Updating of Travel Behavior Model Parameters and Estimation of Vehicle Trip Chain Based on Plate Scanning

REFERENCES

• Arentze, T.A., & Timmermans, H.J. P. (2004). A learning-based transportation oriented simulation system. Transportation Research Part B, 38(7), 613–633.
   Google Scholar
• Axhausen, K.W., Schönfelder, S., Wolf, J., Oliveira, M., & Samaga, U. (2003). 80 weeks of GPS-traces: Approaches to enriching the trip information, Arbeitsbericht Verkehrs- und Raumplanung, 178, Institut für Verkehrsplanung und Transportsysteme, ETH Zürich, Zürich.
   Google Scholar
• Bell, M.G. H. (1991). The estimation of origin-destination matrices by constrained generalised least squares. Transportation Research Part B, 25(1), 13–22.
   Google Scholar
• Ben-Akiva, M., & Lerman, S.R. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT Press.
   Google Scholar
• Bhat, C.R., & Koppelmen, F.S. (1999). Handbook of transportation science. New York, NY: Kluwer Academic.
   Google Scholar
• Bifulco, G., Cartenì, A., & Papola, A. (2010). An activity-based approach for complex travel behaviour modelling. European Transport Research Review, 2, 209–221.
   Google Scholar
• Bricka, S., & Bhat, C.R. (2006). A Comparative analysis of GPS-based and travel survey-based data. Transportation Research Record, 1972, 9–20.
   Google Scholar
• Bowman, J.L., & Bradley, M.A. (2008, October). Activity-based model: Approaches used to achieve integration among trips and tours throughout the day. Paper presented at the European Transport Conference, Leeuwenhorst, The Netherlands.
   Google Scholar
• Bowman, J.L., Bradley, M.A., & Gibb, J. (2006, September). The Sacramento activity-based travel demand model:  estimation and validation results. Paper presented at the European Transport Conference, Strasbourg, France.
   Google Scholar
• Cascetta, E. (1984). Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator. Transportation Research Part B, 18, 289–299.
   Google Scholar
• Cascetta, E., Inaudi, D., & Marquis, G. (1993). Dynamic estimators of origin–destination matrices using traffic counts. Transportation Science, 27(4), 363–373.
   Web of Science ®Google Scholar
• Cascetta, E., & Russo, F. (1997). Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance. Transportation, 24(3), 271–293.
   Web of Science ®Google Scholar
• Castillo, E., Menéndez, J.M., & Jiménez, P. (2008). Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations. Transportation Research Part B, 42(5), 455–481.
   Google Scholar
• Castillo, E., Gallego, I., Menéndez, J.M., & Jiménez, P. (2011): Link flow estimation in traffic networks on the basis of link flow observations, Journal of Intelligent Transportation Systems: Technology,Planning, and Operations, 15(4), 205–222.
   Web of Science ®Google Scholar
• Cools, M., Moons, E., & Wets, G. (2010). Calibrating activity-based models with external origin–destination information: overview of possibilities. Transportation Research Record, 2175, 98–110.
   Web of Science ®Google Scholar
• Dijst, M., & Vidakovic, V. (1997). Individual action space in the city. Activity analyses. Oxford, UK: Pergamon.
   Google Scholar
• Feil, M., Balmer, M., & Axhausen, K.W. (2009). Generating comprehensive all-day schedules: expanding activity-based travel demand modelling. European Transport Planning Colloquium, October, Leiden Leeuwenhorst Conference Centre, The Netherlands.
   Google Scholar
• Flötteröd, G., Chen, U., & Nagel, K. (2011). Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics, A Journal of Infrastructure Modeling and Computation, 12, 1–22.
   Google Scholar
• Frignani, M.Z., Auld, J., Mohammadian, A.K., & Williams, C. (2010). Urban travel route and activity choice survey: Internet-based prompted-recall activity travel survey using global positioning system data. Transportation Research Record, 2183, 19–28.
   Web of Science ®Google Scholar
• Hu, S.R., Madanat, S., Krogmeier, J., & Peeta, S. (2001). Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 6(3), 281–300.
   Web of Science ®Google Scholar
• Li, Z.C., Lam, W.H. K, Wong S.C., & Sumalee A. (2010). An activity-based approach for scheduling multimodal transit services. Transportation, 37(5), 751–774.
   Web of Science ®Google Scholar
• Lao, Y., Zhang, G., Corey, J., & Wang, Y. (2012). Gaussian mixture model-based speed estimation and vehicle classification using single-loop measurements. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 16(4), 184–196.
   Web of Science ®Google Scholar
• Maruyama, T., & Sumalee, A. (2007). Efficiency and equity comparison of cordon- and area-based road pricing schemes using a trip-chain equilibrium model. Transportation Research Part A, 41(7), 655–671.
   Google Scholar
• Maher, M.J. (1983). Inferences on trip matrices from observations on link volumes: A bayesian statistical approach. Transportation Research Part B, 17(6), 435–447.
   Google Scholar
• Ortuzar, J.D., & Willumsen, L.G. (2011). Modelling transport (4th ed.). Chichester, UK: Wiley.
   Google Scholar
• Ozbay, S., & Ercelebi, E. (2005). Automatic vehicle identification by plate recognition. World Academy of Science, Engineering and Technology, 9, 222–225.
   Google Scholar
• Velaga, N.R., Quddus, M.A., & Bristow, A.L. (2012). Improving the performance of a topological map-matching algorithm through error detection and correction, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 16(3), 147–158.
   Web of Science ®Google Scholar
• Vovsha, P., Bradley, M., & Bowman, J.L. (2004, May). Activity-based travel forecasting models in the United States: Progress since 1995 and prospects for the future. Paper presented at the EIRASS Conference on Progress in Activity-Based Analysis, Maastricht, The Netherlands.
   Google Scholar
• Watling, D.P. (1994). Maximum likelihood Estimation of an origin-destination matrix from a partial registration plate survey. Transportation Research Part B, 28(4), 289–314.
   Google Scholar
• Watling, D.P., & Maher, M.J. (1992). A statistical procedure for estimating a mean origin-destination matrix from a partial registration plate survey. Transportation Research Part B, 26(3), 171–193.
   Google Scholar
• Yang, H., Sasaki, T., Iida, Y., & Asakura, Y. (1992). Estimation of origin-destination matrices from link traffic counts on congested networks. Transportation Research Part B, 26(6), 417–434.
   Google Scholar
• Yang, H., & Zhou, J. (1998). Optimal traffic counting locations for origin-destination matrix estimation. Transportation Research Part B, 32(2), 109–126.
   Google Scholar
• Yang, H. (1995). Heuristic algorithms for the bilevel origin–destination matrix estimation problem. Transportation Research Part B, 29, 231–242.
   Google Scholar
• Yuan, Y., Lint, H.V., Wageningen-Kessels, F.V., & Hoogendoorn, S. (2013) Network-wide traffic state estimation using loop detector and floating car data. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 18, 41–50.
   Web of Science ®Google Scholar