Belgium through the Lens of Rail Travel Requests: Does Geography Still Matter?

This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the temporal and regional variations of the travel demand by train in Belgium. Travel requests are collected over a two-month period and consist of origin-destination flows between stations operated by the Belgian national railway company in 2016. The Louvain method is applied to detect communities of tightly-connected stations. Results show the influence of both the urban and network structures on the spatial organization of the clusters. We also further discuss the implications of the observed temporal and regional variations of these clusters for transportation travel demand and planning.

[1]  David M Levinson,et al.  Accessibility Impacts of High Speed Rail , 2012 .

[2]  F. Leisch,et al.  Time use in travel surveys and time use surveys – Two sides of the same coin? , 2015 .

[3]  Effet de frontière et interaction spatiale , 2001 .

[4]  Klaus Spiekermann,et al.  Trans-European Networks and Unequal Accessibility , 1996 .

[5]  Soong Moon Kang,et al.  Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows , 2010, PloS one.

[6]  J. Bergstrand.  The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence , 1985 .

[7]  Hadrien Commenges,et al.  Spatial Position Models , 2015 .

[8]  Mike Coombes,et al.  From City-region Concept to Boundaries for Governance: The English Case , 2014 .

[9]  R. Kitchin.  The real-time city? Big data and smart urbanism , 2013 .

[10]  Vincent A. Traag,et al.  Significant Scales in Community Structure , 2013, Scientific Reports.

[11]  Bernadette Merenne-Schoumaker,et al.  Noyaux d’habitat et Régions urbaines dans une Belgique urbanisée , 2009 .

[12]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Fahui Wang.  Explaining Intraurban Variations of Commuting by Job Proximity and Workers' Characteristics , 2001 .

[14]  Nikolas Thomopoulos,et al.  ICT for transport: Opportunities and threats , 2015 .

[15]  Isabelle Thomas,et al.  Commuting in Belgian metropolitan areas: The power of the Alonso-Muth model , 2010 .

[16]  B. Slack,et al.  The Geography of Transport Systems , 2006 .

[17]  Dominique Peeters,et al.  Revisiting the extension of the Brussels urban agglomeration: New methods, new data⋯ new results? , 2012 .

[18]  Benjamin H. Good,et al.  Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Rik Van de Walle,et al.  What Public Transit API Logs Tell Us about Travel Flows , 2016, WWW.

[20]  D. Huff.  A Probabilistic Analysis of Shopping Center Trade Areas , 1963 .

[21]  Cathy Macharis,et al.  How persuasive is 'free' public transport?: A survey among commuters in the Brussels Capital Region , 2008 .

[22]  Kay W. Axhausen,et al.  Immobility and mobility seen through trip-based versus time-use surveys , 2007 .

[23]  L. Wintr,et al.  Commuting flows across bordering regions: a note , 2009 .

[24]  L. Beauguitte,et al.  Modelling Attractiveness of Global Places A worldwide survey on 9000 undergraduate students∗ , 2010 .

[25]  R. Cervero,et al.  Polycentrism, Commuting, and Residential Location in the San Francisco Bay Area , 1997, Environment & planning A.

[26]  Fahui Wang,et al.  Modeling Commuting Patterns in Chicago in a GIS Environment: A Job Accessibility Perspective , 2000 .

[27]  Ana L. N. Fred,et al.  Robust data clustering , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[28]  I. Thomas,et al.  Bruxelles au sein de l'économie belge : un bilan , 2010 .

[29]  Moshe Givoni,et al.  Development and Impact of the Modern High‐speed Train: A Review , 2006 .

[30]  I. Thomas,et al.  Regions and borders of mobile telephony in Belgium and in the Brussels metropolitan zone , 2010 .

[31]  V A Traag,et al.  Narrow scope for resolution-limit-free community detection. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Santo Fortunato,et al.  Finding Statistically Significant Communities in Networks , 2010, PloS one.

[33]  Frank Witlox,et al.  Rail Commuting to Workplaces in Belgium: A Multilevel Approach , 2012 .

[34]  Samo Drobne,et al.  Methods for delineation of functional regions using data on commuters , 2010 .

[35]  Jean-Charles Delvenne,et al.  The stability of a graph partition: A dynamics-based framework for community detection , 2013, ArXiv.

[36]  Michael Wegener,et al.  The Shrinking Continent: New Time—Space Maps of Europe , 1994 .

[37]  Jean-Claude Thill,et al.  Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing , 2013, Comput. Environ. Urban Syst..

[38]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[39]  Barbara Martini,et al.  The Data Revolution. Big Data, Open Data, Data Infrastructures and Their Consequences , 2016 .

[40]  R. Vickerman.  High-speed rail in Europe: experience and issues for future development , 1997 .

[41]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[42]  Luc Int Panis,et al.  Cycle commuting in Belgium: Spatial determinants and ‘re-cycling’ strategies , 2011 .

[43]  Padraig Cunningham,et al.  Community detection: effective evaluation on large social networks , 2014, J. Complex Networks.

[44]  Jungyul Sohn.  Are commuting patterns a good indicator of urban spatial structure , 2005 .

[45]  C. Farmer,et al.  Network-Based Functional Regions , 2011 .

[46]  Claire Dujardin.  Effet de frontière et interaction spatiale : les migrations alternantes et la frontière linguistique en Belgique , 2001 .