research-article

City-scale traffic simulation from digital footprints

Authors:

Aonghus Lawlor,

Alexei PozdnoukhovAuthors Info & Claims

UrbComp '12: Proceedings of the ACM SIGKDD International Workshop on Urban Computing

Pages 47 - 54

https://doi.org/10.1145/2346496.2346505

Published: 12 August 2012 Publication History

Abstract

This paper introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national road survey, the developed micro-simulation reproduces not only the journey statistics but also the traffic volumes at main road segments with surprising accuracy.

References

[1]

National Travel Survey Report. Central Statistics Office, Government of Ireland, 2009.

[2]

J. Abraham and J. Hunt. Specification and estimation of nested logit model of home, workplaces, and commuter mode choices by multiple-worker households. Transportation Research Record: Journal of the Transportation Research Board, 1606(-1):17--24, 1997.

[3]

K. W. Axhausen. Social networks, mobility biographies, and travel: survey challenges. Environment and Planning B: Planning and Design, 35:981--996, 2008.

[4]

M. Balmer, K. Meister, M. Rieser, K. Nagel, and K. Axhausen. Agent-based simulation of travel demand: Structure and computational performance of MATSim-T. ETH, Eidgenössische Technische Hochschule Zürich, IVT Institut für Verkehrsplanung und Transportsysteme, 2008.

[5]

M. Balmer, B. Raney, and K. Nagel. Adjustment of activity timing and duration in an agent-based traffic flow simulation. Progress in activity-based analysis, pages 91--114, 2005.

[6]

M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz. SUMO - simulation of urban mobility: An overview. In SIMUL 2011, The Third International Conference on Advances in System Simulation, pages 63--68, Barcelona, Spain, October 2011.

[7]

D. Brockmann, L. Hufnagel, and T. Geisel. The scaling laws of human travel. Nature, 439(7075):462--465, 2006.

[8]

Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. Exploring millions of footprints in location sharing services. In ICWSM, 2011.

[9]

E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pages 1082--1090, New York, NY, USA, 2011. ACM.

Digital Library

[10]

N. Eluru, C. Bhat, R. Pendyala, and K. Konduri. A joint flexible econometric model system of household residential location and vehicle fleet composition usage choices. Transportation, 37(4):603--626, 2010.

[11]

P. Expert, T. S. Evans, V. D. Blondel, and R. Lambiotte. Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences, 108(19):7663--7668, May 2011.

[12]

B. Flyvbjerg, M. K. Skamris Holm, and S. L. Buhl. Inaccuracy in traffic forecasts. Transport Reviews, 26(1):1--24, 2006.

[13]

A. S. Fotheringham. A new set of spatial-interaction models: the theory of competing destinations. 1(15):15--36, 1983.

[14]

A. Horni, D. Scott, M. Balmer, and K. Axhausen. Location choice modeling for shopping and leisure activities with matsim. Transportation Research Record: Journal of the Transportation Research Board, 2135(-1):87--95, 2009.

[15]

A. Lawlor, C. Coffey, R. McGrath, and A. Pozdnoukhov. Stratification structure of urban habitats, June 2012. Pervasive Urban Applications workshop (PURBA'12) at PERVASIVE'2012.

[16]

B. Lee and P. Waddell. Residential mobility and location choice: a nested logit model with sampling of alternatives. Transportation, 37(4):587--601, 2010.

[17]

N. Lefebvre and M. Balmer. Fast shortest path computation in time-dependent traffic networks. ETH, Eidgenössische Technische Hochschule Zürich, IVT, Institut für Verkehrsplanung und Transportsysteme, 2007.

[18]

D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, and A. Tomkins. Geographic routing in social networks. Proceedings of the National Academy of Sciences of the United States of America, 102(33):11623--11628, 2005.

[19]

A. Noulas, S. Scellato, R. Lambiotte, M. Pontil, and C. Mascolo. A tale of many cities: universal patterns in human urban mobility. arXiv:1108.5355v4 {physics.soc-ph}, 2011.

[20]

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, LBSN '11, pages 8:1--8:8, New York, NY, USA, 2011. ACM.

Digital Library

[21]

A. Sadilek, H. Kautz, and J. P. Bigham. Finding your friends and following them to where you are. In Proceedings of the fifth ACM international conference on Web search and data mining, WSDM '12, pages 723--732, New York, NY, USA, Feb. 2012. ACM.

Digital Library

[22]

F. Simini, M. C. Gonzalez, A. Maritan, and A.-L. Barabasi. A universal model for mobility and migration patterns. Nature, (484):96--100, 2012.

[23]

C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 327(5968):1018--1021, Feb. 2010.

[24]

S. A. Stouffer. Intervening opportunities: A theory relating mobility and distance. American Sociological Review, 5(6):845--867, 1940.

[25]

D. Veneziano and M. C. Gonzalez. Trip length distribution under multiplicative spatial models of supply and demand: Theory and sensitivity analysis. CoRR, abs/1101.3719, 2011.

[26]

F. Walsh and A. Pozdnoukhov. Spatial structure and dynamics of urban communities, June 2011. The First Workshop on Pervasive Urban Applications (PURBA).

[27]

A. G. Wilson. Entropy in Urban and Regional Modelling. Pion, London, United Kingdom, 1970.

[28]

G. K. Zipf. The p1 P2/D hypothesis: On the intercity movement of persons. American Sociological Review, 11(6), 1946.

Cited By

Falek AGallais APelsser CJulien STheoleyre F(2021)To re-route, or not to re-route: Impact of real-time re-routing in urban road networksJournal of Intelligent Transportation Systems10.1080/15472450.2020.180734526:2(198-212)Online publication date: 1-Jun-2021
https://doi.org/10.1080/15472450.2020.1807345
Hu WYao ZYang SChen SJin P(2019)Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social NetworksMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10928-8_6(88-104)Online publication date: 23-Jan-2019
https://doi.org/10.1007/978-3-030-10928-8_6
Forghani MKarimipour F(2018)Interplay between urban communities and human‐crowd mobility: A study using contributed geospatial data sourcesTransactions in GIS10.1111/tgis.1246522:4(1008-1028)Online publication date: 28-Aug-2018
https://doi.org/10.1111/tgis.12465
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City-scale traffic simulation from digital footprints

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cover image ACM Conferences

UrbComp '12: Proceedings of the ACM SIGKDD International Workshop on Urban Computing

August 2012

176 pages

ISBN:9781450315425

DOI:10.1145/2346496

General Chair:
Ouri E. Wolfson
University of Illinois at Chicago
,
Program Chair:
Yu Zheng
Microsoft Research Asia, China

Copyright © 2012 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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Published: 12 August 2012

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12, 2012

Beijing, China

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Cited By

Falek AGallais APelsser CJulien STheoleyre F(2021)To re-route, or not to re-route: Impact of real-time re-routing in urban road networksJournal of Intelligent Transportation Systems10.1080/15472450.2020.180734526:2(198-212)Online publication date: 1-Jun-2021
https://doi.org/10.1080/15472450.2020.1807345
Hu WYao ZYang SChen SJin P(2019)Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social NetworksMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10928-8_6(88-104)Online publication date: 23-Jan-2019
https://doi.org/10.1007/978-3-030-10928-8_6
Forghani MKarimipour F(2018)Interplay between urban communities and human‐crowd mobility: A study using contributed geospatial data sourcesTransactions in GIS10.1111/tgis.1246522:4(1008-1028)Online publication date: 28-Aug-2018
https://doi.org/10.1111/tgis.12465
Kashiyama TPang YSekimoto Y(2017)Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areasTransportation Research Part C: Emerging Technologies10.1016/j.trc.2017.09.01685(249-267)Online publication date: Dec-2017
https://doi.org/10.1016/j.trc.2017.09.016
Hu WJin P(2017)An adaptive hawkes process formulation for estimating time-of-day zonal trip arrivals with location-based social networking check-in dataTransportation Research Part C: Emerging Technologies10.1016/j.trc.2017.02.00279(136-155)Online publication date: Jun-2017
https://doi.org/10.1016/j.trc.2017.02.002
Sacco DMotta GYou LBertolazzo NCarini FMa T(2017)Smart cities, urban sensing, and big data: mining geo-location in social networksBig Data and Smart Service Systems10.1016/B978-0-12-812013-2.00005-8(59-84)Online publication date: 2017
https://doi.org/10.1016/B978-0-12-812013-2.00005-8
Wakamiya SLee RKawai YSumiya KChoi JHan SLee JPark TLeung CKim YLee Y(2015)Twitter-based Urban Area Characterization by Non-negative Matrix FactorizationProceedings of the 2015 International Conference on Big Data Applications and Services10.1145/2837060.2837079(128-135)Online publication date: 20-Oct-2015
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You LMotta GSacco DMa T(2014)Social data analysis framework in cloud and Mobility Analyzer for Smarter CitiesProceedings of 2014 IEEE International Conference on Service Operations and Logistics, and Informatics10.1109/SOLI.2014.6960700(96-101)Online publication date: Oct-2014
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Tarasov AKling FPozdnoukhov AKoonin SWolfson OZheng Y(2013)Prediction of user location using the radiation model and social check-insProceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing10.1145/2505821.2505833(1-7)Online publication date: 11-Aug-2013
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McArdle GKitchin R(undefined)Improving the Veracity of Open and Real-Time Urban DataSSRN Electronic Journal10.2139/ssrn.2643430
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