Monitoring transit-served areas with smartcard data: A Brisbane case study

Journal of Transport Geography

Volumn 76, Issue , 2019, Pages 265-275

  Zhou, Jiangping ^a   Sipe, Neil ^b   Ma, Zhenliang ^c   Mateo Babiano, Derlie ^d   Darchen, Sébastien ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c Northeastern University   (United States)

^d UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; MONITORING; RAILWAY TRANSPORT; SPATIAL VARIATION; TEMPORAL VARIATION; URBAN AREA;

AUSTRALIA; BRISBANE; QUEENSLAND;

EID: 85025086823     PISSN: 09666923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtrangeo.2017.07.005     Document Type: Article

References (47)

1. ABS, Statistical Area Level 2 (SA2). Available at http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/88F6A0EDEB8879C0CA257801000C64D9, 2016 (accessed July 24, 2016).
2. Alsger, A.A., Mesbah, M., Ferreira, L., Safi, H., Use of smart card fare data to estimate public transport origin-destination matrix. Transp. Res. Rec. 2535 (2015), 88–96.
3. Batty, M., Big data, smart cities and city planning. Dialogues in Hum. Geogr. 3:3 (2013), 274–279.
4. California Department of Transportation (Caltrans), State of California, Highway Design Manual. Available at http://www.dot.ca.gov/hq/oppd/hdm/pdf/english/HDM_Complete_07Mar2014.pdf, 2015.
5. Calthorpe, P., The Next American Metropolis: Ecology, Community, and the American Dream. 1993, Princeton Architectural Press, Princeton, NJ.
6. Cervero, R., Ferrell, C., Murphy, S., Transit Cooperative Research Program, Research Results Digest Number 52. 2002, Transportation Research Board, National Research Council, Washington, D.C.
7. Charron, M., From excess commuting to commuting possibilities: more extension to the concept of excess commuting. Environ. Plan. A 39 (2007), 1238–1254.
8. Engels, B., Liu, G., Social exclusion, location and transport disadvantage amongst non-driving seniors in a Melbourne municipality, Australia. J. Transp. Geogr. 19 (2011), 984–996.
9. Eom, J.K., Song, J.Y., Moon, D.S., Analysis of public transit service performance using transit smart card data in Seoul. KSCE J. Civ. Eng. 19:5 (2015), 1530–1537.
10. Frost, M., Linneker, B., Spence, N., Excess or wasteful commuting in a selection of British cities. Transp. Res. A Policy Pract. 32:7 (1998), 529–538.
11. He, L., Trepanier, M., Estimating the destination of unlinked trips in transit smart card fare data. Transp. Res. Rec. 2535 (2015), 97–104.
12. Healy, E., O'Connor, K., Jobs and housing location in Melbourne,1986–1996. Aust. Plan. 38:1 (2001), 9–15.
13. Horner, M.W., A multi-scale analysis of urban form and commuting change in a small metropolitan area (1990–2000). Ann. Reg. Sci. 41:2 (2007), 312–332.
14. Horner, M.W., Murray, A.T., Excess commuting and the modifiable areal unit problem. Urban Stud. 39:1 (2002), 131–139.
15. Horner, M.W., Schleith, D., Analyzing temporal changes in land-use transportation relationships: a LEHD-based approach. Appl. Geogr. 35 (2012), 491–498.
16. van der Hurk, E., Kroon, L., Maroti, G., Vervest, P., Deduction of passengers' route choices from smart card data. IEEE Trans. Intell. Transp. Syst. 16:1 (2015), 430–440.
17. Janosikova, L., Slavik, J., Kohani, M., Estimation of a route choice model for urban public transport using smart card data. Transp. Plan. Technol. 37:7 (2014), 638–648.
18. Kieu, L.M., Bhaskar, A., Chung, E., Passenger segmentation using smart card data. IEEE Trans. Intell. Transp. Syst. 16:3 (2015), 1537–1548.
19. Kim, K., Oh, K., Lee, Y.K., Kim, S., Jung, J.Y., An analysis on movement patterns between zones using smart card data in subway networks. Int. J. Geogr. Inf. Sci. 28:9 (2014), 1781–1801.
20. Li, T., Corcoran, J., Burke, M., Disaggregate GIS modelling to tract spatial change: exploring a decade of commuting in South East Queensland, Australia. J. Transp. Geogr. 24 (2012), 306–314.
21. Loo, B.P.Y., Chow, A.S.Y., Jobs-housing balance in an era of population decentralization: an analytical framework and a case study. J. Transp. Geogr. 19:4 (2011), 552–562.
22. Ma, K.R., Banister, D., Extended excess commuting: a measure of the jobs-housing imbalance in Seoul. Urban Stud. 43:11 (2006), 2099–2113.
23. Ma, X.L., Wang, Y.H., Chen, F., Liu, J.F., Transit smart card data mining for passenger origin information extraction. J. Zhejiang Univ.-Sci. C-Comput. Electron. 13:10 (2012), 750–760.
24. Morency, C., Trepanier, M., Agard, B., Measuring transit use variability with smart-card data. Transp. Policy 14 (2007), 193–203.
25. Murphy, E., Excess commuting and modal choice. Transp. Res. A-Policy Pract. 43:8 (2009), 735–743.
26. Murphy, E., Killen, J.E., Commuting economy: an alternative approach for assessing regional commuting efficiency. Urban Stud. 48:6 (2011), 1255–1272.
27. Nassir, N., Hickman, M., Ma, Z.L., Activity detection and transfer identification for public transit fare card data. Transportation 42 (2015), 683–705.
28. van Oort, N., Brands, T., de Romph, E., Short-term prediction of ridership on public transport with smart card data. Transp. Res. Rec. 2535 (2015), 105–111.
29. Pelletier, M.P., Trepanier, M., Morency, C., Smart card data use in public transit: a literature review. Transp. Res. Part C-Emerg. Technol. 19 (2011), 557–568.
30. Salas-Olmedo, M.H., Nogués, S., Analysis of commuting needs using graph theory and census data: a comparison between two medium-sized cities in the UK. Appl. Geogr. 35:1 (2012), 132–141.
31. Schleith, D., Horner, M., Commuting, job clusters, and travel burdens: analysis of spatially and socioeconomically disaggregated longitudinal employer-household dynamics data. Transp. Res. Rec. 2542 (2014), 19–27.
32. Tamblay, S., Galilea, P., Iglesias, P., Raveau, S., Munoz, J.C., A zonal inference model based on observed smart-card transactions for Santiago de Chile. Transp. Res. Part A-Policy Pract. 84 (2016), 44–54.
33. Tao, S., Corcoran, J., Mateo-Babiano, I., Rohde, D., Exploring Bus Rapid Transit passenger travel behaviour using big data. Appl. Geogr. 53 (2014), 90–104.
34. Tao, S., Rohde, D., Corcoran, J., Examining the spatial-temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap. J. Transp. Geogr. 41 (2014), 21–36.
35. The Transit Cooperative Research Program (TCRP), Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects. 2004, Washington D.C., Federal Transit Administration.
36. TransLink, TransLink Tracker. October–December Q2. 2016, TransLink, Brisbane, Australia.
37. Treasury, Queensland, Population Growth Highlights and Treads, Queensland Regions. 2015 edition, 2015, Queensland Government, Brisbane, Australia.
38. White, M.J., Urban commuting journeys are not “wasteful”. J. Polit. Econ. 96 (1988), 1097–1110.
39. Yang, J., Ferreira, J.J., Choices versus choice sets: a commuting spectrum method for representing job-housing possibilities. Environ. Plan. B 35 (2008), 364–378.
40. Zhong, C., Manley, E., Arisona, S.M., Batty, M., Schmitt, G., Measuring variability of mobility patterns from multiday smart-card data. J. Comput. Sci. 9 (2015), 125–130.
41. Zhong, C., Batty, M., Manley, E., Wang, J.Q., Wang, Z.J., Chen, F., Schmitt, G., Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PLoS One, 11(2), 2016.
42. Zhou, J., From better understandings to proactive actions: housing and commuting mode choices among university students. Transp. Policy 33 (2014), 166–175.
43. Zhou, J., Long, Y., Jobs-housing balance of bus commuters in Beijing exploration with large-scale synthesized smart card data. Transp. Res. Rec. 2418 (2014), 1–10.
44. Zhou, J., Long, Y., Losers and Pareto optimality in optimizing commuting patterns. Urban Stud., 2016 (In Press).
45. Zhou, J., Wang, Y., Schweitzer, L., Jobs-housing balance and employer-based travel demand management program returns to scale: evidences from Los Angeles. Transp. Policy 20 (2012), 22–35.
46. Zhou, J., Murphy, E., Long, Y., Commuting efficiency in the Beijing Metropolitan Area: an exploration combing smartcard and travel survey data. J. Transp. Geogr. 41 (2014), 175–183.
47. Zhou, J., Murphy, E., Long, Y., Visualizing the minimum and maximum solutions of the transportation problem of linear programming for Beijing's bus commuters. Environ. Plan. A 46 (2014), 2051–2054.