Networks and Spatial Economics

, Volume 13, Issue 3, pp 351–372 | Cite as

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

  • Katrien Ramaekers
  • Sofie Reumers
  • Geert Wets
  • Mario Cools


The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables.


Route choice modelling Shortest path Road category Trip purpose Activity-based approach 


  1. Abdel-Aty MA, Huang Y (2004) Exploratory spatial analysis of expressway ramps and its effects on route choice. J Transp Eng 130(1):104–112CrossRefGoogle Scholar
  2. Abdel-Aty MA, Kitamura R, Jovanis PP (1997) Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice. Transp Res C 5(1):39–50CrossRefGoogle Scholar
  3. Amemiya T (1984) Tobit models: a survey. J Econom 24:3–61CrossRefGoogle Scholar
  4. Avineri E, Prashker J (2004) Violations of expected utility theory in route-choice stated preferences: certainty effect and inflation of small probabilities. Transp Res Rec 1894:222–229CrossRefGoogle Scholar
  5. Bayarma A, Kitamura R, Susilo Y (2007) Recurrence of daily travel patterns: stochastic process approach to multiday travel behavior. Transp Res Rec 2021:55–63CrossRefGoogle Scholar
  6. Bekhor S, Ben-Akiva M, Ramming M (2006) Evaluation of choice set generation algorithms for route choice models. Ann Oper Res 144(1):235–247CrossRefGoogle Scholar
  7. Bellemans T, Kochan B, Janssens D, Wets G, Timmermans H (2008) Field evaluation of personal digital assistant enabled by global positioning system: impact on quality of activity and diary data. Transp Res Rec 2049:136–143CrossRefGoogle Scholar
  8. Chen BY, Lam WHK, Sumalee A, Li Q, Shao H, Fang Z (2012) Finding reliable shortest paths in road networks under uncertainty. Netw Spat Econ. doi:10.1007/s11067-012-9175-1 Google Scholar
  9. Cirillo C, Cornelis E, Toint PL (2012) A Model of weekly labor participation for a Belgian synthetic population. Netw Spat Econ 12(1):59–73CrossRefGoogle Scholar
  10. Cools M, Moons E, Wets G (2009) Investigating the variability in daily traffic counts 16 through use of ARIMAX and SARIMAX models: assessing the effect of holidays on two site locations. Transp Res Rec 2136:57–66CrossRefGoogle Scholar
  11. Cools M, Moons E, Creemers L, Wets G (2010a) Changes in travel behavior in response to weather conditions: do type of weather and trip purpose matter? Transp Res Rec 2157:22–28CrossRefGoogle Scholar
  12. Cools M, Moons E, Wets G (2010b) Assessing the impact of public holidays on travel time expenditure: differentiation by trip motive. Transp Res Rec 2157:29–37CrossRefGoogle Scholar
  13. Cools M, Moons E, Wets G (2010c) Assessing the impact of weather on traffic intensity. Weather Clim Soc 2(1):60–68CrossRefGoogle Scholar
  14. Davidson W, Donnelly R, Vovsha P, Freedman J, Ruegg S, Hicks J, Castiglione J, Picado R (2007) Synthesis of first practices and operational research approaches in activity-based travel demand modeling. Transp Res A 41:454–488Google Scholar
  15. de Palma A, Picard N (2005) Route choice decision under travel time uncertainty. Transp Res A Policy Pract 39(4):295–324CrossRefGoogle Scholar
  16. Duffel J, Kalombaris A (1988) Empirical studies of car driver route choice in Hertfordshire. Traffic Eng Control 29(7/8):398–408Google Scholar
  17. Flötteröd G, Chen Y, Nagel K (2012) Behavioral calibration and analysis of a large-scale travel microsimulation. Netw Spat Econ 12(4):481–502CrossRefGoogle Scholar
  18. Goldenbeld C, Drolenga J, Smits A (2007) Routekeuze van automobilisten. Resultaten van een vragenlijstonderzoek R-2006–33:116Google Scholar
  19. Han Q, Timmermans HJP, Dellaert BGC, van Raaij F (2008) Route choice under uncertainty: effects of recommendations. Transp Res Rec 2082:72–80CrossRefGoogle Scholar
  20. Hoffman SD, Duncan GK (1988) Multinomial and conditional logit discrete-choice models in demography. Demography 25(3):415–427CrossRefGoogle Scholar
  21. Huang A, Levinson D (2012) Accessibility, network structure, and consumers’ destination choice: a GIS analysis of GPS travel data. Proceedings of the 91st Annual Meeting of the Transportation Research Board. Transportation Research Board of the National Academies, Washington, D.CGoogle Scholar
  22. Huchingson R, McNees R, Dudek C (1977) Survey of motorist route-selection criteria. Transp Res Rec 643:45–48Google Scholar
  23. Jackson W, Jucker J (1981) An empirical study of travel time variability and travel choice behaviour. Transp Sci 16(4):460–475CrossRefGoogle Scholar
  24. Jan O, Horowitz A, Peng Z-R (2000) Using global positioning system data to understand variations in path choice. Transp Res Rec 1725:37–44CrossRefGoogle Scholar
  25. Jou R, Mahmassani H (1994) Comparability and transferability of commuter behaviour characteristics between cities: departure time and route switching decisions. Presented at the 73rd Annual Meeting of the Transportation Research BoardGoogle Scholar
  26. Kaplan S, Prato CG (2012) Closing the gap between behavior and models in route choice: the role of spatiotemporal constraints and latent traits in choice set formation. Transp Res F 15:9–24CrossRefGoogle Scholar
  27. Levinson D, El-Geneidy A (2009) The minimum circuity frontier and the journey to work. Reg Sci Urban Econ 39(6):732–738CrossRefGoogle Scholar
  28. Levinson D, Zhu S (2012) The hierarchy of roads, the locality of traffic, and governance. Transp Policy 19:147–154CrossRefGoogle Scholar
  29. Li H (2004) Investigating morning commute route choice behavior using global positioning systems and multi-day travel data. PhD thesis, Georgia Institute of Technology, GAGoogle Scholar
  30. Li H, Guensler R, Ogle J (2005) Analysis of morning commute route choice patterns using global positioning system-based vehicle activity data. Transp Res Rec 1926:162–170CrossRefGoogle Scholar
  31. Mannering F, Kim S-G, Barfield W, Ng L (1994) Statistical analysis of commuters’ route, mode and departure time flexibility. Transp Res C 2(1):35–47CrossRefGoogle Scholar
  32. Marquardt DW (1980) You should standardize the predictor variables in your regression models. J Am Stat Assoc 75(369):74–103CrossRefGoogle Scholar
  33. McDonald FF, Moffitt RA (1980) The uses of tobit analysis. Rev Econ Stat 62(2):318–321CrossRefGoogle Scholar
  34. Miermans W, Janssens D, Cools M, Wets G (2010) Onderzoek verplaatsingsgedrag vlaanderen 4.1 (2008-2009): verkeerskundige interpretatie van de belangrijkste gegevens. Transportation Research Institute, Hasselt University, Diepenbeek, BelgiumGoogle Scholar
  35. Montgomery DC, Runger GC (2003) Applied statistics and probability for engineers, 4th edn. John Wiley and Sons, New YorkGoogle Scholar
  36. Murakami E, Wagner DP (1999) Can using global positioning system (GPS) improve trip reporting? Transp Res C Emerg Technol 7(2–3):149–165CrossRefGoogle Scholar
  37. Papinski D, Scott DM, Doherty ST (2009) Exploring the route choice decision-making process: a comparison of planned and observed routes obtained using person-based GPS. Transp Res F Traffic Psychol Behav 12(4):347–358CrossRefGoogle Scholar
  38. Parkany E, Du J, Aultman-Hall L, Gallagher R (2006) Modeling stated and revealed route choice: consideration of consistency, diversion, and attitudinal variables. Transp Res Rec 1985:29–39CrossRefGoogle Scholar
  39. Parthasarathi P (2011) Network structure and travel. PhD dissertation, University of MinnesotaGoogle Scholar
  40. Parthasarathi P, Hochmair H, Levinson D (2012) Network structure and spatial separation. Environ Plan B Plan Des 39(1):137–154CrossRefGoogle Scholar
  41. Peeta S, Yu JW (2005) A hybrid model for driver route choice incorporating en-route attributes and real-time information effects. Netw Spat Econ 5:21–40CrossRefGoogle Scholar
  42. Prato CG (2009) Route choice modeling: past, present and future research directions. J Choice Model 2(1):65–100CrossRefGoogle Scholar
  43. Prato C, Bekhor S (2006) Applying brand-and-bound techniques to route choice set generation. Transp Res Rec 1985(1):19–28CrossRefGoogle Scholar
  44. Qian Z, Zhang HM (2012) A hybrid route choice model for dynamic traffic assignment. Netw Spat Econ. doi:10.1007/s11067-012-9177-z Google Scholar
  45. Ramadurai G, Ukkusuri S (2010) Dynamic User equilibrium model for combined activity-travel choices using activity-travel supernetwork representation. Netw Spat Econ 10(2):273–292CrossRefGoogle Scholar
  46. Ramming MS (2002) Network knowledge and route choice PhD-thesis, Massachusetts Institute of TechnologyGoogle Scholar
  47. Scheiner J (2010) Social inequalities in travel behaviour: trip distances in the context of residential self-selection and lifestyles. J Transp Geogr 18:679–690CrossRefGoogle Scholar
  48. Schuessler N, Balmer M, Axhausen KW (2010) Route choice sets for very high-resolution data. Proceedings of the 89th Annual Meeting of the Transportation Research Board. CD-ROM. Transportation Research Board of the National Academies, Washington, D.C.Google Scholar
  49. Spissu E, Meloni I, Sanjust B (2011) Behavioral analysis of choice of daily route with data from global positioning system. Transp Res Rec 2230:96–103CrossRefGoogle Scholar
  50. Tobin J (1958) Estimation of relationships for limited dependent variables. Econometrica 26(1):24–36CrossRefGoogle Scholar
  51. Wachs M (1967) Relationships between drivers’ attitudes toward alternate routes and driver and route characteristics. Highw Res Rec 197:70–87Google Scholar
  52. Weijermars W, Gitelman V, Papadimitriou E, Lima de Azevedo C (2008) Safety performance indicators for the road network. Proceedings of the European Transport Conference 2008. CD-ROM. The Netherlands: Association for European TransportGoogle Scholar
  53. Wolf J, Hallmark S, Oliveira M, Guensler R, Sarasua W (1999) Accuracy issues with route choice data collection by using global positioning system. Highw Res Rec 1660:66–74CrossRefGoogle Scholar
  54. Zhang L, Levinson D (2008) Determinants of route choice and value of traveler information: a field experiment. Highw Res Rec 2086:81–92CrossRefGoogle Scholar
  55. Zhu S (2010) The roads taken: theory and evidence on route choice in the wake of the I-35 W Mississippi River Bridge Collapse and Reconstruction. Ph.D. Dissertation, University of MinnesotaGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Katrien Ramaekers
    • 1
  • Sofie Reumers
    • 2
  • Geert Wets
    • 2
  • Mario Cools
    • 3
    • 2
  1. 1.Research Group LogisticsHasselt UniversityDiepenbeekBelgium
  2. 2.Transportation Research Institute (IMOB)Hasselt UniversityDiepenbeekBelgium
  3. 3.Transport, Logistique, Urbanisme, Conception (TLU + C)Université de LiègeLiègeBelgium

Personalised recommendations