016/j.asoc.2023.111200
Wang YQin QChen JWang JLiu K(2023)Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, ChinaJournal of Advanced Transportation10.1155/2023/55962852023(1-19)Online publication date: 10-Nov-2023
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Index Terms

SmartTransfer: Modeling the Spatiotemporal Dynamics of Passenger Transfers for Crowdedness-Aware Route Recommendations
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 6

Regular Papers

November 2018

290 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3289398

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 08 November 2018

Accepted: 01 June 2018

Revised: 01 March 2018

Received: 01 October 2017

Published in TIST Volume 9, Issue 6

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University of Missouri Research Board
Beijing Municipal Science and Technology Project
Natural Science Foundation of China
National Science Foundation

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Cited By

Zhang LZhu XMa J(2024)IoT Route Planning Based on Spatiotemporal Interactive Attention Neural NetworkIEEE Internet of Things Journal10.1109/JIOT.2023.331898411:5(7697-7709)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3318984
Halder SLim KChan JZhang X(2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
https://doi.org/10.1016/j.asoc.2023.111200
Wang YQin QChen JWang JLiu K(2023)Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, ChinaJournal of Advanced Transportation10.1155/2023/55962852023(1-19)Online publication date: 10-Nov-2023
https://doi.org/10.1155/2023/5596285
Wang DFu YLiu KChen FWang PLu C(2023)Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and EvaluationACM Transactions on Spatial Algorithms and Systems10.1145/35243029:1(1-24)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1145/3524302
戴政(2022)Offline Key Station and Path Impedance Based Subway Route Recommendation MethodHans Journal of Data Mining10.12677/HJDM.2022.12302512:03(246-258)Online publication date: 2022
https://doi.org/10.12677/HJDM.2022.123025
Verma RPargal SDas DParbat TKambalapalli SMitra BChakraborty S(2022)Impact of Driving Behavior on Commuter’s Comfort During Cab Rides: Towards a New Perspective of Driver RatingACM Transactions on Intelligent Systems and Technology10.1145/352306313:6(1-25)Online publication date: 22-Mar-2022
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Bieler MSkretting ABudinger PGronli T(2022)Survey of Automated Fare Collection Solutions in Public TransportationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316160623:9(14248-14266)Online publication date: Sep-2022
https://doi.org/10.1109/TITS.2022.3161606
Wang KTsung F(2022)Sparse and Robust Multivariate Functional Principal Component Analysis for Passenger Flow Pattern Discovery in Metro SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.307881623:7(8367-8379)Online publication date: Jul-2022
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Luo DZhao DKe QYou XLiu LMa H(2022)Spatiotemporal Hashing Multigraph Convolutional Network for Service-Level Passenger Flow Forecasting in Bus Transit SystemsIEEE Internet of Things Journal10.1109/JIOT.2021.31162419:9(6803-6815)Online publication date: 1-May-2022
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Yuan TXiong WYun L(2022)Transfer Route Recommendation for Metro Systems Based on Multi-source Data Fusion2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00274(1814-1821)Online publication date: Dec-2022
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