Dynamic Traffic Flow–Dispersion Model Based on TTI of Floating Cars
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 1
Abstract
This paper presents a probability distribution model of vehicles’ travel time index (TTI) in urban road segments that depicts traffic flow dispersion. Compared with traditional models, it can depict traffic flow dispersion with a uniform model under conditions of different road speeds or road lengths. Based on it, a dynamic model that depicts how the TTI distribution parameters vary with speed of road segment is proposed. Furthermore, parameters of the dynamic model are used to construct a characteristic vector to reveal a new inherent feature of road segments. The tests of goodness of fit for the two proposed models were carried out with data collected from 66,000 floating cars. The test area covered 40,400 urban road segments in the urban area of Beijing. Test results showed that more than 98% of the tested bins accepted the proposed TTI probability distribution model, and more than 90% of the tested road segments accepted the proposed dynamic model. The proposed models would be applied in traffic simulation, congestion forecast, signal control, and other relevant fields in the future.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This paper is sponsored by National Science Foundation of China (No. 61572069), Research Foundation for Outstanding Scholars of Transport Ministry of China (No. 201540), Beijing Municipality Key Laboratory of Urban Traffic Operation Simulation and Decision Support (No. BZ0012).
References
Adams, W. (1950). “Road traffic considered as a random series.” Oper. Res. Q., 1(1), 9–9.
Al Mutaz, M., Malott, L., and Chellappan, S. (2013). “Leveraging platoon dispersion for sybil detection in vehicular networks.” Proc., Privacy, Security and Trust (PST), 2013 11th Annual Int. Conf., IEEE, New York, 340–347.
Bentaleb, K., Jetto, K., Ez-Zahraouy, H., and Benyoussef, A. (2013). “A cellular automata traffic flow modeling of desired speed variability.” Chin. Phys. B, 22(1), 018902.
De Maio, A., Farina, A., and Foglia, G. (2010). “Knowledge-aided Bayesian radar detectors and their application to live data.” IEEE Trans. on Aerosp. Electron. Syst., 46(1), 170–183.
Gao, S., Wang, Y., Gao, Y., and Liu, Y. (2013). “Understanding urban traffic-flow characteristics: A rethinking of betweenness centrality.” Environ. Plann. B: Plann. Des., 40(1), 135–153.
Ge, H.-x., Meng, X.-p., Ma, J., and Lo, S.-m. (2012). “An improved car-following model considering influence of other factors on traffic jam.” Phys. Lett. A, 377(1), 9–12.
Greenshields, B., Channing, W., and Miller, H. (1935). “A study of traffic capacity.” Proc., Highway Research Board, National Research Council, National Research Council, Washington, DC.
Helbing, D. (1997). “Fundamentals of traffic flow.” Phys. Rev. E, 55(3), 3735–3738.
Holland, E. N., and Woods, A. W. (1997). “A continuum model for the dispersion of traffic on two-lane roads.” Transp. Res. Part B: Methodol., 31(6), 473–485.
Hong, W.-C. (2011). “Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm.” Neurocomputing, 74(12), 2096–2107.
Israeli, O. (2007). “A Shapley-based decomposition of the R-square of a linear regression.” J. Econ. Inequality, 5(2), 199–212.
Jun, J. (2010). “Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic.” Transp. Res. Part C: Emerging Technol., 18(4), 599–610.
Kim, J., and Mahmassani, H. S. (2014). “A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times.” Transp. Res. Part C: Emerging Technol., 46, 83–97.
Kinzer, J. P. (1933). “Application of the theory of probability to problems of highway traffic.” B.C.E. thesis, Polytechnic Institute of Brooklyn, Brooklyn, NY.
Laval, J. A., and Leclercq, L. (2013). “The Hamilton–Jacobi partial differential equation and the three representations of traffic flow.” Transp. Res. Part B: Methodol., 52, 17–30.
Lei, F., Wang, Y., Lu, G., and Sun, J. (2014). “A travel time reliability model of urban expressways with varying levels of service.” Transp. Res. Part C: Emerging Technol., 48, 453–467.
Lighthill, M. J., and Whitham, G. B. (1955). “On kinematic waves. II: A theory of traffic flow on long crowded roads.” Proc. R. Soc. London A: Math. Phys. Eng. Sci., 229(1178), 317–345.
Lomax, T., Schrank, D., and Turner, S. (2011). “Annual urban mobility report.” Texas Transportation Institute, College Station, TX.
Maher, M. (2011). “A comparison of the use of the cell transmission and platoon dispersion models in TRANSYT 13.” Transp. Plann. Technol., 34(1), 71–85.
Massey, J. F. J. (1951). “The Kolmogorov-Smirnov test for goodness of fit.” J. Am. Stat. Assoc., 46(253), 68–78.
Park, B., and Schneeberger, J. (2003). “Microscopic simulation model calibration and validation: Case study of VISSIM simulation model for a coordinated actuated Signal system.” Transp. Res. Rec., 1856, 185–192.
Qiao, F., Yang, H., and Lam, W. H. (2001). “Intelligent simulation and prediction of traffic flow dispersion.” Transp. Res. Part B: Methodol., 35(9), 843–863.
Richards, P. I. (1956). “Shock waves on the highway.” Oper. Res., 4(1), 42–51.
Šelmić, M., Teodorović, D., and Vukadinović, K. (2010). “Locating inspection facilities in traffic networks: An artificial intelligence approach.” Transp. plann. Technol., 33(6), 481–493.
Semertzidis, T., Dimitropoulos, K., Koutsia, A., and Grammalidis, N. (2010). “Video sensor network for real-time traffic monitoring and surveillance.” IET Intell. Transp. Syst., 4(2), 103–112.
Shoufeng, L., Ximin, L., and Shiqiang, D. (2008). “Revised MAXBAND model for bandwidth optimization of traffic flow dispersion.” Proc., ISECS Int. Colloquium on Computing, Communication, Control, and Management, IEEE, New York, 85–89.
Song, L. (2012). “Improved intelligent method for traffic flow prediction based on artificial neural networks and ant colony optimization.” J. Convergence Inform. Technol., 7(8), 272–280.
Tang, T., Huang, H., and Shang, H. (2010). “A new macro model for traffic flow with the consideration of the driver’s forecast effect.” Phys. Lette. A, 374(15), 1668–1672.
Vandaele, N., Van Woensel, T., and Verbruggen, A. (2000). “A queueing based traffic flow model.” Transp. Res. Part D: Transp. Environ., 5(2), 121–135.
Wang, D., Zhang, Y., and Zhitao, W. (2003). “Study of platoon dispersion models.” Proc., Transportation Research Board Annual Meeting, National Academies of Sciences, Engineering, and Medicine, Washington, DC, 3563–3571.
Zwahlen, H., Oner, E., and Suravaram, K. (2007). “Approximated headway distributions of free-flowing traffic on Ohio freeways for work zone traffic simulations.” Transp. Res. Rec., 1999, 131–140.
Information & Authors
Information
Published In
Copyright
©2016 American Society of Civil Engineers.
History
Received: Feb 23, 2016
Accepted: Jul 7, 2016
Published online: Nov 4, 2016
Published in print: Jan 1, 2017
Discussion open until: Apr 4, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.