research-article

Analytics on public transport delays with spatial big data

Authors:

Jayanth Raghothama,

Vinutha Magal Shreenath,

Sebastiaan MeijerAuthors Info & Claims

BigSpatial '16: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data

Pages 28 - 33

https://doi.org/10.1145/3006386.3006387

Published: 31 October 2016 Publication History

Abstract

The increasing pervasiveness of location-aware technologies is leading to the rise of large, spatio-temporal datasets and to the opportunity of discovering usable knowledge about the behaviors of people and objects. Applied extensively in transportation, spatial big data and its analytics can deliver useful insights on a number of different issues such as congestion, delays, public transport reliability and so on. Predominantly studied for its use in operational management, spatial big data can be used to provide insight in strategic applications as well, from planning and design to evaluation and management. Such large scale, streaming spatial big data can be used in the improvement of public transport, for example the design of public transport networks and reliability. In this paper, we analyze GTFS data from the cities of Stockholm and Rome to gain insight on the sources and factors influencing public transport delays in the cities. The analysis is performed on a combination of GTFS data with data from other sources. The paper points to key issues in the analysis of real time data, driven by the contextual setting in the two cities.

References

[1]

Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G. and Portugali, Y. 2012. Smart cities of the future. The European Physical Journal Special Topics. 214, 1 (Dec. 2012), 481--518.

[2]

Becker, R., Volinsky, C., Cáceres, R., Hanson, K., Isaacman, S., Loh, J.M., Martonosi, M., Rowland, J., Urbanek, S. and Varshavsky, A. 2013. Human mobility characterization from cellular network data. Communications of the ACM. 56, 1 (Jan. 2013), 74.

Digital Library

[3]

Calabrese, F., Colonna, M., Lovisolo, P., Parata, D. and Ratti, C. 2011. Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome. Trans. Intell. Transport. Sys. 12, 1 (Mar. 2011), 141--151.

Digital Library

[4]

Curwell, S., Deakin, M., Cooper, I., Paskaleva-Shapira, K., Ravetz, J. and Babicki, D. 2005. Citizens' expectations of information cities: implications for urban planning and design. Building Research & Information. 33, 1 (Jan. 2005), 55--66.

[5]

Greenfield, A. 2006. Readings from Everyware: The dawning age of ubiquitous computing. ITP v0. 1, (2006).

[6]

Grotenhuis, J.-W., Wiegmans, B.W. and Rietveld, P. 2007. The desired quality of integrated multimodal travel information in public transport: Customer needs for time and effort savings. Transport Policy. 14, 1 (Jan. 2007), 27--38.

[7]

Hasan, S., Schneider, C.M., Ukkusuri, S.V. and González, M.C. 2013. Spatiotemporal patterns of urban human mobility. Journal of Statistical Physics. 151, 1--2 (2013), 304--318.

[8]

Hasan, S., Zhan, X. and Ukkusuri, S.V. 2013. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (2013), 6.

Digital Library

[9]

Holleczek, T., The Anh, D., Yin, S., Jin, Y., Antonatos, S., Goh, H.L., Low, S. and Shi-Nash, A. 2015. Traffic Measurement and Route Recommendation System for Mass Rapid Transit (MRT). Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2015), 1859--1868.

Digital Library

[10]

Hunter, T., Abbeel, P. and Bayen, A. 2014. The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data. IEEE Transactions on Intelligent Transportation Systems. 15, 2 (Apr. 2014), 507--529.

[11]

Jin, J.G., Teo, K.M. and Sun, L. 2013. Disruption response planning for an urban mass rapid transit network. transportation research board 92nd annual meeting, Washington DC (2013).

[12]

Kitchin, R. 2013. The real-time city? Big data and smart urbanism. GeoJournal. 79, 1 (Nov. 2013), 1--14.

[13]

Reades, J., Calabrese, F., Sevtsuk, A. and Ratti, C. 2007. Cellular Census: Explorations in Urban Data Collection. IEEE Pervasive Computing. 6, 3 (Jul. 2007), 30--38.

Digital Library

[14]

Shekhar, S., Gunturi, V., Evans, M.R. and Yang, K. 2012. Spatial Big-data Challenges Intersecting Mobility and Cloud Computing. Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access (New York, NY, USA, 2012), 1--6.

Digital Library

[15]

Song, C., Qu, Z., Blumm, N. and Barabási, A.-L. 2010. Limits of Predictability in Human Mobility. Science. 327, 5968 (Feb. 2010), 1018--1021.

[16]

Sun, L., Lee, D.-H., Erath, A. and Huang, X. 2012. Using Smart Card Data to Extract Passenger's Spatio-temporal Density and Train's Trajectory of MRT System. Proceedings of the ACM SIGKDD International Workshop on Urban Computing (New York, NY, USA, 2012), 142--148.

Digital Library

[17]

Vatsavai, R.R., Ganguly, A., Chandola, V., Stefanidis, A., Klasky, S. and Shekhar, S. 2012. Spatiotemporal Data Mining in the Era of Big Spatial Data: Algorithms and Applications. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (New York, NY, USA, 2012), 1--10.

Digital Library

[18]

List of public transit agencies publishing transit data. https://code.google.com/archive/p/googletransitdatafeed/wikis/PublicFeeds.wiki

Cited By

KASAHARA YNAKAYA TISODA Y(2022)Analysis of Spatio-Temporal Patterns of Public Bus Delay Times Using GTFS Data:A Case Study of Buses of Sendai City Transportation BureauGTFSデータを用いた公共バスの遅延時間に関する時空間パターンの分析── 仙台市営バスを事例に ──Quarterly Journal of Geography10.5190/tga.73.4_26473:4(264-273)Online publication date: 2022
https://doi.org/10.5190/tga.73.4_264
Wrona PGrzenda MLuckner M(2022)Streaming Detection of Significant Delay Changes in Public Transport SystemsComputational Science – ICCS 202210.1007/978-3-031-08760-8_41(486-499)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08760-8_41
Gramacki PWoźniak SSzymański P(2021)gtfs2vecProceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data10.1145/3486640.3491392(5-12)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486640.3491392
Show More Cited By

Index Terms

Analytics on public transport delays with spatial big data
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
    2. Spatial-temporal systems
      1. Global positioning systems

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Published In

cover image ACM Other conferences

BigSpatial '16: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data

October 2016

65 pages

ISBN:9781450345811

DOI:10.1145/3006386

Editors:
Varun Chandola
State University of New York at Buffalo, NY
,
Ranga Raju Vatsavai
North Carolina State University, NC

Copyright © 2016 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 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]

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Association for Computing Machinery

New York, NY, United States

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Published: 31 October 2016

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SIGSPATIAL'16

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31, 2016

California, Burlingame

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BigSpatial '16 Paper Acceptance Rate 8 of 14 submissions, 57%;

Overall Acceptance Rate 32 of 58 submissions, 55%

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Cited By

KASAHARA YNAKAYA TISODA Y(2022)Analysis of Spatio-Temporal Patterns of Public Bus Delay Times Using GTFS Data:A Case Study of Buses of Sendai City Transportation BureauGTFSデータを用いた公共バスの遅延時間に関する時空間パターンの分析── 仙台市営バスを事例に ──Quarterly Journal of Geography10.5190/tga.73.4_26473:4(264-273)Online publication date: 2022
https://doi.org/10.5190/tga.73.4_264
Wrona PGrzenda MLuckner M(2022)Streaming Detection of Significant Delay Changes in Public Transport SystemsComputational Science – ICCS 202210.1007/978-3-031-08760-8_41(486-499)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08760-8_41
Gramacki PWoźniak SSzymański P(2021)gtfs2vecProceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data10.1145/3486640.3491392(5-12)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486640.3491392
Raczycki KSzymański MYeliseyenka YSzymański PKajdanowicz T(2021)Spatial data mining of public transport incidents reported in social mediaProceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/3486629.3490696(1-8)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3486629.3490696
Aksehirli ELi Y(2018)Predicting MRT Trips in Singapore by Creating a Mobility Behavior Model Based on GSM Data2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00098(632-639)Online publication date: Nov-2018
https://doi.org/10.1109/ICDMW.2018.00098
El Alaoui IGahi YMessoussi RTodoskoff AKobi A(2018)Big Data Analytics: A Comparison of Tools and ApplicationsInnovations in Smart Cities and Applications10.1007/978-3-319-74500-8_54(587-601)Online publication date: 21-Mar-2018
https://doi.org/10.1007/978-3-319-74500-8_54
Kunama NWorapan MPhithakkitnukoon SDemissie MLee STakayama LTruong K(2017)GTFS-VizProceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers10.1145/3123024.3124415(388-396)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3123024.3124415

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