A tool for measuring and visualizing connectivity of transit stop, route and transfer center in a multimodal transportation network

详细信息    查看全文

• 作者：Sabyasachee Mishra (1)
  Timothy F. Welch (2)
  Paul M. Torrens (3)
  Cheng Fu (3)
  Haojie Zhu (4)
  Eli Knaap (5)
  
  1. Department of Civil and Environmental Engineering and Intermodal Freight Transportation Institute ; University of Memphis ; 3815 Central Avenue ; Memphis ; TN ; 38152 ; USA
  2. School of City and Regional Planning ; Georgia Institute of Technology ; 245 4th Street NW ; Suite 204 ; Atlanta ; GA ; 30332 ; USA
  3. Department of Geographical Sciences and Institute for Advanced Computer Studies ; University of Maryland ; 1104 LeFrak Hall ; College Park ; MD ; 20742 ; USA
  4. Department of Geographical Sciences ; University of Maryland ; 1104 LeFrak Hall ; College Park ; MD ; 20742 ; USA
  5. National Center for Smart Growth Research and Education ; 054 Preinkert Fieldhouse ; University of Maryland ; College Park ; MD ; 20742 ; USA
  
• 关键词：Transit connectivity ; Graph theory ; Public transportation ; Multimodal transportation system ; GIS
• 刊名：Public Transport
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：7
• 期：1
• 页码：77-99
• 全文大小：15,762 KB
• 参考文献：1. Ahmed A, Dwyer T, Forster M et al (2006) GEOMI: GEOmetry for Maximum Insight. In: Healy P, Nikolov NS (eds) Graph drawing. Springer, Berlin, pp 468鈥?79 CrossRef
  2. Aittokallio T, Schwikowski B (2006) Graph-based methods for analysing networks in cell biology. Brief Bioinform 7:243鈥?55. doi:10.1093/bib/bbl022 CrossRef
  3. Barthlemy M (2004) Betweenness centrality in large complex networks. Eur Phys J B Condens Matter 38:163鈥?68. doi:10.1140/epjb/e2004-00111-4
  4. Bell DC, Atkinson JS, Carlson JW (1999) Centrality measures for disease transmission networks. Soc Netw 21:1鈥?1 pii: 16/S0378-8733(98)00010-0 CrossRef
  5. Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Netw 29:555鈥?64 pii: 16/j.socnet.2007.04.002 CrossRef
  6. Bonacich P, Lloyd P (2001) Eigenvector-like measures of centrality for asymmetric relations. Soc Netw 23:191鈥?01 pii: 16/S0378-8733(01)00038-7 CrossRef
  7. Borgatti SP (2005) Centrality and network flow. Soc Netw 27:55鈥?1 pii: 16/j.socnet.2004.11.008 CrossRef
  8. Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163鈥?77. doi:10.1080/0022250X.2001.9990249 CrossRef
  9. Carrington PJ, Scott J, Wasserman S (2005) Models and methods in social network analysis. Cambridge University Press, Cambridge
  10. Ceder A, Net YL, Coriat C (2009) Measuring public transport connectivity performance applied in Auckland, New Zealand. Transp Res Record J Transp Res Board 2111:139鈥?47 CrossRef
  11. Costenbader E, Valente TW (2003) The stability of centrality measures when networks are sampled. Soc Netw 25:283鈥?07 pii: 16/S0378-8733(03)00012-1 CrossRef
  12. Crucitti P, Latora V, Porta S (2006) Centrality in networks of urban streets. Chaos 16:015113. doi:10.1063/1.2150162 CrossRef
  13. Derrible S, Kennedy C (2009) Network analysis of world subway systems using updated graph theory. Transp Res Record J Transp Res Board 2112:17鈥?5. doi:10.3141/2112-03 CrossRef
  14. Estrada E, Rodr铆guez-Vel谩zquez JA (2005) Subgraph centrality in complex networks. Phys Rev E 71:056103. doi:10.1103/PhysRevE.71.056103 CrossRef
  15. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1:215鈥?39 pii: 16/0378-8733(78)90021-7 CrossRef
  16. Garroway CJ, Bowman J, Carr D, Wilson PJ (2008) Applications of graph theory to landscape genetics. Evol Appl 1:620鈥?30. doi:10.1111/j.1752-4571.2008.00047.x
  17. Goh K-I, Oh E, Kahng B, Kim D (2003) Betweenness centrality correlation in social networks. Phys Rev E 67:017101. doi:10.1103/PhysRevE.67.017101 CrossRef
  18. Guimer脿 R, Mossa S, Turtschi A, Amaral LAN (2005) The worldwide air transportation network: anomalous centrality, community structure, and cities鈥?global roles. Proc Natl Acad Sci USA 102:7794鈥?799. doi:10.1073/pnas.0407994102 CrossRef
  19. Hadas Y, Ceder A (2010) Public transit network connectivity. Transp Res Record J Transp Res Board 2143:1鈥?. doi:10.3141/2143-01 CrossRef
  20. Hadas Y, Ceder A, Ranjitkar P (2011) Modeling public-transit connectivity with quality-of-transfer measurements
  21. Jiang B, Claramunt C (2004) A structural approach to the model generalization of an urban street network. GeoInformatica 8:157鈥?71. doi:10.1023/B:GEIN.0000017746.44824.70 CrossRef
  22. Junker B, Koschutzki D, Schreiber F (2006) Exploration of biological network centralities with CentiBiN. BMC Bioinform 7:219. doi:10.1186/1471-2105-7-219 CrossRef
  23. Lam TN, Schuler HJ (1982) Connectivity index for systemwide transit route and schedule performance. Transp Res Rec 854:17鈥?3
  24. Latora V, Marchiori M (2007) A measure of centrality based on network efficiency. New J Phys 9:188. doi:10.1088/1367-2630/9/6/188 CrossRef
  25. Liu X, Bollen J, Nelson ML, Van de Sompel H (2005) Co-authorship networks in the digital library research community. Inf Process Manage 41:1462鈥?480 pii: 16/j.ipm.2005.03.012 CrossRef
  26. Martinez KLH, Porter BE (2006) Characterizing red light runners following implementation of a photo enforcement program. Accid Anal Prev 38:862鈥?70. doi:10.1016/j.aap.2006.02.011 CrossRef
  27. Mishra S, Welch TF, Jha MK (2012) Performance indicators for public transit connectivity in multi-modal transportation networks. Transp Res Part A Policy Pract 46:1066鈥?085. doi:10.1016/j.tra.2012.04.006 CrossRef
  28. Moore S, Eng E, Daniel M (2003) International NGOs and the role of network centrality in humanitarian aid operations: a case study of coordination during the 2000 Mozambique floods. Disasters 27:305鈥?18. doi:10.1111/j.0361-3666.2003.00235.x CrossRef
  29. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70:056131. doi:10.1103/PhysRevE.70.056131 CrossRef
  30. Newman MEJ (2005) A measure of betweenness centrality based on random walks. Soc Netw 27:39鈥?4 pii: 16/j.socnet.2004.11.009 CrossRef
  31. Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Soc Netw 32:245鈥?51 pii: 16/j.socnet.2010.03.006 CrossRef
  32. Otte E, Rousseau R (2002) Social network analysis: a powerful strategy, also for the information sciences. J Inf Sci 28:441鈥?53. doi:10.1177/016555150202800601 CrossRef
  33. 脰zg眉r A, Vu T, Erkan G, Radev DR (2008) Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics 24:i277鈥搃285. doi:10.1093/bioinformatics/btn182 CrossRef
  34. Park J, Kang SC (2011) A model for evaluating the connectivity of multimodal transit networks. Transportation Research Board 90th annual meeting
  35. Ruhnau B (2000) Eigenvector-centrality鈥攁 node-centrality? Soc Netw 22:357鈥?65 pii: 16/S0378-8733(00)00031-9 CrossRef
  36. White HD (2003) Pathfinder networks and author cocitation analysis: a remapping of paradigmatic information scientists. J Am Soc Inform Sci Technol 54:423鈥?34. doi:10.1002/asi.10228 CrossRef
  37. White DR, Borgatti SP (1994) Betweenness centrality measures for directed graphs. Soc Netw 16:335鈥?46 pii: 16/0378-8733(94)90015-9 CrossRef
  
• 刊物主题：Operations Research/Decision Theory; Automotive Engineering; Computer-Aided Engineering (CAD, CAE) and Design; Transportation;
• 出版者：Springer Berlin Heidelberg
• ISSN：1613-7159

文摘

Agencies at the federal, state and local level are aiming to enhance the public transportation system (PTS) as one alternative to alleviate congestion and to cater to the needs of captive riders. To effectively act as a viable alternative transportation mode, the system must be highly efficient. One way to measure efficiency of the PTS is connectivity. In a multimodal transportation system, transit is a key component. Transit connectivity is relatively complex to calculate, as one has to consider fares, schedule, capacity, frequency and other features of the system at large. Thus, assessing transit connectivity requires a systematic approach using many diverse parameters involved in real-world service provision. In this paper, we use a graph theoretic approach to evaluate transit connectivity at various levels of service and for various components of transit, such as nodes, lines, and transfer centers in a multimodal transportation system. Further, we provide a platform for computing connectivity over large-scale applications, using visualization to communicate results in the context of their geography and to facilitate public transit decision-making. The proposed framework is then applied to a comprehensive transit network in the Washington-Baltimore region. Underpinning the visualization, we introduce a novel spatial data architecture and Web-based interface designed with free and open source libraries and crowd-sourced contextual data, accessible on various platforms such as mobile phones, tablets and personal computers. The proposed methodology is a useful tool for both riders and decision-makers in assessing transit connectivity in a multimodal transit network in a number of ways such as the identification of under-served transit areas, prioritization and allocation of funds to locations for improving transit service.