research-article

Public Access

MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems

Authors:

Desheng ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 2

Article No.: 42, Pages 1 - 24

https://doi.org/10.1145/3328913

Published: 21 June 2019 Publication History

Abstract

Urban anomalies have a large impact on passengers' travel behavior and city infrastructures, which can cause uncertainty on travel time estimation. Understanding the impact of urban anomalies on travel time is of great value for various applications such as urban planning, human mobility studies and navigation systems. Most existing studies on travel time have been focused on the total riding time between two locations on an individual transportation modality. However, passengers often take different modes of transportation, e.g., taxis, subways, buses or private vehicles, and a significant portion of the travel time is spent in the uncertain waiting. In this paper, we study the fine-grained travel time patterns in multiple transportation systems under the impact of urban anomalies. Specifically, (i) we investigate implicit components, including waiting and riding time, in multiple transportation systems; (ii) we measure the impact of real-world anomalies on travel time components; (iii) we design a learning-based model for travel time component prediction with anomalies. Different from existing studies, we implement and evaluate our measurement framework on multiple data sources including four city-scale transportation systems, which are (i) a 14-thousand taxicab network, (ii) a 13-thousand bus network, (iii) a 10-thousand private vehicle network, and (iv) an automatic fare collection system for a public transit network (i.e., subway and bus) with 5 million smart cards.

References

[1]

Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, Shijian Li, and Zonghui Wang. 2013. iBOAT: Isolation-Based Online Anomalous Trajectory Detection. IEEE Transactions on Intelligent Transportation Systems 14, 2 (2013), 806--818.

Digital Library

[2]

Zhihan Fang, Fan Zhang, Ling Yin, and Desheng Zhang. 2018. MultiCell: Urban Population Modeling Based on Multiple Cellphone Networks. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 106 (Sept. 2018), 25 pages.

Digital Library

[3]

Piero Lovisolo Dario Parata Francesco Calabrese, Massimo Colonna and Carlo Ratti. 2011. Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 12 (March 2011).

Digital Library

[4]

Mengdan Gao, Tongyu Zhu, Xuejin Wan, and Qi Wang. 2013. Analysis of travel time patterns in urban using taxi gps data. In 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. IEEE, 512--517.

Digital Library

[5]

Qi Guan-De, Pan Yao, Li Shi-Jian, and Pan Gang. 2013. Predicting Passengers' Waiting Time by Mining Taxi Traces. (2013).

[6]

Mordechai Haklay and Patrick Weber. 2008. Openstreetmap: User-generated street maps. Ieee Pervas Comput 7, 4 (2008), 12--18.

Digital Library

[7]

Wonjae Jang. 2010. Travel time and transfer analysis using transit smart card data. Transportation Research Record: Journal of the Transportation Research Board 2144 (2010), 142--149.

[8]

Yanjie Ji, Liangpeng Gao, Yingling Fan, Chu Zhang, and Ruochen Zhang. 2017. Waiting time perceptions at bus and metro stations in Nanjing, China: the importance of station amenities, trip contexts, and passenger characteristics. Transportation Letters (2017), 1--7.

[9]

Neal Lathia and Licia Capra. 2011. How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 291--300.

Digital Library

[10]

Neal Lathia and Licia Capra. 2011. Mining mobility data to minimise travellers' spending on public transport. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1181--1189.

Digital Library

[11]

Haengju Lee, Desheng Zhang, Tian He, and Sang Hyuk Son. 2017. MetroTime: Travel Time Decomposition under Stochastic Time Table for Metro Networks. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP). 1--8.

[12]

Junghoon Lee. 2008. Analysis on the Waiting Time of Empty Taxis for the Taxi Telematics System. In 2008 Third International Conference on Convergence and Hybrid Information Technology, Vol. 1. 66--69.

Digital Library

[13]

Bin Li, Daqing Zhang, Lin Sun, Chao Chen, Shijian Li, Guande Qi, and Qiang Yang. 2011. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In PerCom Workshops.

[14]

Miao Lin and Wen-Jing Hsu. 2014. Mining GPS data for mobility patterns: A survey. Pervasive and Mobile Computing 12 (2014), 1--16.

[15]

Zhidan Liu, Zhenjiang Li, Mo Li, Wei Xing, and Dongming Lu. 2016. Mining road network correlation for traffic estimation via compressive sensing. IEEE Transactions on Intelligent Transportation Systems 17, 7 (2016), 1880--1893.

[16]

Eric Hsueh-Chan Lu, Chih-Yuan Lin, and Vincent S Tseng. 2011. Trip-mine: An efficient trip planning approach with travel time constraints. In Mobile Data Management (MDM), 2011 12th IEEE International Conference on, Vol. 1. IEEE, 152--161.

Digital Library

[17]

Google Map. 2018. Shenzhen Guangdong, China. (2018). https://www.google.ca/maps/place/Shenzhen,+Guangdong,+China/@22.5554167,113.9137924,11z/data=!3m1!4b1!4m5!3m4!1s0x3403f408d0e15291:0xfdee550db79280c9!8m2!3d22.543096!4d114.057865

[18]

Stephan Wagner Gerd Kortuem Marcus Handte, Stefan Foell and Pedro José Marrón. 2016. An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders. IEEE Internet of Things Journal 3, 5 (October 2016).

[19]

Population Reference Bureau. 2013. World Population Data Sheet. http://www.prb.org/pdf13/2013-population-data-sheet-eng.pdf (2013).

[20]

Zhou Qin, Zhihan Fang, Yunhuai Liu, Chang Tan, Wei Chang, and Desheng Zhang. 2018. EXIMIUS: A measurement framework for explicit and implicit urban traffic sensing. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. ACM, 1--14.

Digital Library

[21]

Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki. 2016. DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level. In IJCAI, Vol. 16. 2618--2624.

Digital Library

[22]

Lijun Sun, Der-Horng Lee, Alex Erath, and Xianfeng Huang. 2012. Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system. In Proceedings of the ACM SIGKDD international workshop on urban computing. ACM, 142--148.

Digital Library

[23]

Arvind Thiagarajan, James Biagioni, Tomas Gerlich, and Jakob Eriksson. 2010. Cooperative transit tracking using smart-phones. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. 85--98.

Digital Library

[24]

Alejandro Tirachini. 2013. Estimation of travel time and the benefits of upgrading the fare payment technology in urban bus services. Transportation Research Part C: Emerging Technologies 30 (2013), 239--256.

[25]

Huandong Wang, Fengli Xu, Yong Li, Pengyu Zhang, and Depeng Jin. 2015. Understanding mobile traffic patterns of large scale cellular towers in urban environment. In Proceedings of the 2015 Internet Measurement Conference. ACM, 225--238.

Digital Library

[26]

Yilun Wang, Yu Zheng, and Yexiang Xue. 2014. Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 25--34.

Digital Library

[27]

Xiaoyang Xie, Yu Yang, Zhihan Fang, Guang Wang, Fan Zhang, Fan Zhang, Yunhuai Liu, and Desheng Zhang. 2018. coSense: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 4 (2018), 196.

Digital Library

[28]

Y. Zheng Y. Wang and Y. Xue. 2014. Travel Time Estimation of a Path using Sparse Trajectories. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2014).

Digital Library

[29]

Yu Yang, Xiaoyang Xie, Zhihan Fang, Fang Zhang, Yang Wang, and Desheng Zhang. 2018. VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration. arXiv preprint arXiv:1812.02780 (2018).

[30]

Yu Yang, Fan Zhang, and Desheng Zhang. 2018. SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 48.

Digital Library

[31]

Yu Yang, Fan Zhang, and Desheng Zhang. 2018. SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 48 (March 2018), 26 pages.

Digital Library

[32]

Desheng Zhang, Jun Huang, Ye Li, Fan Zhang, Chengzhong Xu, and Tian He. 2014. Exploring human mobility with multi-source data at extremely large metropolitan scales. In Proceedings of the 20th annual international conference on Mobile computing and networking. 201--212.

Digital Library

[33]

Desheng Zhang, Ye Li, Fan Zhang, Mingming Lu, Yunhuai Liu, and Tian He. 2013. coRide: Carpool Service with a Win-win Fare Model for Large-scale Taxicab Networks. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13). ACM, New York, NY, USA, Article 9, 14 pages.

Digital Library

[34]

Desheng Zhang, Juanjuan Zhao, Fan Zhang, and Tian He. 2015. UrbanCPS: A Cyber-physical System Based on Multi-source Big Infrastructure Data for Heterogeneous Model Integration. In Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems (ICCPS '15). ACM, New York, NY, USA, 238--247.

Digital Library

[35]

Huichu Zhang, Yu Zheng, and Yong Yu. 2018. Detecting Urban Anomalies Using Multiple Spatio-Temporal Data Sources. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 54.

Digital Library

[36]

Jianting Zhang. 2012. Smarter outlier detection and deeper understanding of large-scale taxi trip records: a case study of NYC. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing. 157--162.

Digital Library

[37]

Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, and Wei-Ying Ma. 2008. Understanding mobility based on GPS data. In Proceedings of the 10th international conference on Ubiquitous computing. ACM, 312--321.

Digital Library

[38]

Yu Zheng, Huichu Zhang, and Yong Yu. 2015. Detecting collective anomalies from multiple spatio-temporal datasets across different domains. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2.

Digital Library

[39]

Pengfei Zhou, Yuanqing Zheng, and Mo Li. 2012. How long to wait?: predicting bus arrival time with mobile phone based participatory sensing. In Proceedings of the 10th international conference on Mobile systems, applications, and services. ACM, 379--392.

Digital Library

[40]

Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, and J. Andrew Bagnell. 2008. Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. In Proceedings of the 10th international conference on Ubiquitous computing (UbiComp '08).

Digital Library

Cited By

Loo BWang H(2024)Dynamics of in-station time within metro systems: Measurement and determining factorsTunnelling and Underground Space Technology10.1016/j.tust.2024.106006153(106006)Online publication date: Nov-2024
https://doi.org/10.1016/j.tust.2024.106006
Tan HYuan YZhong SYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed DemandProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614942(2392-2401)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614942
Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614924
Show More Cited By

Index Terms

MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
    2. Ubiquitous and mobile computing design and evaluation methods
2. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

Recommendations

Learning-based adversarial agent detection and identification in cyber physical systems applied to autonomous vehicular platoon
SEsCPS '19: Proceedings of the 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems

The security of cyber physical systems such as autonomous vehicle platoons plays a vital role in ensuring passenger safety. An adversary in control of a single vehicle can degrade platoon efficiency or even cause collisions. In this paper, we focus on ...
Design of cooperative vehicle safety systems based on tight coupling of communication, computing and physical vehicle dynamics
ICCPS '10: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems

One of the main characteristics of a Cyber Physical System (CPS) is the tight coupling of the computing and communications aspects of the system with its physical dynamics. In this paper, we examine this characteristic for a cooperative vehicle safety (...
Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities
With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 2

June 2019

802 pages

EISSN:2474-9567

DOI:10.1145/3341982

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2019

Accepted: 01 April 2019

Revised: 01 February 2019

Received: 01 November 2018

Published in IMWUT Volume 3, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

40
Total Citations
View Citations
1,167
Total Downloads

Downloads (Last 12 months)174
Downloads (Last 6 weeks)18

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Loo BWang H(2024)Dynamics of in-station time within metro systems: Measurement and determining factorsTunnelling and Underground Space Technology10.1016/j.tust.2024.106006153(106006)Online publication date: Nov-2024
https://doi.org/10.1016/j.tust.2024.106006
Tan HYuan YZhong SYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed DemandProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614942(2392-2401)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614942
Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614924
Yi JYan HWang HYuan JLi YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly ConditionsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614671(4916-4922)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614671
E JLi MHuang J(2023)CrowdAtlas: Estimating Crowd Distribution within the Urban Rail Transit SystemACM Transactions on Knowledge Discovery from Data10.1145/355852117:4(1-24)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3558521
Xiao ZFang HJiang HBai JHavyarimana VChen HJiao L(2023)Understanding Private Car Aggregation Effect via Spatio-Temporal Analysis of Trajectory DataIEEE Transactions on Cybernetics10.1109/TCYB.2021.311770553:4(2346-2357)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3117705
Mo XCao CLi MWang D(2022)Predicting the Impact of Disruptions to Urban Rail Transit SystemsACM Transactions on Sensor Networks10.1145/351701519:1(1-17)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1145/3517015
Duan SLyu FRen JWang YYang PZhang DZhang Y(2022)Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316906823:10(19484-19496)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3169068
Wang PHuang ZLai JZheng ZLiu YLin T(2022)Traffic Speed Estimation Based on Multi-Source GPS Data and Mixture ModelIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.309540823:8(10708-10720)Online publication date: Aug-2022
https://doi.org/10.1109/TITS.2021.3095408
Fang ZWang GYang YZhang FWang YZhang D(2022)A long-term travel delay measurement study based on multi-modal human mobility dataScientific Reports10.1038/s41598-022-19394-z12:1Online publication date: 26-Sep-2022
https://doi.org/10.1038/s41598-022-19394-z
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents