Door-to-door travel times in RP departure time choice models: An approximation method using GPS data

Transportation Research Part B: Methodological

Volumn 58, Issue , 2013, Pages 134-150

  Peer, Stefanie ^a,b   Knockaert, Jasper ^a   Koster, Paul ^a,c   Tseng, Yin Yen ^a,d   Verhoef, Erik T ^a,c  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

^b VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

^c TINBERGEN INSTITUTE   (Netherlands)

^d SOUTHWESTERN UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Departure time choice; Door to door travel times; Geographically weighted regression (GWR); GPS data; Revealed preference (RP) data; Valuation of travel time and schedule delays

Indexed keywords

GLOBAL POSITIONING SYSTEM; SPEED REGULATORS; TRAFFIC CONTROL; TRANSPORTATION;

DEPARTURE TIME CHOICE; GEOGRAPHICALLY WEIGHTED REGRESSION; GPS DATA; REVEALED PREFERENCE; SCHEDULE DELAY;

TRAVEL TIME;

GPS; MODEL TEST; REGRESSION ANALYSIS; TEMPORAL VARIATION; TRAVEL BEHAVIOR; TRAVEL TIME;

EID: 84887690400     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trb.2013.10.006     Document Type: Article

References (58)

1. Abkowitz M. An analysis of the commuter departure time decision. Transportation 1981, 10(3):283-297.
2. Aultman-Hall, L., Du, J., 2006. Using spatial analysis to estimate link travel times on local roads. In: Transportation Research Board 85th Annual Meeting, Washington, DC.
3. Bhat C. Accommodating flexible substitution patterns in multi-dimensional choice modeling: formulation and application to travel mode and departure time choice. Transportation Research Part B 1998, 32(7):455-466.
4. Bhat C. Analysis of travel mode and departure time choice for urban shopping trips. Transportation Research Part B 1998, 32(6):361-371.
5. Bhatta B., Larsen O. Errors in variables in multinomial choice modeling: a simulation study applied to a multinomial logit model of travel mode choice. Transport Policy 2011, 18(2):326-335.
6. Bierlaire, M., Chen, J., Newman, J., 2010. Modeling route choice behavior from smartphone GPS data. Report TRANSP-OR, Transport and Mobility Laboratory, Ecole Polytechnique Fédérale de Lausanne (101016).
7. Börjesson M. Joint RP-SP data in a mixed logit analysis of trip timing decisions. Transportation Research Part E 2008, 44(6):1025-1038.
8. Bowman A., Hall P., Prvan T. Bandwidth selection for the smoothing of distribution functions. Biometrika 1998, 85(4):799.
9. Brownstone D., Small K. Valuing time and reliability: assessing the evidence from road pricing demonstrations. Transportation Research Part A 2005, 39(4):279-293.
10. Brunsdon C., Fotheringham S., Charlton M. Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician) 1998, 47(3):431-443.
11. Carrion C., Levinson D. Valuation of travel time reliability from a GPS-based experimental design. Transportation Research Part C 2013, 35:305-323.
12. Castro P., Zhang D., Li S. Urban traffic modelling and prediction using large scale taxi GPS traces. Pervasive Computing, Lecture Notes in Computer Science 2012, 7319:57-72.
13. Charlton, M., Fotheringham, A., 2009. Geographically weighted regression (White Paper). National Centre for Geocomputation, National University of Ireland Maynooth.
14. Chen W., Li Z., Yu M., Chen Y. Effects of sensor errors on the performance of map matching. Journal of Navigation 2005, 58(2):273-282.
15. Choi K., Chung Y. A data fusion algorithm for estimating link travel time. ITS Journal 2002, 7(3-4):235-260.
16. Chu, L., Oh, S., Recker, W., 2005. Adaptive Kalman filter based freeway travel time estimation. In: Transportation Research Board 84th Annual Meeting, Washington, DC.
17. Cleveland W. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 1979, 74(368):829-836.
18. Cosslett S. The trip timing decision for travel to work by automobile. The Institute of Transportation Studies 1977, The University of California, Berkely and Irvine, CA.
19. Daly, A., Hess, S., 2011. Simple approaches for random utility modeling with panel data. In: Transportation Research Board 90th Annual Meeting, Washington, DC.
20. De Fabritiis, C., Ragona, R., Valenti, G., 2008. Traffic estimation and prediction based on real time floating car data. In: Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Madeira, Portugal.
21. El Esawey M., Sayed T. A framework for neighbour links travel time estimation in an urban network. Transportation Planning and Technology 2012, 35(3):281-301.
22. Fan J., Gijbels I. Local Polynomial Modelling and its Applications 1996, Chapman & Hall/CRC, London, UK.
23. Fifer S., Greaves S., Rose J., Ellison R. A combined GPS/stated choice experiment to estimate values of crash-risk reduction. Journal of Choice Modelling 2011, 4(1):44-61.
24. Ghosh, A., 2001. Valuing time and reliability: Commuters' mode choice from a real time congestion pricing experiment. Doctoral dissertation, University of California, Irvine, CA.
25. Hackney J.K., Bernard M., Bindra S., Axhausen K.W. Predicting road system speeds using spatial structure variables and network characteristics. Journal of Geographical Systems 2007, 9(4):397-417.
26. Harris P., Fotheringham A., Crespo R., Charlton M. The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences 2010, 42(6):657-680.
27. Hensher D. Hypothetical bias, choice experiments and willingness to pay. Transportation Research Part B 2010, 44(6):735-752.
28. Herring, R., Hofleitner, A., Abbeel, P., Bayen, A., 2010. Estimating arterial traffic conditions using sparse probe data. In: Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Madeira, Portugal.
29. Idé, T., Kato, S., 2009. Travel-time prediction using Gaussian process regression: a trajectory-based approach. In: Proceedings of the 9th SIAM International Conference on Data Mining (SDM), Sparks, NV.
30. Jenelius E., Koutsopoulos H. Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B 2013, 53:64-81.
31. Kamarianakis Y., Prastacos P. Space-time modeling of traffic flow. Computers & Geosciences 2005, 31(2):119-133.
32. Knockaert, J., Ettema, D., Verhoef, E., Koster, P., Peer, S., Tseng, Y.-Y., 2012. Spitsmijden Gouda-Zoetermeer. http://www.spitsmijden.nl/downloads/SM2D_scientific_report_final.pdf.
33. Knockaert J., Tseng Y.-Y., Verhoef E., Rouwendal J. The Spitsmijden experiment: a reward to battle congestion. Transport Policy 2012, 24:260-272.
34. Krause A., Horvitz E., Kansal A., Zhao F. Toward community sensing. Proceedings of the 7th International Conference on Information Processing in Sensor Networks 2008, IEEE Computer Society, St. Louis, MO.
35. Lam T., Small K. The value of time and reliability: measurement from a value pricing experiment. Transportation Research Part E 2001, 37(2-3):231-251.
36. Leduc, G., 2008. Road traffic data: collection methods and applications. Working Papers on Energy, Transport and Climate Change: European Commission Joint Research Centre, Institute for Prospective Technological Studies.
37. Li Z., Hensher D., Rose J. Willingness to pay for travel time reliability in passenger transport: a review and some new empirical evidence. Transportation Research Part E 2010, 46(3):384-403.
38. McCafferty D., Hall F. The use of multinomial logit analysis to model the choice of time to travel. Economic Geography 1982, 236-246.
39. Miura H. A study of travel time prediction using universal kriging. Top 2010, 18(1):257-270.
40. Modelit, 2009. TRIP Handleiding. http://www.modelit.nl/modelit/pdf/MVManual.pdf.
41. Peer, S., Knockaert, J., Koster, P., Verhoef, E., 2013. Overreporting vs. Overreacting: Commuters' Perceptions of Travel Times. Tinbergen Institute Discussion Paper TI 2013-123.
42. Quddus M.A., Ochieng W.Y., Noland R.B. Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transportation Research Part C 2007, 15(5):312-328.
43. Ramos, G.M., Frejinger, E., Daamen, W., Hoogendoorn, S., 2012. A revealed preference study on route choices in a congested network with real-time information. In: Proceedings of the 13th International Conference on Travel Behaviour Research, Toronto, Canada, pp. 15-20.
44. Small K. The scheduling of consumer activities: work trips. The American Economic Review 1982, 72(3):467-479.
45. Small K. A discrete choice model for ordered alternatives. Econometrica: Journal of the Econometric Society 1987, 55(2):409-424.
46. Small K., Winston C., Yan J. Uncovering the distribution of motorists' preferences for travel time and reliability. Econometrica 2005, 73(4):1367-1382.
47. Stone C. Consistent nonparametric regression. The Annals of Statistics 1977, 5(4):595-620.
48. Train K. Discrete Choice Models with Simulation 2003, Cambridge University Press, Cambridge, UK.
49. Tseng Y., Knockaert J., Verhoef E. A revealed-preference study of behavioural impacts of real-time traffic information. Transportation Research Part C 2013, 30:196-209.
50. Tseng, Y., Rietveld, P., Verhoef, E., 2005. A meta-analysis of valuation of travel time reliability. http://www.transumofootprint.nl.
51. Van Lint J. Empirical evaluation of new robust travel time estimation algorithms. Transportation Research Record 2010, 2160:50-59.
52. Vickrey W. Congestion theory and transport investment. The American Economic Review 1969, 59(2):251-260.
53. Wang H., Yue Y., Li Q. How many probe vehicles are enough for identifying traffic congestion? - A study from a streaming data perspective. Frontiers of Earth Science 2013, 7(1):34-42.
54. Washington S., Ravulaparthy S., Rose J., Hensher D., Pendyala R. Bayesian imputation of non-chosen attribute values in revealed preference surveys. Journal of Advanced Transportation 2012, 10.1002/atr.201.
55. Westgate, B. S., Woodard, D. B., Matteson, D. S., Henderson, S. G., 2011. Travel time estimation for ambulances using Bayesian data augmentation. School of Operations Research and Information Engineering, Cornell University.
56. Wheeler D., Páez A. Geographically weighted regression. Handbook of Applied Spatial Analysis 2010, 461-486.
57. Zito R., d'Este G., Taylor M.A. Global positioning systems in the time domain: How useful a tool for intelligent vehicle-highway systems?. Transportation Research Part C 1995, 3(4):193-209.
58. Zou H., Yue Y., Li Q., Yeh A.G. An improved distance metric for the interpolation of link-based traffic data using kriging: a case study of a large-scale urban road network. International Journal of Geographical Information Science 2012, 26(4):667-689.