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BusTr: Predicting Bus Travel Times from Real-Time Traffic

Authors:

Richard Barnes,

Senaka Buthpitiya,

Alex Fabrikant,

Andrew Tomkins,

Fangzhou XuAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3243 - 3251

https://doi.org/10.1145/3394486.3403376

Published: 20 August 2020 Publication History

Abstract

We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. We demonstrate that our neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. We also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.

References

[1]

Anne Aguiléra and Jean Grébert. Passenger transport mode share in cities: exploration of actual and future trends with a worldwide survey. International Journal of Automotive Technology and Management, 14 (3--4): 203--216, 2014.

[2]

Michael L Anderson. Subways, strikes, and slowdowns: The impacts of public transit on traffic congestion. American Economic Review, 104 (9): 2763--96, 2014.

[3]

Richard Barnes. Optimal orientations of discrete global grids and the poles of inaccessibility. International Journal of Digital Earth, 0 (0): 1--14, 2019.

[4]

Candace Brakewood, Sean Barbeau, and Kari Watkins. An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida. Transportation Research Part A: Policy and Practice, 69: 409--422, 2014.

[5]

Sandip Chakrabarti and Genevieve Giuliano. Does service reliability determine transit patronage? insights from the Los Angeles Metro bus system. Transport Policy, 42: 12--20, 2015. ISSN 0967-070X. URL http://www.sciencedirect.com/science/article/pii/S0967070X15300068.

[6]

Mei Chen, Jason Yaw, Steven I. Chien, and Xiaobo Liu. Using automatic passenger counter data in bus arrival time prediction. Journal of Advanced Transportation, 41 (3): 267--283, 2007. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/atr.5670410304.

[7]

Raj Chetty and Nathaniel Hendren. The impacts of neighborhoods on intergenerational mobility I: Childhood exposure effects. The Quarterly Journal of Economics, 133 (3): 1107--1162, 2018.

[8]

B. Dhivyabharathi, B. Anil Kumar, Avinash Achar, and Lelitha Vanajakshi. Bus travel time prediction: A lognormal auto-regressive (AR) modeling approach. arXiv: 1904.03444, 2019.

[9]

Alex Fabrikant. Predicting bus delays with machine learning. Google AI Blog, 2019. URL https://ai.googleblog.com/2019/06/predicting-bus-delays-with-machine.html.

[10]

Brian Ferris, Kari Watkins, and Alan Borning. OneBusAway: results from providing real-time arrival information for public transit. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1807--1816. ACM, 2010.

Digital Library

[11]

Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley. Google Vizier: A Service for Black-Box Optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17, pages 1487--1495, Halifax, NS, Canada, 2017. ACM Press. ISBN 978-1-4503-4887-4.

Digital Library

[12]

GTFS. GTFS Realtime Specification. https://developers.google.com/transit/gtfsrealtime/reference/, 2020.

[13]

GTFS. GTFS static overview. https://developers.google.com/transit/gtfs, 2020b.

[14]

M. Amac Guvensan, Burak Dusun, Baris Can, and H. Irem Turkmen. A novel segment-based approach for improving classification performance of transport mode detection. Sensors, 18 (1), 2018.

[15]

Cristina Heghedus. PhD Forum: Forecasting Public Transit Using Neural Network Models. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pages 1--2, Hong Kong, China, May 2017. IEEE. ISBN 978-1-5090-6517-2.

[16]

Cristina Heghedus, Antorweep Chakravorty, and Chunming Rong. Neural Network Frameworks. Comparison on Public Transportation Prediction. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 842--849, Rio de Janeiro, Brazil, May 2019. IEEE. ISBN 978-1-72813-510-6.

[17]

IPCC. Climate Change 2014: Mitigation of Climate Change. Cambridge University Press, 2014. ISBN 978-1-107-05821-7.

[18]

Ranhee Jeong and R Rilett. Bus arrival time prediction using artificial neural network model. In Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No. 04TH8749), pages 988--993. IEEE, 2004.

[19]

Nikolas Julio, Ricardo Giesen, and Pedro Lizana. Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms. Research in Transportation Economics, 59: 250--257, 2016. ISSN 0739-8859. Competition and Ownership in Land Passenger Transport (selected papers from the Thredbo 14 conference).

[20]

Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2014.

[21]

Terence C. Lam and Kenneth A. Small. The value of time and reliability: measurement from a value pricing experiment. Transportation Research Part E: Logistics and Transportation Review, 37 (2): 231--251, 2001. ISSN 1366--5545. Advances in the Valuation of Travel Time Savings.

[22]

Ehsan Mazloumi, Geoff Rose, Graham Currie, and Majid Sarvi. An integrated framework to predict bus travel time and its variability using traffic flow data. Journal of Intelligent Transportation Systems, 15 (2): 75--90, 2011.

[23]

Claire McKnight, Herbert Levinson, Kaan Ozbay, Camille Kamga, and Robert Paaswell. Impact of traffic congestion on bus travel time in northern new jersey. Transportation Research Record, 1884: 27--35, 01 2004.

[24]

Daniel L Mendoza, Martin P Buchert, and John C Lin. Modeling net effects of transit operations on vehicle miles traveled, fuel consumption, carbon dioxide, and criteria air pollutant emissions in a mid-size US metro area: findings from Salt Lake City, UT. Environmental Research Communications, 1 (9): 091002, Sep 2019.

[25]

LiveTravelGeorg Osang, James Cook, Alex Fabrikant, and Marco Gruteser. Livetravel: Real-time matching of transit vehicle trajectories to transit routes at scale. In Proceedings of 2019 IEEE ITSC, pages 2244--2251, 2019.

Digital Library

[26]

Rahul Pathak, Christopher K. Wyczalkowski, and Xi Huang. Public transit access and the changing spatial distribution of poverty. Regional Science and Urban Economics, 66: 198--212, 2017. ISSN 0166-0462.

[27]

Thilo Reich, Marcin Budka, Derek Robbins, and David Hulbert. Survey of ETA prediction methods in public transport networks. arXiv: 1904.05037, 2019.

[28]

Kevin Sahr, Denis White, and A. Jon Kimerling. Geodesic discrete global grid systems. Cartography and Geographic Information Science, 30 (2): 121--134, 2003.

[29]

G. Salvo, G. Amato, and Pietro Zito. Bus speed estimation by neural networks to improve the automatic fleet management. European Transport, 37: 93--104, 2007.

[30]

Benjamin Solnik, Daniel Golovin, Greg Kochanski, John Elliot Karro, Subhodeep Moitra, and D. Sculley. Bayesian optimization for a better dessert. In Proceedings of the 2017 NIPS Workshop on Bayesian Optimization, December 9, 2017, Long Beach, USA, 2017. The workshop is BayesOpt 2017 NIPS Workshop on Bayesian Optimization December 9, 2017, Long Beach, USA.

[31]

Sun2016F. Sun, Y. Pan, J. White, and A. Dubey. Real-time and predictive analytics for smart public transportation decision support system. In 2016 IEEE International Conference on Smart Computing (SMARTCOMP), May 2016.

[32]

Yidan Sun, Guiyuan Jiang, Siew-Kei Lam, Shicheng Chen, and Peilan He. Bus Travel Speed Prediction using Attention Network of Heterogeneous Correlation Features. In Proceedings of ICDM. Society for Industrial and Applied Mathematics, May 2019. ISBN 978-1-61197-567-3. URL https://epubs.siam.org/doi/book/10.1137/1.9781611975673.

[33]

Transit App. "how we mapped the world's weirdest streets", 2015. URL "https://medium.com/transit-app/hello-nairobi-cc27bb5a73b7".

[34]

Transit Center. Who's on board. Technical report, Transit Center, 2016. URL http://transitcenter.org/wp-content/uploads/2016/07/Whos-On-Board-2016-7_12_2016.pdf.

[35]

W. Treethidtaphat, W. Pattara-Atikom, and S. Khaimook. Bus arrival time prediction at any distance of bus route using deep neural network model. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pages 988--992, Oct 2017.

Digital Library

[36]

William Vincent and Lisa Callaghan Jerram. The potential for bus rapid transit to reduce transportation-related co_2 emissions. Journal of Public Transportation, 9 (3): 12, 2006.

[37]

Jiafu Wan, Jianqi Liu, Zehui Shao, Athanasios V. Vasilakos, Muhammad Imran, and Keliang Zhou. Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors (Basel), 16 (1), 2016.

[38]

Dong Wang, Junbo Zhang, Wei Cao, Jian Li, and Yu Zheng. When will you arrive? Estimating travel time based on deep neural networks. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

[39]

Kari Edison Watkins, Brian Ferris, Alan Borning, G Scott Rutherford, and David Layton. Where is my bus? impact of mobile real-time information on the perceived and actual wait time of transit riders. Transportation Research Part A: Policy and Practice, 45 (8): 839--848, 2011.

[40]

Nate Wessel, Jeff Allen, and Steven Farber. Constructing a routable retrospective transit timetable from a real-time vehicle location feed and GTFS. Journal of Transport Geography, 62: 92--97, 2017.

[41]

Haitao Xu and Jing Ying. Bus arrival time prediction with real-time and historic data. Cluster Computing, 20 (4): 3099--3106, December 2017. ISSN 1573--7543.

Digital Library

[42]

Feng Zhang, Qing Shen, and Kelly J. Clifton. Examination of traveler responses to real-time information about bus arrivals using panel data. Transportation Research Record, 2082 (1): 107--115, 2008.

[43]

Chang-Jiang Zheng, Yi-Hua Zhang, and Xue-Jun Feng. Improved iterative prediction for multiple stop arrival time using a support vector machine. Transport, 27 (2): 158--164, 2012.

Cited By

Luo GZhang HYuan QLi JWang WWang F(2024)One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial–Temporal DependencyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325904535:8(11317-11331)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3259045
Chen LFang JYu ZTong YCao SWang LSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Data-driven Region Generation Framework for Spatiotemporal Transportation Service ManagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599760(3842-3854)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599760
Dai SWang JHuang CYu YDong J(2023)Dynamic Multi-View Graph Neural Networks for Citywide Traffic InferenceACM Transactions on Knowledge Discovery from Data10.1145/356475417:4(1-22)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3564754
Show More Cited By

Index Terms

BusTr: Predicting Bus Travel Times from Real-Time Traffic
1. Applied computing
  1. Operations research
    1. Forecasting
    2. Transportation
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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New York, NY, United States

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Published: 20 August 2020

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Luo GZhang HYuan QLi JWang WWang F(2024)One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial–Temporal DependencyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325904535:8(11317-11331)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3259045
Chen LFang JYu ZTong YCao SWang LSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Data-driven Region Generation Framework for Spatiotemporal Transportation Service ManagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599760(3842-3854)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599760
Dai SWang JHuang CYu YDong J(2023)Dynamic Multi-View Graph Neural Networks for Citywide Traffic InferenceACM Transactions on Knowledge Discovery from Data10.1145/356475417:4(1-22)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3564754
Liu ZLi MLi MLiao LLi K(2023)An Efficient Hierarchical-Reduction Architecture for Aggregation in Route Travel Time EstimationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.329284134:9(2541-2552)Online publication date: Sep-2023
https://doi.org/10.1109/TPDS.2023.3292841
Tang YQu AChow ALam WWong SMa WAl Hasan MXiong L(2022)Domain Adversarial Spatial-Temporal NetworkProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557294(1905-1915)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557294
Xue QYe QHu HZhu YWang J(2022)DDRM: A Continual Frequency Estimation Mechanism with Local Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3177721(1-1)Online publication date: 2022
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Rong YXu ZLiu JLiu HDing JLiu XLuo WZhang CGao J(2022)Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-Source DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.321017023:12(24524-24539)Online publication date: Dec-2022
https://doi.org/10.1109/TITS.2022.3210170
Chen KChu GYang XShi YLei KDeng M(2022)HSETA: A Heterogeneous and Sparse Data Learning Hybrid Framework for Estimating Time of ArrivalIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.317091723:11(21873-21884)Online publication date: Nov-2022
https://doi.org/10.1109/TITS.2022.3170917
Bannur CBhat CGoutham GMamatha H(2022)General Transit Feed Specification Assisted Effective Traffic Congestion Prediction Using Decision Trees and Recurrent Neural Networks2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)10.1109/ICDDS56399.2022.10037408(1-6)Online publication date: 2-Dec-2022
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Ma JChan JRajasegarar SLeckie C(2022)Multi-attention graph neural networks for city-wide bus travel time estimation using limited dataExpert Systems with Applications10.1016/j.eswa.2022.117057202(117057)Online publication date: Sep-2022
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