Developing a Model of Heterogeneity in Driver’s Behavior

Document Type: Research Paper

Authors

1 PhD Candidate, Systems and Control Group, Department of Electrical Engineering Department, Khaje Nassir Toosi University of Technology, Tehran, Iran

2 Assistant Professor, Systems and Control Group, Department of Electrical Engineering, Khaje Nassir Toosi University of Technology, Tehran, Iran

Abstract

Intelligent Driver Model (IDM) is a well-known microscopic model of traffic flow within the traffic engineering societies. While it is a powerful technique for modeling traffic flows, the Intelligent Driver Model lacks the potential of accommodating the notion of drivers’ heterogeneous behavior whenever they are on roads. Concerning the above mentioned, this paper takes the lane to recognize the heterogeneity in drivers’ behavior based on Heterogeneity Vector. Heterogeneity vector is an integral part of a new model that holds the potential to provide a method that in turn can accommodate the effect of the above mentioned differentiation in the traffic pattern. The Intelligent Driver Model in combination to Heterogeneous vector results to Intelligent Driver Model Heterogeneous Calibration (IDMHC) which in turn has the capability to improve the accuracy of IDM calibration, and as a result, enhances its performance under real conditions of traffic systems. Following the pre-stated, the study formulates that, the heterogeneity vector, as an output of the computation block, will apply in the simulation of the traffic of vehicles. To validate the performance of the IDMHC model, NGSIM project has been applied. As such, the most notable contributions of this study are, presenting a new method for calibration of microscopic flow model based on individual trajectory data, depicting the differences among drivers based on the newly defined heterogeneity measure, and illustrating the differences among drivers shape, traffic patterns that are causing different distributions of macroscopic variables such as travel time. Based on the study, the results obtained depicts that the difference between the presumed values regarding the IDM parameters has a great difference when compared with the calculated values for each vehicle based on a 50% variance.   These results have the likelihood to significantly affect the mode in which microscopic models simulate and predict the traffic situation.   

Keywords


-Abdi, Ali, Saffarzadeh, Mahmoud  and Salehikalam, Arsalan (2016) "Identifying and analyzing stop and go traffic based on asymmetric theory of driving behavior in acceleration and deceleration." International Journal of Transportation Engineereing, Vol. 3, No. 4, pp. 237-251.

-Ahmed, Kazi Iftekhar (1999) "Modeling drivers' acceleration and lane changing behavior." PhD diss., Massachusetts Institute of Technology.

-Alexiadis, Vassili, James Colyar, John Halkias, Rob Hranac, and Gene McHale (2004) "The next generation simulation program." Institute of Transportation Engineers. ITE Journal Vol. 74, no. 8, pp. 22.

-Antoniou, Andreas (2006) ”Digital signal processing”. Toronto, Canada: McGraw-Hill.

-Chen, Chao, Alexander Skabardonis, and Pravin Varaiya (2003) "Travel-time reliability as a measure of service." Transportation Research Record: Journal of the Transportation Research Board, Vol. 1855, pp. 74-79.

-Dang, Shuping, Zeqi Hong, Sen Yang, and Liam Baker (2013) "Intelligent urban traffic management system based on the energy-circle cards platform." In Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on, vol. 1, pp. 457-459.

-Guo, Xiaolei, and Henry X. Liu (2011) "Bounded rationality and irreversible network change." Transportation Research Part B: Methodological Vol.45, No.10, pp. 1606-1618.

-Hakamies-Blomqvist, Liisa, Sirén, Anu, and Davidse, Ragnhild (2004). “Older drivers: a review”. Väg-och transportforskningsinstitutet.

-Hoogendoorn, Serge, and Hoogendoorn, Raymond (2010) "Generic calibration framework for joint estimation of car-following models by using microscopic data." Transportation Research Record: Journal of the Transportation Research Board Vol. 2188, pp. 37-45.

-Kesting, Arne, Treiber, Martin Schönhof, Maring and Helbing, Dirk (2007) "Extending adaptive cruise control to adaptive driving strategies." Transportation Research Record: Journal of the Transportation Research Board, No. 2000, pp 16-24.

-Krajzewicz, Daniel, Reinhart Kühne, and Peter Wagner (2004) "A Car Driver´ s Cognition Model." Proceedings of Intelligent Transportation Systems Safety and Security Conference. Vol. 400.

-Leduc, Guillaume (2008) "Road traffic data: Collection methods and applications." Working Papers on Energy, Transport and Climate Change 1. No. 55.

-Liu, YanFei, and ZhaoHui,  Wu (2007) "Improvement of ACT-R for modeling of parallel and multiprocessing driver behavior." International Journal of Intelligent Control and Systems Vol.12, No.1, pp.72-81.

-Lo, Hong K., Elbert Chang and Yiu, Cho Chan (2001) "Dynamic network traffic control." Transportation Research Part A: Policy and Practice Vol. 35, No. 8, pp. 721-744.

-Manolis, Diamantis (2016) "Automated tuning of ITS management and control systems: Results from real-life experiments." Transportation Research Part C: Emerging Technologies Vol. 66, pp. 119-135.

-Poor Arab Moghadam, M., Pahlavani, P. and Naseralavi, S. (2016) “Prediction of car following behavior based on the instantaneous reaction time using an ANFIS-CART based model”,  International Journal of Transportation Engineering, Vol. 4, No. 2, pp.109-126.

-Salvucci, Dario D. (2006) "Modeling driver behavior in a cognitive architecture", Human Factors: The Journal of the Human Factors and Ergonomics Society Vol. 48, No. 2, pp. 362-380.

-Tang, Tieqiao, Weifung, Shi; Huayan, Shang, Yunpeng, Shang … (2014) "A new car-following model with consideration of inter-vehicle communication." Nonlinear Dynamics,  Vol. 76, No. 4, pp. 2017-2023.

-Toledo, Tomer, Haris N. Koutsopoulos and Ben-Akiva, Moshe (2009) "Estimation of an integrated driving behavior model." Transportation Research Part C: Emerging Technologies Vol. 17, No. 4, pp. 365-380.

-Treiber, Martin and Helbing, Dirk (1999) "Macroscopic simulation of widely scattered synchronized traffic states." Journal of Physics A: Mathematical and General Vol. 32, number 1, L17.

-Treiber, M.; Hennecke, A. and Helbing, D. (2000) "Congested traffic states in empirical observations and microscopic simulations",  Physical Review E, Vol. 62, No.2, pp. 1805.

-Web page http://www.fhwa.dot.gov/publications/publicroads/07jan/01.cfm visited at 2016/12/01

-Zhao, Dongbin; Yujie Dai, and Zhang, Zhen (2012) "Computational intelligence in urban traffic signal control: A survey." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) Vol.42, No. 4, pp. 485-494.

-Zhou, Tong; Zhou, Tang; Sun, Dihna; Kang, Yrong; Li, Haumin and Tian, Chuan (2014) "A new car-following model with consideration of the prevision driving behavior", Communications in Nonlinear Science and Numerical Simulation Vol. 19, No. 10, pp. 3820-3826.