skip to main content
10.1145/2939672.2939723acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics

Published: 13 August 2016 Publication History

Abstract

Given the changing dynamics of mobility patterns and rapid growth of cities, transport agencies seek to respond more rapidly to needs of the public with the goal of offering an effective and competitive public transport system. A more data-centric approach for transport planning is part of the evolution of this process. In particular, the vast penetration of mobile phones provides an opportunity to monitor and derive insights on transport usage. Real time and historical analyses of such data can give a detailed understanding of mobility patterns of people and also suggest improvements to current transit systems. On its own, however, mobile geolocation data has a number of limitations. We thus propose a joint telco-and-farecard-based learning approach to understanding urban mobility. The approach enhances telecommunications data by leveraging it jointly with other sources of real-time data. The approach is illustrated on the First- and last-mile problem as well as route choice estimation within a densely-connected train network.

References

[1]
R. Becker, R. Cáceres, K. Hanson, S. Isaacman, J. M. Loh, M. Martonosi, J. Rowland, S. Urbanek, A. Varshavsky, and C. Volinsky. Human mobility characterization from cellular network data. Communications of the ACM, 56(1):74--82, 2013.
[2]
Beeline. Beeline. https://www.beeline.sg/.
[3]
F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti. Real-time urban monitoring using cell phones: A case study in rome. Intelligent Transportation Systems, IEEE Transactions on, 12(1):141--151, 2011.
[4]
N. Eagle and A. S. Pentland. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, 63(7):1057--1066, 2009.
[5]
Elastic. Elastic search. https://www.elastic.co/products/elasticsearch.
[6]
A. Flajolet, S. Blandin, and P. Jaillet. Robust Adaptive Routing Under Uncertainty. submitted to Operations Research, 2016.
[7]
A. Foundation. Apache hive. https://hive.apache.org.
[8]
F. Girardin, F. Calabrese, F. D. Fiore, C. Ratti, and J. Blat. Digital footprinting: Uncovering tourists with user-generated content. Pervasive Computing, IEEE, 7(4):36--43, 2008.
[9]
A. Guttman. R-trees: A dynamic index structure for spatial searching. SIGMOD Rec., 14(2):47--57, June 1984.
[10]
S. Hasan, C. M. Schneider, S. V. Ukkusuri, and M. C. González. Spatiotemporal patterns of urban human mobility. Journal of Statistical Physics, 151(1--2):304--318, 2013.
[11]
S. Hasan, X. Zhan, and S. V. Ukkusuri. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, page 6. ACM, 2013.
[12]
Y. He, S. Blandin, L. Wynter, and B. Trager. Analysis and real-time prediction of local incident impact on transportation networks. In Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, pages 158--166. IEEE, 2014.
[13]
T. Holleczek, S. Yin, Y. Jin, S. Antonatos, H. L. Goh, S. Low, A. Shi-Nash, et al. Traffic measurement and route recommendation system for mass rapid transit (mrt). In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1859--1868. ACM, 2015.
[14]
T. Holleczek, L. Yu, J. K. Lee, O. Senn, C. Ratti, and P. Jaillet. Detecting weak public transport connections from cellphone and public transport data. In Proceedings of the 2014 International Conference on Big Data Science and Computing, page 9. ACM, 2014.
[15]
T. Hunter, P. Abbeel, and A. Bayen. The path inference filter: model-based low-latency map matching of probe vehicle data. Intelligent Transportation Systems, IEEE Transactions on, 15(2):507--529, 2014.
[16]
J. G. Jin, K. M. Teo, and L. Sun. Disruption response planning for an urban mass rapid transit network. In transportation research board 92nd annual meeting, Washington DC, 2013.
[17]
J. Jonas. Analytic superfood. http://jeffjonas.typepad.com/jeff_jonas/2009/08/your-movements-speak-for-themselves-spacetimetravel-data-is-analytic-superfood.html.
[18]
F. Kling and A. Pozdnoukhov. When a city tells a story: urban topic analysis. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pages 482--485. ACM, 2012.
[19]
V. Kolar, S. Ranu, A. P. Subramainan, Y. Shrinivasan, A. Telang, R. Kokku, and S. Raghavan. People in motion: Spatio-temporal analytics on call detail records. In Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on, pages 1--4. IEEE, 2014.
[20]
A. Krause, E. Horvitz, A. Kansal, and F. Zhao. Toward community sensing. In Proceedings of the 7th international conference on Information processing in sensor networks, pages 481--492. IEEE Computer Society, 2008.
[21]
R. K.-W. Lee, T. S. Kam, et al. Time-series data mining in transportation: A case study on singapore public train commuter travel patterns. International Journal of Engineering and Technology, 6(5):431, 2014.
[22]
T. Möller and B. Trumbore. Fast, minimum storage ray/triangle intersection. In ACM SIGGRAPH 2005 Courses, SIGGRAPH '05, New York, NY, USA, 2005. ACM.
[23]
P. Newson and J. Krumm. Hidden markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '09, pages 336--343, New York, NY, USA, 2009. ACM.
[24]
N. B. Othman, E. F. Legara, V. Selvam, and C. Monterola. Simulating congestion dynamics of train rapid transit using smart card data. Procedia Computer Science, 29:1610--1620, 2014.
[25]
A. D. Patire, M. Wright, B. Prodhomme, and A. M. Bayen. How much gps data do we need? Transportation Research Part C: Emerging Technologies, 2015.
[26]
F. C. Pereira, F. Rodrigues, and M. Ben-Akiva. Using data from the web to predict public transport arrivals under special events scenarios. Journal of Intelligent Transportation Systems, 19(3):273--288, 2015.
[27]
A. Pozdnoukhov and C. Kaiser. Space-time dynamics of topics in streaming text. In Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks, pages 1--8. ACM, 2011.
[28]
J. Reades, F. Calabrese, A. Sevtsuk, and C. Ratti. Cellular census: Explorations in urban data collection. Pervasive Computing, IEEE, 6(3):30--38, 2007.
[29]
A. Sadilek and J. Krumm. Far out: Predicting long-term human mobility. In AAAI, 2012.
[30]
S. Samaranayake, S. Blandin, and A. Bayen. A tractable class of algorithms for reliable routing in stochastic networks. International Symposium on Transportation and Trafic Theory (ISTTT), Procedia Social and Behavioral Sciences, 17:341--363, 2011.
[31]
C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 327(5968):1018--1021, 2010.
[32]
L. Sun, D.-H. Lee, A. Erath, and X. Huang. 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, pages 142--148. ACM, 2012.
[33]
X. Tang, S. Blandin, and L. Wynter. A fast decomposition approach for transportation network optimization. In World Congress, volume 19, pages 5109--5114, 2014.
[34]
A. Vaccari, L. Liu, A. Biderman, C. Ratti, F. Pereira, J. Oliveirinha, and A. Gerber. A holistic framework for the study of urban traces and the profiling of urban processes and dynamics. In Intelligent Transportation Systems, 2009. ITSC'09. 12th International IEEE Conference on, pages 1--6. IEEE, 2009.
[35]
M. Vlachos, G. Kollios, and D. Gunopulos. Discovering similar multidimensional trajectories. In Data Engineering, 2002. Proceedings. 18th International Conference on, pages 673--684, 2002.
[36]
D. B. Work, O.-P. Tossavainen, S. Blandin, A. M. Bayen, T. Iwuchukwu, and K. Tracton. An ensemble kalman filtering approach to highway traffic estimation using gps enabled mobile devices. In Decision and Control, 2008. CDC 2008. 47th IEEE Conference on, pages 5062--5068. IEEE, 2008.
[37]
Y. Zheng. Trajectory data mining: An overview. ACM Transaction on Intelligent Systems and Technology, September 2015.

Cited By

View all
  • (2024)A Transport Mode Detection Framework Based on Mobile Phone Signaling Data Combined with Bus GPS DataMathematics10.3390/math1223384312:23(3843)Online publication date: 5-Dec-2024
  • (2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
  • (2024)Review of Mobile Phone Data in Travel Characteristics RecognitionReliability Evaluation and Its Influence on Traffic Application10.1007/978-981-97-7950-5_2(13-30)Online publication date: 17-Nov-2024
  • Show More Cited By

Index Terms

  1. Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Conferences
          KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
          August 2016
          2176 pages
          ISBN:9781450342322
          DOI:10.1145/2939672
          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 ACM 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]

          Sponsors

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 13 August 2016

          Permissions

          Request permissions for this article.

          Check for updates

          Author Tags

          1. big data
          2. mapmatching
          3. mobility
          4. public transport route choice

          Qualifiers

          • Research-article

          Conference

          KDD '16
          Sponsor:

          Acceptance Rates

          KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
          Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

          Upcoming Conference

          KDD '25

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • Downloads (Last 12 months)42
          • Downloads (Last 6 weeks)5
          Reflects downloads up to 15 Jan 2025

          Other Metrics

          Citations

          Cited By

          View all
          • (2024)A Transport Mode Detection Framework Based on Mobile Phone Signaling Data Combined with Bus GPS DataMathematics10.3390/math1223384312:23(3843)Online publication date: 5-Dec-2024
          • (2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
          • (2024)Review of Mobile Phone Data in Travel Characteristics RecognitionReliability Evaluation and Its Influence on Traffic Application10.1007/978-981-97-7950-5_2(13-30)Online publication date: 17-Nov-2024
          • (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
          • (2022)JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.309154223:7(9146-9156)Online publication date: Jul-2022
          • (2022)Crowd Flow Prediction for Social Internet-of-Things Systems Based on the Mobile Network Big DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30628849:1(267-278)Online publication date: Feb-2022
          • (2022)Sensing Multi-modal Mobility Patterns: A Case Study of Helsinki using Bluetooth Beacons and a Mobile Application2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020578(2007-2016)Online publication date: 17-Dec-2022
          • (2022)First Mile/Last Mile Problems in Smart and Sustainable Cities: A Case Study in Stockholm CountyJournal of Urban Technology10.1080/10630732.2022.2033949(1-23)Online publication date: 15-Feb-2022
          • (2022)Activity location recognition from mobile phone data using improved HAC and Bi‐LSTMIET Intelligent Transport Systems10.1049/itr2.1221116:10(1364-1379)Online publication date: 3-Jun-2022
          • (2022)TRAWEL: A Transportation and Wellbeing Conceptual Framework for Broadening the Understanding of Quality of LifeQuantifying Quality of Life10.1007/978-3-030-94212-0_24(553-581)Online publication date: 14-Apr-2022
          • Show More Cited By

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          Media

          Figures

          Other

          Tables

          Share

          Share

          Share this Publication link

          Share on social media