Abstract
Emerging technologies provide a venue on which on-line traffic controls and management systems can be implemented. For such applications, having access to accurate predictions on travel-times are mandatory for their successful operations. Transportation engineers have developed numerous approaches including model-based approaches. The model-based approaches consider underlying traffic mechanisms and behaviors in developing the prediction procedures and they are logically intuitive unlike datadriven approaches. Because of this explanation power, the model-based approaches have been developed for the on-line control purposes. For departments of transportation (DOTs), it is still a challenge to choose a specific approach that meets their requirements. In efforts to develop a unique guideline for transportation engineers and decision makers when considering for implementing modelbased approaches for highways, this paper reviews model-based travel-time prediction approaches by classifying them into four categories according to the level of details involved in the model: Macroscopic, Mesoscopic, CA-based, and Microscopic. Then each method is evaluated from five main perspectives: Prediction range, Accuracy, Efficiency, Applicability, and Robustness. Finally, this paper concludes with evaluations of model-based approaches in general and discusses them in relation to data-driven approaches along with future research directions.
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References
Abdulhai, Baher; Porwal, Himanshu, and Recker, Will. (1999). Short Term Freeway Traffic Flow Prediction Using Genetically-Optimized Time-Delay-Based Neural Networks, California Partners for Advanced Transit and Highways (PATH). UC Berkeley: California Partners for Advanced Transportation Technology.
Azevedo, C, Oh, S., Deshmunk, N., Marimuthu, B., Marczuk, K., Soh, H., Basak, K., Toledo, T., Peh, L., Ben-Akiva, M. (2017). “SimMobility Short-term: An integrated microscopic mobility simulator.” In Transportation Research Board 96th Annual Meeting, Nos. 17-06621.
Azevedo, C. L., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., and Ben-Akiva, M. (2016). “Microsimulation of demand and supply of autonomous mobility on demand.” Transportation Research Record: Journal of the Transportation Research Board, 2564), pp. 21–30.
Balakrishna, R. (2006). Off-line calibration of dynamic traffic assignment models (Doctoral dissertation, Massachusetts Institute of Technology).
Behrisch, M., Bieker, L., Erdmann, J., and Krajzewicz, D. (2011). “SUMO–simulation of urban mobility: An overview. In Proceedings of SIMUL 2011.” The Third International Conference on Advances in System Simulation. ThinkMind. (ISBN 978-1-61208-169-4).
Bell, M. G., Shield, C. M., Busch, F., and Kruse, G. (1997). “A stochastic user equilibrium path flow estimator.” Transportation Research Part C: Emerging Technologies, Vol. 5, No. 3, pp. 197–210, DOI: 10.1016/S0968-090X(97)00009-0.
Ben-Akiva, M., Bierlaire, M., Burton, D., Koutsopoulos, H. N., and Mishalani, R. (2001). “Network state estimation and prediction for real-time traffic management.” Networks and Spatial Economics, Vol. 1, Nos. 3-4, pp. 293–318, DOI: 10.1023/A:1012883811652.
Casas, J., Torday, A., Perarnau, J., Breen, M., and Ruiz de Villa, A. (2013, October). “Present and future methodology for the implementation of decision support systems for traffic management.” In Australasian Transport Research Forum (ATRF), 36th, 2013, Brisbane, Queensland, Australia.
Chen, H., Grant-Muller, S., Mussone, L., and Montgomery, F. (2001). “A study of hybrid neural network approaches and the effects of missing data on traffic forecasting.” Neural Computing & Applications, Vol. 10, No. 3, pp. 277–286, DOI: 10.1007/s521-001-8054-3.
Chen, M. and Chien, S. I. (2001). “Dynamic freeway travel-time prediction with probe vehicle data: Link based versus path based.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1768, No. 1, pp. 157–161, DOI: 10.3141/1768-19.
Chow, A., Dadok, V., Dervisoglu, G., Gomes, G., Horowitz, R., Kurzhanskiy, A. A., Kwon, J., Lu, X.-Y., Muralidharan, A., Norman, S., Sá nchez, R. O., and Varaiya, P. (2008, January). “Topl: Tools for operational planning of transportation networks.” In ASME 2008 Dynamic Systems and Control Conference, pp. 1035–1042. American Society of Mechanical Engineers.
Chrobok, R. (2005). Theory and Application of Advanced Traffic Forecast Methods (Doctoral dissertation, Universitätsbibliothek Duisburg).
Chrobok, R., Hafstein, S. F., and Pottmeier, A. (2004). “Olsim: A new generation of traffic information systems.” Forschung und wissenschaftliches Rechnen, Vol. 63, pp. 11–25, DOI: 10.1.1.90.8400.
Chrobok, R., Pottmeier, A., ur Marinosson, S., and Schreckenberg, M. (2002). “On-line simulation and traffic forecast: Applications and results.” Cell, Vol. 3, No. 1, pp. 2–2.
D’Angelo, M. P., Al-Deek, H. M., and Wang, M. C. (1999). “Traveltime prediction for freeway corridors.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1676, No. 1, pp. 184–191, DOI: 10.3141/1676-23.
Daganzo, C. F. (1994). “The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory.” Transportation Research Part B: Methodological, Vol. 28, No. 4, pp. 269–287, DOI: 10.1016/0191-2615(94)90002-7.
Daganzo, C. F. (1995). “The cell transmission model, part II: Network traffic.” Transportation Research Part B: Methodological, Vol. 29, No. 2, pp. 79–93, DOI: 10.1016/0191-2615(94)00022-R.
Davis, G. A. and Nihan, N. L. (1991). “Nonparametric regression and short-term freeway traffic forecasting.” Journal of Transportation Engineering, Vol. 117, No. 2, pp. 178–188, DOI: 10.1061/(ASCE) 0733-947X(1991)117:2(178).
Davis, G. A., Nihan, N. L., Hamed, M. M., and Jacobson, L. N. (1990). “Adaptive forecasting of freeway traffic congestion.” Transportation Research Record, Vol. 1287, DOI: 10.1016/S1874-1029(08)60062-2.
Dervisoglu, G. (2011). TOPL Project Review presentation material (source: http://path.berkeley.edu/topl).
Dia, H. (2001). “An object-oriented neural network approach to shortterm traffic forecasting.” European Journal of Operational Research, Vol. 131, No. 2, pp. 253–261, DOI: 10.1016/S0377-2217(00)00125-9.
Dougherty, M. S. and Cobbett, M. R. (1997). “Short-term inter-urban traffic forecasts using neural networks.” International Journal of Forecasting, Vol. 13, No. 1, pp. 21–31, DOI: 10.1016/S0169-2070(96)00697-8.
Esser, J. and Schreckenberg, M. (1997). “Microscopic simulation of urban traffic based on cellular automata.” International Journal of Modern Physics C, Vol. 8, No. 5, pp. 1025–1036, DOI: 10.1142/S0129183197000904.
Gipps, P. G. (1981). “A behavioural car-following model for computer simulation.” Transportation Research Part B: Methodological, Vol. 15, No. 2, pp. 105–111, DOI: 10.1016/0191-2615(81)90037-0.
Hafstein, S., Chrobok, R., Pottmeier, A., Schreckenberg, M., and C Mazur, F. (2004). “A high-resolution cellular automata traffic simulation model with application in a freeway traffic information system.” Computer-Aided Civil and Infrastructure Engineering, Vol. 19, No. 5, pp. 338–350, DOI: 10.1111/j.1467-8667.2004.00361.x.
Mahmassani, H. S., Fei, X., Eisenman, S., Zhou, X., and Qi, X. (2005). DYNASMART-X evaluation for real-time TMC application: CHART test bed. Maryland Transportation Initiative, University of Maryland, College Park, Maryland.
Hoogendoorn, S. P. and Bovy, P. H. (2001). “State-of-the-art of vehicular traffic flow modelling.” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Vol. 215, No. 4, pp. 283–303, DOI: 10.1177/095965180121500402.
Hoogendoorn, S. P., Schuurman, H., and De Schutter, B. (2003, August). “Real-time traffic management scenario evaluation.” In Proceedings of the 10th IFAC Symposium on Control in Transportation Systems (CTS 2003), pp. 343–348.
Innamaa, S. (2005). “Short-term prediction of travel-time using neural networks on an interurban highway.” Transportation, Vol. 32, No. 6, pp. 649–669, DOI: 10.1007/s11116-005-0219-y.
Ishak, S. and Al-Deek, H. (2002). “Performance evaluation of shortterm time-series traffic prediction model.” Journal of Transportation Engineering, Vol. 128, No. 6, pp. 490–498, DOI: 10.1061/(ASCE) 0733-947X(2002)128:6(490).
Kerner, B. S. (1998). “Experimental features of self-organization in traffic flow.” Physical Review Letters, Vol. 81, pp. 3797–3800, DOI: 10.1103/PhysRevLett.81.3797.
Kerner, B., Kirschfink, H., and Rehborn, H. (1999). U.S. Patent No. 5,861,820. Washington, DC: U.S. Patent and Trademark Office.
Kirby, H. R., Watson, S. M., and Dougherty, M. S. (1997). “Should we use neural networks or statistical models for short-term motorway traffic forecasting?.” International Journal of Forecasting, Vol. 13, No. 1, pp. 43–50, DOI: 10.1016/S0169-2070(96)00699-1.
Kotsialos, A., Papageorgiou, M., Diakaki, C., Pavlis, Y., and Middelham, F. (2002). “Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET.” Intelligent Transportation Systems, IEEE Transactions on, Vol. 3, No. 4, pp. 282–292, DOI: 10.1109/TITS.2002.806804.
Kwon, J., Coifman, B., and Bickel, P. (2000). “Day-to-day travel-time trends and travel-time prediction from loop-detector data.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1717, No. 1, pp. 120–129, DOI: 10.3141/1717-15.
Leutzbach, W. and Wiedemann, R. (1986). “Development and applications of traffic simulation models at the Karlsruhe Institut für Verkehrswesen.” Traffic Engineering & Control, Vol. 27, No. 5, pp. 270–278.
Lighthill, M. J. and Whitham, G. B. (1955). “On kinematic waves. I: Flood movement in longrivers. II: A theory of traffic flow on long crowded roads.” Proc. Royal Soc. London, Ser. A 229, pp. 281–345.
Liu, X. (2004). Development of dynamic recursive models for freeway travel time prediction, Ph.D. thesis, New Jersey Institute of Technology.
Liu, Y., Lin, P. W., Lai, X., Chang, G. L., and Marquess, A. (2006). “Developments and applications of simulation-based online travel time prediction system: Traveling to Ocean City, Maryland.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1959, No. 1, pp. 92–104, DOI: 10.3141/1959-11.
Mahmassani, H. and Zhou, X. (2005). “Transportation system intelligence: Performance measurement and real-time traffic estimation and prediction in a day-to-day learning framework.” In Advances in Control, Communication Networks, and Transportation Systems, pp. 305-328, Birkhäuser Boston, DOI: 10.1007/0-8176-4409-1_16.
Messner, A. and Papageorgiou, M. (1990). “METANET: A macroscopic simulation program for motorway networks.” Traffic Engineering & Control, Vol. 31, Nos. 8-9, pp. 466–470, DOI: 10.1007/978-1-4419-6142-6_11.
Miska, M. P. (2007). Microscopic online simulation for real time traffic management (No. Trail Thesis Series T2007/1). Netherlands TRAIL Research School. (ISBN-13: 978-90-5584-082-3).
Nagel, K. and Schreckenberg, M. (1992). “A cellular automaton model for freeway traffic.” Journal de Physique I, Vol. 2, No. 12, pp. 2221–2229, DOI: 10.1051/jp1:1992277.
Nagel, K., Esser, J., and Rickert, M. (2000). “Large-scale traffic simulations for transportation planning.” Annual Review of Computational Physics, Vol. 7, pp. 151–202, DOI: 10.1.1.13.5188.
Ohba, Y., Ueno, H., and Kuwahara, M. (2001). “Travel time prediction method for expressway using toll collection system data.” In Proceedings of the 7th World Congress on Intelligent Systems, DOI: 10.1109/ITSC.1999.821103.
Papageorgiou, M., Papamichail, I., Messmer, A., and Wang, Y. (2010). “Traffic simulation with metanet.” In Fundamentals of Traffic Simulation pp. 399–430. Springer New York. Smulders et al., 1999, DOI: 10.1007/978-1-4419-6142-6_11.
Park, D. and Rilett, L. R. (1999). “Forecasting freeway link travel times with a multilayer feed forward neural network.” Computer-Aided Civil and Infrastructure Engineering, Vol. 14, No. 5, pp. 357–367, DOI: 10.1111/0885-9507.00154.
Payne, H. J. (1971). Models of freeway traffic and control, Mathematical models of public systems. (ISSN: 0037-5519)
Rice, J. and Van Zwet, E. (2004). “A simple and effective method for predicting travel times on freeways.” Intelligent Transportation Systems, IEEE Transactions on, Vol. 5, No. 3, pp. 200–207, DOI: 10.1109/TITS.2004.833765.
Richards, P. I. (1956). “Shock waves on the highway.” Operations Research, Vol. 4, No. 1, pp. 42–51, DOI: 10.1287/opre.4.1.42.
Shen, L. (2008). “Freeway travel-time estimation and prediction using dynamic neural networks.” January 1, 2008. ProQuest ETD Collection for FIU. Paper AAI3346733.
Smith, B. L. and Demetsky, M. J. (1997). “Traffic flow forecasting: comparison of modeling approaches.” Journal of Transportation Engineering, Vol. 123, No. 4, pp. 261–266, DOI: 10.1061/(ASCE) 0733-947X(1997)123:4(261).
Smith, B. L., Williams, B. M., and Oswald, R. K. (2000). Parametric and nonparametric traffic volume forecasting, In Transportation Research Board 79th Annual Meeting.
Sun, H., Liu, H. X., Xiao, H., He, R. R., and Ran, B. (2003, January). Short term traffic forecasting using the local linear regression model, In 82nd Annual Meeting of the Transportation Research Board, Washington, DC.
Torday, A. (2010). Simulation-based Decision Support System for Real Time Traffic Management, In Transportation Research Board 89th Annual Meeting (No. 10-2120).
van Lint, H. (2004). Reliable travel-time prediction for freeways, Netherlands TRAIL Research School, ISBN: 90-5584-054-8.
van Lint, J. W. (2006). “Reliable real-time framework for short-term freeway travel-time prediction.” Journal of Transportation Engineering, Vol. 132, No. 12, pp. 921–932, DOI: 10.1061/(ASCE)0733-947X (2006)132:12(921).
van Lint, J. W. C., Hoogendoorn, S. P., and van Zuylen, H. J. (2005). “Accurate freeway travel time prediction with state-space neural networks under missing data.” Transportation Research Part C: Emerging Technologies, Vol. 13, No. 5, pp. 347–369, DOI: 10.1016/j.trc.2005.03.001.
Vortisch, P. (2001). Use of PTV-Software in the traffic management centre (VMZ) Berlin, In th PTV Vision User Group Meeting, Berlin, Germany.
Vythoulkas, P. C. (1993). Alternative approaches to short term traffic forecasting for use in driver information systems, In International Symposium on the Theory of Traffic Flow and Transportation (12th: 1993: Berkeley, Calif.). Transportation and traffic theory.
Williams, B. M. (2001). “Multivariate vehicular traffic flow prediction: Evaluation of ARIMAX modeling.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 1776, No. 1, 194–200, DOI: 10.3141/1776-25.
Zhang, X. and Rice, J. A. (2003). “Short-term travel-time prediction.” Transportation Research Part C: Emerging Technologies, Vol. 11, No. 3, pp. 187–210, DOI: 10.1016/S0968-090X(03)00026-3.
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Oh, S., Byon, YJ., Jang, K. et al. Short-term travel-time prediction on highway: A review on model-based approach. KSCE J Civ Eng 22, 298–310 (2018). https://doi.org/10.1007/s12205-017-0535-8
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DOI: https://doi.org/10.1007/s12205-017-0535-8