1 Civil Engineering & Built Environment School, Science and Engineering Faculty, ... Car following (CF) and lane changing (LC) are two primary driving tasks ...
Modelling, calibrating, and validating car following and lane changing behavior Zuduo Zheng1, Majid Sarvi2 1
Civil Engineering & Built Environment School, Science and Engineering Faculty, Queensland University of Technology (QUT), George St GPO Box 2434 Brisbane Qld 4001 Australia 2 Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010 Australia Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behaviour under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety. This realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modelling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few. The Special Issue focuses on latest developments in modelling, calibrating, and validating two primary vehicular interactions observed in traffic flow: CF and LC. Eight papers (five are related to CF, and three are related to LC) included in this Special Issue cover a wide range of issues and challenges traffic flow modellers are facing, as elaborated below. (1) CF related papers Durrani et al. (2016) investigate vehicle class’ impact on CF behavior in the Wiedemann’s model using vehicular trajectories collected from US-101 in Los Angeles, California. Three vehicle classes were considered: cars, heavy vehicles, and motorcycles, and they found significant impact of vehicle class on estimated CF parameters. Furthermore, parameter variation is also present for the same vehicle class, which highlights the importance of considering parameter variability when calibrating CF models. Hamdar et al. (2016) investigate impact of road geometry and weather on driver behavior by extending a Prospect Theory based acceleration modeling framework. Data from 76 driving simulator experiments were used, with different weather conditions (foggy, icy, and wet) and different geometric features (horizontal and vertical curves, and different lane and shoulder widths). They concluded that participants’ risk-perception and acceleration maneuver in different weather conditions and road geometries can be reasonably captured by the extended Prospect Theory based model parameters, and that compared with weather conditions, road geometries draw more attention and greater effort from participants. Hao et al. (2016) develop an artificial intelligence CF model to mimic human drivers. Based on the stimulus–response framework, their model consists of a five-layer structure (i.e., Perception–Anticipation–Inference–Strategy–Action) and a fuzzy logic-based inference mechanism, and has been calibrated using NGSIM data. Rhoades (2016) propose a new method to calibrate nonlinear car-following models by simultaneously considering the driver’s car-following behavior, the vehicle trajectory’s time-
domain features, and frequency-domain properties. They demonstrated the good performance of this method using Newell’s car-following model (1961) and NGSIM data. Unlike the papers above that are primarily for lane-based traffic, Choudhury et al. (2016) focus on modelling mixed traffic flow, that is, traffic with weak lane discipline. A latent leader acceleration model has been developed with two components: a random utility based dynamic class membership model (latent leader component) and a class-specific acceleration model (acceleration component), and calibrated using trajectory data collected from Dhaka, Bangladesh. A good performance of this model was reported. (2) LC related papers Instead of applying one single lane-changing strategy to all drivers as most of the existing LC models do, Keyvan-Ekbatani et al. (2016) propose four distinct lane-changing strategies based on data collected from a two-stage test drive. In addition, their interviews with participants revealed that a driver often perceives his/her lane-changing strategy as the obvious one and thus believes that it is also the one adopted by other drivers. Not using turn signals when changing lanes is a major challenge to advanced driver assistance systems that rely on turn signals to detect lane-changing intention. To address this issue, Li et al. (2016) propose a lane-changing intention recognition algorithm by combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques. The proposed algorithm was trained and validated by using a naturalistic data set, and a high recognition accuracy and an early stage recognition were obtained. Balal et al. (2016) design a fuzzy inference system to model a driver’s decision on whether or not executing a discretionary lane change on freeways, with four input variables that were selected based on a driver survey. The system was trained by using NGSIM data. Its performance was compared with the TransModeler’s gap acceptance model, and a higher estimation accuracy of this fuzzy inference system was reported. In summary, this Special Issue provides a collection of state-of-the-art research activities devoted to tackling challenges related to various aspects of CF or LC modelling. We believe that by reading this Special Issue, researchers who are interested in this topic can quickly grasp the most recent developments of the field, and identify issues worthy of further investigation.
The bibliography details of the published papers (they are ordered according to this link: http://www.sciencedirect.com/science/journal/0968090X/vsi/103P7KVGNBC): Durrani, U., Lee, C., & Maoh, H. (2016). Calibrating the Wiedemann’s vehicle-following model using mixed vehicle-pair interactions. Transportation Research Part C: Emerging Technologies, 67, 227-242. Hamdar, S. H., Qin, L., & Talebpour, A. (2016). Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework. Transportation Research Part C: Emerging Technologies, 67, 193-213.
Keyvan-Ekbatani, M., Knoop, V. L., & Daamen, W. (2016). Categorization of the lane change decision process on freeways. Transportation Research Part C: Emerging Technologies, 69, 515-526. Hao, H., Ma, W., & Xu, H. (2016). A fuzzy logic-based multi-agent car-following model. Transportation Research Part C: Emerging Technologies, 69, 477-496. Li, K., Wang, X., Xu, Y., & Wang, J. (2016). Lane changing intention recognition based on speech recognition models. Transportation Research Part C: Emerging Technologies, 69, 497-514. Balal, E., Cheu, R. L., & Sarkodie-Gyan, T. (2016). A binary decision model for discretionary lane changing move based on fuzzy inference system. Transportation Research Part C: Emerging Technologies, 67, 47-61. Choudhury, C. F., & Islam, M. M. (2016). Modelling acceleration decisions in traffic streams with weak lane discipline: A latent leader approach. Transportation Research Part C: Emerging Technologies, 67, 214-226. Rhoades, C., Wang, X., & Ouyang, Y. (2016). Calibration of nonlinear car-following laws for traffic oscillation prediction. Transportation research part C: emerging technologies, 69, 328-342.
