Advertisement

Visualisation of trip chaining behaviour and mode choice using household travel survey data

  • Günter Wallner
  • Simone Kriglstein
  • Edward Chung
  • Syeed Anta Kashfi
Original Paper

Abstract

Planning for transport infrastructure requires forecasting of future travel demand. Various factors such as future population, employment, and the travel behaviour of the residents drive travel demand. In order to better understand human travel behaviour, household travel surveys—which require participants to record all their trips made during a single day or over a whole week—are conducted. However, the daily travel routines of people can be very complex, including routes with multiple stops and/or different purposes and often may involve different modes of transport. Visualisations that are currently employed in transport planning are, however, limited for the analysis of complex trip chains and multi-modal travel. In this paper, we introduce a unique visualisation approach which simultaneously represents several important factors involved in analysing trip chaining: number and type of stops, the quantity of traffic between them, and the utilised modes of transport. Moreover, our proposed technique facilitates the inspection of the sequential relation between incoming and outgoing traffic at stops. Using data from the South-East Queensland Travel Survey 2009, we put our developed algorithm into practice and visualise the journey-to-work travel behaviour of the residents of inner Brisbane, Australia. Our visualisation technique can assist transport planners to better understand the characteristics of the trip data and, in turn, inform subsequent statistical analysis and the development of travel demand models.

Keywords

Trip chain Multi-modal travel Trip scheduling Human travel behaviour Visualisation Household travel survey 

JEL Classification

R29 R41 R42 

Notes

Acknowledgements

We would like to thank the Queensland Department of Transport and Main Roads for providing the HTS data.

References

  1. Alsnih R, Hensher DA (2005) The travel behaviour of seniors in an ageing population in Sydney: an exploratory study of trip chains and modal preferences in the greater metropolitan area of Sydney. Road Transp Res 14(4):60–76Google Scholar
  2. Andrienko G, Andrienko N, Bak P, Keim D, Wrobel S (2013) Visual analytics focusing on space. In: Visual analytics of movement. Springer, Berlin, Heidelberg, pp 253–305. ISBN:978-3-642-37583-5CrossRefGoogle Scholar
  3. Bastert O, Matuszewski C (2001) Layered drawings of digraphs. Drawing graphs: methods and models. Springer, Berlin, pp 87–120CrossRefGoogle Scholar
  4. Bhat CR (1997) Work travel mode choice and number of non-work commute stops. Transp Res Part B 31(1):41–54CrossRefGoogle Scholar
  5. Bhat CR, Koppelman FS (1999) Activity-based modeling of travel demand. In: Hall RW (ed) Handbook of transportation science, vol 23. Springer, New York, pp 35–61.  https://doi.org/10.1007/978-1-4615-5203-1_3 CrossRefGoogle Scholar
  6. Bureau of Transport Statistics: JTW visualiser. http://visual.bts.nsw.gov.au/jtwdynamic/. Accessed July 2017)
  7. Capiler Corporation. TransCAD transportation planning software. http://www.caliper.com/TCTravelDemand.htm. Accessed July 2017
  8. Corcoran J, Chhetri P, Stimson R (2009) Using circular statistics to explore the geography of the journey to work. Pap Reg Sci 88(1):119–132.  https://doi.org/10.1111/j.1435-5957.2008.00164.x CrossRefGoogle Scholar
  9. Currie G, Delbosc A (2011) Exploring the trip chaining behavior of public transport users in Melbourne. Transp Policy 18:201–210Google Scholar
  10. Department of Economic Development, Jobs, Transport and Resources (2016) VISTA 2012–13 - Journey to work - victorian integrated survey of travel & activity (VISTA). https://transport.vic.gov.au/data-and-research/vista/vista-data-and-publications/
  11. Department of Transport and Main Roads (2009) South East Queensland Travel Survey 2009. https://data.qld.gov.au/dataset/2009-south-east-queensland-household-travel-survey
  12. Eades P, Kelly D (1986) Heuristics for reducing crossings in 2-layered networks. ARS Combin 21:89–98Google Scholar
  13. Eiglsperger M, Siebenhaller M, Kaufmann M (2005) An efficient implementation of Sugiyama’s algorithm for layered graph drawing. J Graph Algorithms Appl 9(3):305–325CrossRefGoogle Scholar
  14. Fiorenzo-Catalano S, Van Nes R, Bovy PH (2004) Choice set generation for multi-modal travel analysis. Eur J Transp Infrastruct Res 4(2):195–209Google Scholar
  15. Golob TF, Hensher DA (2007) The trip chaining activity of Sydney residents: a cross-section assessment by age group with a focus on seniors. J Transp Geogr 15:298–312.  https://doi.org/10.1016/j.jtrangeo.2006.09.005 CrossRefGoogle Scholar
  16. Habib KMN, Day N, Miller EJ (2009) An investigation of commuting trip timing and mode choice in the Greater Toronto Area: application of a joint discrete-continuous model. Transp Res Part A 43(7):639–653.  https://doi.org/10.1016/j.tra.2009.05.001 CrossRefGoogle Scholar
  17. Harney D, Rajoo PD (2015) Moving to tour based models in QLD, is it time? In: Australian Institute of Traffic Planning and Management (AITPM) national conference, 28–31 July 2015, BrisbaneGoogle Scholar
  18. Harrower M, Brewer C (2003) ColorBrewer.org: an online tool for selecting colour schemes for maps. Cartogr J 40(1):27–37.  https://doi.org/10.1179/000870403235002042 CrossRefGoogle Scholar
  19. Healy N (2013) Hierarchical drawing algorithms. In: Tamassia R (ed) Handbook of graph drawing and visualization. Chapman and Hall/CRC, Boca RatonGoogle Scholar
  20. Hensher DA (2007) Some insights into the key influences on trip chaining activity and public transport use of seniors and the elderly. Int J Sustain Transp 1(1):53–68.  https://doi.org/10.1080/15568310601097004 CrossRefGoogle Scholar
  21. Hensher DA, Reyes AJ (2000) Trip chaining as a barrier to the propensity to use public transport. Transportation 27(4):341–361.  https://doi.org/10.1023/A:1005246916731 CrossRefGoogle Scholar
  22. Holten D, Van Wijk JJ (2009a) Force-directed edge bundling for graph visualization. In: Proceedings of the 11th Eurographics/IEEE—VGTC conference on visualization (Aire-la-Ville, Switzerland, Switzerland), EuroVis’09. Euro graphics Association, pp 983–998.  https://doi.org/10.1111/j.1467-8659.2009.01450.x CrossRefGoogle Scholar
  23. Holten D, Van Wijk JJ (2009b) A user study on visualizing directed edges in graphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI’09. ACM, New York, pp 2299–2308.  https://doi.org/10.1145/1518701.1519054
  24. Islam MT, Habib KN (2012) Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary. Transp Plan Technol 35(4):409–426CrossRefGoogle Scholar
  25. Leveson GT (2013) 2011 Census analysis—method of travel to work in England and Wales Report. Tech. Rep., Office for National StatisticsGoogle Scholar
  26. Ma J, Mitchell G, Heppenstall A (2014) Daily travel behaviour in Beijing, China: an analysis of workers’ trip chains, and the role of socio-demographics and urban form. Habitat Int 43:263–273CrossRefGoogle Scholar
  27. Manore MA (2007) Visualization in transportation 101. In: TR news, visualization in transportation: empowering innovation, transportation research board of the national academes, vol 252, September-October, p 3. http://onlinepubs.trb.org/onlinepubs/trnews/trnews252.pdf
  28. McGuckin N, Murakami E (1999) Examining trip-chaining behavior. A comparison of travel by men and women. Transp Res Rec 1963:79–85. http://nhts.ornl.gov/1995/Doc/Chain2.pdf,  https://doi.org/10.3141/1693-12 CrossRefGoogle Scholar
  29. McGuckin N, Zmud J, Nakamoto Y (2005) Trip chaining trends in the U.S.—understanding travel behaviour for policy making. Transp Res Rec 1917:199–204.  https://doi.org/10.3141/1917-22 CrossRefGoogle Scholar
  30. Monteiro N, Rossetti R, Campos P, Kokkinogenis Z (2014) A framework for a multimodal transportation network: an agent-based model approach. Transp Res Procedia 4:213–227.  https://doi.org/10.1016/j.trpro.2014.11.017 CrossRefGoogle Scholar
  31. Nagel T, Maitan M, Duval E, Moere AV, Klerkx K, Ratti C (2014) Touching transport—a case study on visualizing metropolitan public transit on interactive tabletops. In: Proceedings of the 2014 international working conference on advanced visual interfaces, AVI’14. ACM, New York, pp 281–288.  https://doi.org/10.1145/2598153.2598180
  32. Omer M, Kim H, Sasaki K, Nishii K (2010) Tour based travel demand model using person trip data and its application to advanced policies. KSCE J Civil Eng 14(2):221–230.  https://doi.org/10.1007/s12205-010-0221-6 CrossRefGoogle Scholar
  33. Perer A, Wang F (2014) Frequence: interactive mining and visualization of temporal frequent event sequences. In: Proceedings of the 19th international conference on intelligent user interfaces, IUI’14. ACM, pp 153–162.  https://doi.org/10.1145/2557500.2557508
  34. Phan D, Xiao L, Hanrahan P, Wino-Grad T (2005) Flow map layout. In: Proceedings of the 2005 IEEE symposium on information visualization. IEEE Computer Society, pp 219–224Google Scholar
  35. Philip M, Sreelatha T, George S (2013) Activity based travel behavioural study and mode choice modelling. Int J Innov Res Sci Eng Technol 2(1):181–190Google Scholar
  36. Primerano F, Michael APT, Pitaksringkarn L, Tisato P (2008) Defining and understanding trip chaining behavior. Transportation 35(1):55–72.  https://doi.org/10.1007/s11116-007-9134-8 CrossRefGoogle Scholar
  37. Public Transport Victoria—Journey Planner. https://www.ptv.vic.gov.au/journey. Accessed July 2017
  38. Pupyrev S, Nachmanson L, Kaufmann M (2011) Improving layered graph layouts with edge bundling. In: Brandes U, Cornelsen S (eds) Graph drawing, vol 6502. Lecture Notes in Computer Science. Springer, Berlin, pp 329–340.  https://doi.org/10.1007/978-3-642-18469-7_30 CrossRefGoogle Scholar
  39. Queensland Government—Office of Economic and Statistical Research (2010) Information brief—Regional Population Growth: 2008–09. http://www.qgso.qld.gov.au/products/reports/pop-growth-reg-qld/reg-pop-growth-2008-09.pdf. Accessed August 2017
  40. Rail Europe—Rail travel planner Europe. http://www.raileurope-world.com. Accessed July 2017
  41. Riehmann P, Hanfler M, Froehlich B (2005) Interactive Sankey diagrams. In: Proceedings of the IEEE symposium on information visualization, INFOVIS, pp 233–240.  https://doi.org/10.1109/infvis.2005.1532152
  42. Rosvall M, Bergstrom T (2010) Mapping change in large networks. PLoS One 5:1.  https://doi.org/10.1371/journal.pone.0008694 CrossRefGoogle Scholar
  43. Shiftan Y (1998) Practical approach to model trip chaining. Transp Res Rec 1645:17–23CrossRefGoogle Scholar
  44. Silva S, Santos BS, Madeira J (2011) Using color in visualization: a survey. Comput Graph 35(2):320–333CrossRefGoogle Scholar
  45. Strathman JG, Dueker KJ, Davis JS (1994) Effects of household structure and selected travel characteristics on trip chaining. Transportation 21(1):23–45.  https://doi.org/10.1007/BF01119633 CrossRefGoogle Scholar
  46. Sugiyama K, Tagawa S, Toda M (1981) Methods for visual understanding of hierarchical system structures. IEEE Trans Syst Man Cybern 11(2):109–125.  https://doi.org/10.1109/TSMC.1981.4308636 CrossRefGoogle Scholar
  47. Sun Y, Shi J, Schonfeld PM (2016) Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro. Public Transp 8(3):341–363CrossRefGoogle Scholar
  48. TransLink—Journey Planner. http://jp.translink.com.au. Accessed July 2017
  49. Tufte E (2001) The visual display of quantitative information. Graphics Press, CheshireGoogle Scholar
  50. Verbeek K, Buchin K, Speckmann B (2011) Flow map layout via spiral trees. IEEE Trans Vis Comput Graph 17(12):2536–2544CrossRefGoogle Scholar
  51. Wallace B, Barnes J, Rutherford GS (2000) Evaluating the effect of traveler and trip characteristics on trip chaining, with implications for transportation demand management strategies. Transp Res Rec 1718:97–106CrossRefGoogle Scholar
  52. Walle SV, Steenberghen T (2006) Space and time related determinants of public transport use in trip chains. Transp Res Part A 40:151–162Google Scholar
  53. Ware C (2004) Information visualization: perception for design, 2nd edn. Morgan Kaufmann, BurlingtonGoogle Scholar
  54. Wongsuphasawat K, Gotz D (2012) Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans Vis Comput Graph 18(12):2659–2668.  https://doi.org/10.1109/TVCG.2012.225 CrossRefGoogle Scholar
  55. Xianyu J (2013) An exploration of the interdependencies between trip chaining behavior and travel mode choice. Procedia Soc Behav Sci 96:1967–1975.  https://doi.org/10.1016/j.sbspro.2013.08.222 CrossRefGoogle Scholar
  56. Xu B, Milthorpe F (2010) Analysis of journey to work travel patterns in Sydney. In: Proceedings of the Australasian transport research forum. http://atrf.info/papers/2010/2010_Xu_Milthorpe.pdf
  57. Xu M, Milthorpe F, Tsang K (2011) Detailed analysis of the travel patterns of rail users in Sydney. In: Proceedings of the Australasian transport research forum. http://atrf.info/papers/2011/2011_Xu_Milthorpe_Tsang.pdf
  58. Ye X, Pendyala RM, Gottardi G (2007) An exploration of the relationship between mode choice and complexity of trip chaining patterns. Transp Res Part B 41:96–113.  https://doi.org/10.1016/j.trb.2006.03.004 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Design and Assessment of TechnologyVienna University of TechnologyViennaAustria
  2. 2.Center for Technology ExperienceAIT Austrian Institute of Technology GmbHViennaAustria
  3. 3.Department of Electrical Engineering, Faculty of EngineeringHong Kong Polytechnic UniversityKowloonHong Kong
  4. 4.Queensland University of TechnologyBrisbaneAustralia

Personalised recommendations