Adaptability of a Hybrid Route Choice Model to Incorporating Driver Behavior Dynamics Under Information Provision

Sch. of Civil Eng., Purdue Univ., West Lafayette, IN, USA
IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans (Impact Factor: 2.18). 04/2004; 34(2):243 - 256. DOI: 10.1109/TSMCA.2003.822272
Source: IEEE Xplore


This paper proposes a seamless framework to incorporate the day-to-day and within-day dynamics of driver route choice decisions under real-time information provision by adapting a hybrid probabilistic-possibilistic model previously developed by the authors. The day-to-day dynamics are captured through the update of driver perception and route choice rules based on the current day's experience. The within-day dynamics are captured through the en-route adjustment of the weights of the driver route choice rules in response to situational factors. Experiments are conducted to analyze the model's ability to capture driver behavior dynamics, and the associated prediction accuracy. The results suggest that the framework can reflect the evolution of driver route choice behavior over time, and adapt to the within-day variability in ambient driving conditions. This is illustrated by its ability to capture phenomena such as inertia, compliance, delusion, freezing, and perception update under information provision, in addition to the effects of familiarity and route complexity. In the within-day context, the results highlight the sensitivity to the situational factors unfolding in real-time. The results also illustrate the better prediction power of the hybrid model compared to that of a traditional multinomial probit model; however, this gap reduces with increasing heterogeneity in driver behavioral class fractions. Elsewhere, the authors show that the proposed framework can be used to predict the ambient driver class fractions, thereby addressing a key deployment limitation of existing dynamic network models for real-time traffic control through route guidance.


Available from: Srinivas Peeta, Jan 05, 2015
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    • "Hence, the incorrect modeling of driver behavior can negatively impact the prediction of the traffic network states and the effectiveness of information-based control strategies. Peeta and Yu (2004, 2006) highlight realism issues arising from the rigid representation of driver behavior under information provision, and the consequent barriers to developing effective operational paradigms for information-based traffic network management. "

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