Calibration of choice model parameters in a transport scenario with heterogeneous traffic conditions and income dependency

References

• Agarwal, A. 2012. “Agent Based Simulation of the Travel Demand for Patna City, India.” Master’s thesis, Indian Institute of Technology, Delhi, India.
   Google Scholar
• Agarwal, A. 2017. “Mitigating Negative Transport Externalities in Industrialized and Industrializing Countries.” PhD diss., TU Berlin, Berlin. doi: 10.14279/depositonce-5825.
   Google Scholar
• Agarwal, A., G. Flötteröd, and K. Nagel. 2017. “Calibration of Behavioural Parameters Using Optimization Technique in an Agent-Based Transport Simulation.” In 6th Symposium of the European Association for Research in Transportation. Haifa, Israel.
   Google Scholar
• Agarwal, A., and G. Lämmel. 2016. “Modeling Seepage Behavior of Smaller Vehicles in Mixed Traffic Conditions Using an Agent Based Simulation.” Transp. In Dev. Econ. 2 (2): 1–12. doi:10.1007/s40890-016-0014-9.
   Web of Science ®Google Scholar
• Agarwal, A., G. Lämmel, and K. Nagel. 2016. “Modelling of Backward Travelling Holes in Mixed Traffic Conditions.” In Traffic and Granular Flow ‘15, edited by V. L. Knoop and W. Daamen, 1st ed., Chap. 53, 419–426. doi: 10.1007/978-3-319-33482-0_53. Delft, NL: Springer International Publishing.
   Google Scholar
• Agarwal, A., G. Lämmel, and K. Nagel. 2017. Incorporating within Link Dynamics in an Agent-Based Computationally Faster and Scalable Queue Model. Transportmetrica A: Transport Science: 520 – 541. doi: 10.1080/23249935.2017.1364802
   Google Scholar
• Agarwal, A., M. Zilske, K. R. Rao, and K. Nagel. 2015. “An Elegant and Computationally Efficient Approach for Heterogeneous Traffic Modelling Using Agent Based Simulation.” Procedia Computer Science 52 (C): 962–967. doi:10.1016/j.procs.2015.05.173.
   Google Scholar
• Balmer, M., K. Meister, M. Rieser, K. Nagel, and K. W. Axhausen. 2008. “Agent-Based Simulation of Travel Demand: Structure and Computational Performance of MATSim-T.” In 2nd TRB conference on Innovations in Travel Modeling (ITM) Portland, June 2008. Also VSP WP 08-07, see http://www.vsp.tu-berlin.de/publications
   Google Scholar
• Barmpounakis, E. N., E. I. Vlahogianni, J. C. Golias, and A. Babinec. 2017. “How Accurate are Small Drones for MeasuringMicroscopic Traffic Parameters?” Transportation Letters 1–9, 332 – 340. doi:10.1080/19427867.2017.1354433.
   Google Scholar
• Bell, M. G. H. 1983. “The Estimation of an Origin-Destination Matrix from Traffic Counts.” Transportation Science 17 (2): 198–217. doi:10.1287/trsc.17.2.198.
   Web of Science ®Google Scholar
• Bliemer, M. C. J. 2007. “Dynamic Queuing and Spillback in Analytical Multiclass Dynamic Network Loading Model.” Transportation Research Record: Journal of the Transportation Research Board 2029: 14–21. doi:10.3141/2029-02.
   Web of Science ®Google Scholar
• Cascetta, E., D. Inaudi, and G. Marquis. 1993. “Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts.” Transportation Science 27 (4): 363–373. doi:10.1287/trsc.27.4.363.
   Web of Science ®Google Scholar
• Cetin, N., A. Burri, and K. Nagel. 2003. “A Large-Scale Agent-Based Traffic Microsimulation Based On Queue Model.” In Swiss Transport Research Conference (STRC), Monte Verita, Switzerland. See http://www.strc.ch
   Google Scholar
• Chandra, S., and P. K. Sikdar. 2000. “Factors Affecting PCU in Mixed Traffic Situations on Urban Roads.” Road and Transport Research 9 (3): 40–50.
   Google Scholar
• Charypar, D., and K. Nagel. 2005. “Generating Complete All-Day Activity Plans with Genetic Algorithms.” Transportation 32 (4): 369–397. doi:10.1007/s11116-004-8287-y.
   Web of Science ®Google Scholar
• Chen, H.-K., and C.-F. Hsueh. 1998. “A Model and an Algorithm for the Dynamic User- Optimal Route Choice Problem.” Transportation Research Part B: Methodological 32 (3): 219–234. doi:10.1016/S0191-2615(97)00026-X.
   Web of Science ®Google Scholar
• Chen, J., and M. Bierlaire. 2014. “Probabilistic Multimodal Map Matching With Rich Smartphone Data.” Journal of Intelligent Transportation Systems 19 (2): 134–148. doi:10.1080/15472450.2013.764796.
   Web of Science ®Google Scholar
• Chung, E.-H., and A. Shalaby. 2005. “A Trip Reconstruction Tool for GPS-based Personal Travel Surveys.” Transportation Planning and Technology 28 (5): 381–401. doi:10.1080/03081060500322599.
   Web of Science ®Google Scholar
• Currin, T. R. 2012. Introduction to Traffic Engineering: A Manual for Data Collection and Analysis. USA: Cengage Learning.
   Google Scholar
• Domencich, T., and D. L. McFadden. 1996. “Urban Travel Demand: A Behavioral Analysis.” North- Holland Publishing Company. http://eml.berkeley.edu/∼mcfadden/travel.html
   Google Scholar
• Flötteröd, G. 2010. “Cadyts – Calibration of Dynamic Traffic Simulations – Version 1.1.0 Manual.442849027495500.” Transport and Mobility Laboratory, É cole Polytechnique Fédérale de Lausanne. http://home.abe.kth.se/~gunnarfl/files/cadyts/Cadytsmanual1-1-0.pdf
   Google Scholar
• Flötteröd, G., M. Bierlaire, and K. Nagel. 2011. “Bayesian Demand Calibration for Dynamic Traffic Simulations.” Transportation Science 45 (4): 541–561. doi:10.1287/trsc.1100.0367.
   Web of Science ®Google Scholar
• Flötteröd, G., Y. Chen, and K. Nagel. 2011. “Behavioral Calibration and Analysis of a Large- Scale Travel Microsimulation.” Networks and Spatial Economics 12 (4): 481–502. doi:10.1007/s11067-011-9164-9.
   Web of Science ®Google Scholar
• Franklin, J. P. 2006. “The Distributional Effects of Transportation Policies: The Case of a Bridge Toll for Seattle.” PhD diss., University of Washington, Seattle.
   Google Scholar
• Gawron, C. 1998. “An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model.” International Journal of Modern Physics C 9 (3): 393–407. doi:10.1142/S0129183198000303.
   Web of Science ®Google Scholar
• Groves, R. M. 2006. “Nonresponse Rates and Nonresponse Bias in Household Surveys.” The Public Opinion Quarterly 70 (5): 646–675. http://www.jstor.org/stable/4124220
   Web of Science ®Google Scholar
• Hood, J., E. Sall, and B. Charlton. 2011. “A GPS-based Bicycle Route Choice Model for San Francisco, California.” Transportation Letters (3): 63–75. doi:10.3328/TL.2011.03.01.63-75.
   Web of Science ®Google Scholar
• Horni, A., K. Nagel, and K. W. Axhausen, eds. 2016. The Multi-Agent Transport Simulation MATSim. Ubiquity: London. http://matsim.org/the-book
   Google Scholar
• Iqbal, S., C. Choudhury, P. Wang, and M. C. González. 2014. “Development of Origin-Destination Matrices Using Mobile Phone Call Data.” Transportation Research Part C 40: 63–74. doi:10.1016/j.trc.2014.01.002.
   Web of Science ®Google Scholar
• IRC:SP:30. 2009. Manual on Economic Evaluation of Highway Projects in India. New Delhi, India: Indian Roads Congress.
   Google Scholar
• Kickhöfer, B., and K. Nagel. 2016. “Microeconomic Interpretation of MATSim for Benefit- Cost Analysis.” In The Multi-Agent Transport Simulation MATSim, edited by A. Horni, K. Nagel, and K. W. Axhausen, Chap. 51. Ubiquity, London. http://matsim.org/the-book
   Google Scholar
• Kumar, C. V. P., D. Basu, and B. Maitra. 2004. “Modeling Generalized Cost of Travel for Rural Bus Users: A Case Study.” Journal of Public Transportation 7 (2): 59–72. doi:10.5038/2375-0901.7.2.4.
   Google Scholar
• Kuwahara, M., and E. C. Sullivan. 1987. “Estimating Origin-Destination Matrices from Roadside Survey Data.” Transportation Research Part B: Methodological 21 (3): 233–248. doi:10.1016/0191-2615(87)90006-3.
   Web of Science ®Google Scholar
• Lam, W. H. K., and H.-J. Huang. 1995. “Dynamic User Optimal Traffic Assignment Model for Many to One Travel Demand.” Transportation Research Part B: Methodological 29 (4): 243–259. doi:10.1016/0191-2615(95)00001-T.
   Web of Science ®Google Scholar
• Lee, R. J., I. N. Sener, and J. A. Mullins III. 2016. “An Evaluation of Emerging Data Collection Technologies for Travel Demand Modeling: From Research to Practice.” Transportation Letters 8 (4): 181–193. doi:10.1080/19427867.2015.1106787.
   Web of Science ®Google Scholar
• Moyo Oliveros, M., and K. Nagel. 2012. “Automatic Calibration of Microscopic, Activity-Based Demand for a Public Transit Line.” In Annual Meeting Preprint 12–3279. Washington, D.C.: Transportation Research Board. Also VSP WP 11-13, see. http://www.vsp.tu-berlin.de/publications
   Google Scholar
• Nagel, K., and G. Flötteröd. 2012. “Agent-Based Traffic Assignment: Going from Trips to Behavioural Travelers.” In Travel Behaviour Research in an Evolving World – Selected papers from the 12th international conference on travel behaviour research, edited by R. M. Pendyala and C. R. Bhat, 261–294. Toronto, Canada: International Association for Travel Behaviour Research. .
   Google Scholar
• Rieser-Schüssler, N. 2012. “Capitalising Modern Data Sources for Observing and Modelling Transport Behaviour.” Transportation Letters 4 (2): 115–128. doi:10.3328/TL.2012.04.02.115-128.
   Web of Science ®Google Scholar
• Shen, L., and P. R. Stopher. 2014. “Review of GPS Travel Survey and GPS Data-Processing Methods.” Transport Reviews 34 (3): 316–334. doi:10.1080/01441647.2014.903530.
   Web of Science ®Google Scholar
• Simon, P. M., J. Esser, and K. Nagel. 1999. “Simple Queueing Model Applied to the City of Portland.” International Journal of Modern Physics 10 (5): 941–960. doi:10.1142/S0129183199000747.
   Web of Science ®Google Scholar
• Stipancic, J., L. MIranda-Moreno, A. Labbe, and N. Saunier. 2017. “Measuring and Visualizing Space–Time Congestion Patterns in an Urban Road Network Using Large-Scale Smartphone- Collected GPS Data.” Transportation Letters 1–11. doi:10.1080/19427867.2017.1374022.
   Web of Science ®Google Scholar
• Szeto, W. 2008. “Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment.” Transportation Research Record: Journal of the Transportation Research Board 2085: 76–85. doi:10.3141/2085-09.
   Web of Science ®Google Scholar
• Szeto, W., and S. Wong. 2012. “Dynamic Traffic Assignment: Model Classifications and Recent Advances in Travel Choice Principles.” Open Engineering 2 (1): 1–18. doi:10.2478/s13531-011-0057-y.
   Google Scholar
• Szeto, W. Y., Y. Jiang, and A. Sumalee. 2011. “A Cell-Based Model for Multi-Class Doubly Stochastic Dynamic Traffic Assignment.” Computer-Aided Civil and Infrastructure Engineering 26 (8): 595–611. doi:10.1111/mice.2011.26.issue-8.
   Web of Science ®Google Scholar
• TRIPP, iTrans, and VKS. 2009. “Comprehensive Mobility Plan for Patna Urban Agglomeration Area.” Technical Report. Department of Urban Development. Government of Bihar.
   Google Scholar
• van Zuylen, H., and L. G. Willumsen. 1980. “The Most Likely Trip Matrix Estimated from Traffic Counts.” Transportation Research 14B: 281–293. doi:10.1016/0191-2615(80)90008-9.
   Google Scholar
• Wolf, J. 2000. “Using GPS Data Loggers to Replace Travel Diaries in the Collection of Travel Data.” PhD diss., Georgia Institute of Technology.
   Google Scholar
• Ziemke, D., and K. Nagel. 2017. “Development of a Fully Synthetic and Open Scenario for Agent- Based Transport Simulations – The MATSim Open Berlin Scenario.” VSP Working Paper 17–12. TU Berlin, Transport Systems Planning and Transport Telematics. http://www.vsp.tu-berlin.de/publications
   Google Scholar
• Ziemke, D., K. Nagel, and C. Bhat. 2015. “Integrating CEMDAP and MATSim to Increase the Transferability of Transport Demand Models.” Transportation Research Record 2493: 117–125. doi:10.3141/2493-13.
   Google Scholar
• Zilske, M., and K. Nagel. 2015. “A Simulation-Based Approach for Constructing All-Day Travel Chains from Mobile Phone Data.” Procedia Computer Science 52: 468–475. doi:10.1016/j.procs.2015.05.017.
   Google Scholar
• Zimowski, M., R. Tourangeau, R. Ghadialy, and S. Pedlow. 1997. “Nonresponse in Household Travel Surveys.” Technical Report. Federal Highway Administration.
   Google Scholar