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.

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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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    ABSTRACT: The main component of a lock case is a helical compression spring. The spring is used to move the latch bolt. It is ex-pected to operate over very long periods of time without significant changes in dimension and displacement. The most common failure modes for springs are fracture due to fatigue and excessive loss of load due to stress relaxation. If fracture is occurred, latch bolt cannot be pulled from its door jamb, and the door cannot be opened. Springs tend to be highly stressed because they are designed to fit into small spaces with the least possible weight and lowest material cost. At the same time they are required to deliver the required force over a long period of time. The reliability of a spring is therefore related to its material strength, design characteristics, and the operating environment. The optimal design of helical springs has become an interest in optimization and engineering design. The aim of this paper is to develop an optimization model for helical compression spring of lock case using maximum reliability as criteria. To design a helical spring, fatigue, shear stress, surging, buckling, diameter limitations and operating environment should be considered as constraints. The design variables for the spring considered in this paper are wire diameter (d w), coil diameter (D) and number of active coils (Na). These three variables completely define the geometry of the spring. After suitable material is selected and the design variables are obtained, all the other spring characteristics such as spring rate, free length, pitch, and solid length can be determined.
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    • "It provide the best route for the driver based on the optimum route selection criteria. The idea of " optimum " has been published in some literatures gradually such as Hybrid probabilistic-possibilistic framework by Peeta and Yu in 2004 [5], LOGIT model by Henn in 2000 [6], and Fuzzy model by Pang et al in 1999 [7]. Compare to the existing methods, researchers used Fuzzy logic on the route choice problem because of its advantages as a suitable tool for modeling driver's route choice behavior which is mostly interpret as vague attributes. "
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    ABSTRACT: Transportation is a key component of economic growth, however the increasing population nowadays cause traffic congestion becomes a challenging issue. Intelligent Transportation Systems (ITS) with its technology progressivity had been a powerful solution to increase transportation efficiency. One promising option among ITS is Advanced Traveler In-formation Systems (ATIS) which enable driver in having less frustrated driving experience by providing valuable real time infor-mation. Even though this systems capable in guiding driver to reach destination, the recommendation will not always be the optim-al one because different drivers have different personal preferences. Moreover, the route selection system can be based on the driver attributes, route characteristics, and situational factors with different degree of importance which make the systems be more difficult to generate a right recommendation for the driver. Due to these facts, self-learning ability in the systems becomes neces-sary. Navigation systems requires to be able to personalize to individual driver by adapt to driver’s behavior. The ability of the system to responsively self-adjust upon change is the objective here. Therefore, Adaptive Neural Fuzzy Inference Systems (ANFIS) is used to model the vague attributes which is come from the driver’s feedback and then learn it by itself. It has both ad-vantages of Fuzzy Logic which has a role as an excellent tool for modeling human thought and Neural Network for the learning capability. Finally the systems will gradually improve the model in order to give a better route suggestion corresponding to driver’s inclination.
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