Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32727
Stochastic Modeling for Parameters of Modified Car-Following Model in Area-Based Traffic Flow

Authors: N. C. Sarkar, A. Bhaskar, Z. Zheng


The driving behavior in area-based (i.e., non-lane based) traffic is induced by the presence of other individuals in the choice space from the driver’s visual perception area. The driving behavior of a subject vehicle is constrained by the potential leaders and leaders are frequently changed over time. This paper is to determine a stochastic model for a parameter of modified intelligent driver model (MIDM) in area-based traffic (as in developing countries). The parametric and non-parametric distributions are presented to fit the parameters of MIDM. The goodness of fit for each parameter is measured in two different ways such as graphically and statistically. The quantile-quantile (Q-Q) plot is used for a graphical representation of a theoretical distribution to model a parameter and the Kolmogorov-Smirnov (K-S) test is used for a statistical measure of fitness for a parameter with a theoretical distribution. The distributions are performed on a set of estimated parameters of MIDM. The parameters are estimated on the real vehicle trajectory data from India. The fitness of each parameter with a stochastic model is well represented. The results support the applicability of the proposed modeling for parameters of MIDM in area-based traffic flow simulation.

Keywords: Area-based traffic, car-following model, micro-simulation, stochastic modeling.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 706


[1] J. Barceló, Fundamentals of Traffic Simulation: Springer, 2010.
[2] T. Toledo, "Driving behaviour: models and challenges," Transport Reviews, vol. 27, pp. 65-84, 2007.
[3] V. T. Arasan and R. Z. Koshy, "Methodology for modeling highly heterogeneous traffic flow," Journal of Transportation Engineering, vol. 131, pp. 544-551, 2005.
[4] T. V. Mathew, C. R. Munigety, and A. Bajpai, "Strip-Based Approach for the Simulation of Mixed Traffic Conditions," Journal of Computing in Civil Engineering, vol. 29, p. 04014069, 2013.
[5] C. F. Choudhury and M. M. Islam, "Modelling acceleration decisions in traffic streams with weak lane discipline: A latent leader approach," Transportation Research Part C: Emerging Technologies, vol. 67, pp. 214-226, 2016.
[6] N. C. Sarkar, A. Bhaskar, and Z. Zheng, "Microscopic Modelling for Area-Based Heterogeneous Traffic," TRB Annual Meeting, 2018.
[7] M. Treiber, A. Hennecke, and D. Helbing, "Congested traffic states in empirical observations and microscopic simulations," Physical Review E, vol. 62, p. 1805, 2000.
[8] V. Kanagaraj, G. Asaithambi, T. Toledo, and T.-C. Lee, "Trajectory Data and Flow Characteristics of Mixed Traffic," Transportation Research Record: Journal of the Transportation Research Board, pp. 1-11, 2015.
[9] J. M. Chambers, Graphical Methods for Data Analysis: 0: Chapman and Hall/CRC, 2017.
[10] F. J. Massey Jr, "The Kolmogorov-Smirnov test for goodness of fit," Journal of the American statistical Association, vol. 46, pp. 68-78, 1951.
[11] C. D. Ghilani, Adjustment Computations: Spatial Data Analysis. Newark, UNITED STATES: John Wiley & Sons, Incorporated, 2017.