Psychological factors that influence car-following and car-following model development

Ranney, Thomas A.

doi:10.1016/s1369-8478(00)00010-3

Cited by 86 publications

(55 citation statements)

References 6 publications

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“…A number of factors have been found to influence car-following behavior. These factors can be classified into two categories [1]. The first category is individual differences consisting of age, gender, risk-taking behavior, driving skill, vehicle size, and vehicle performance characteristics.…”

Section: Brief Review Of Car-following Modelsmentioning

confidence: 99%

Comparative Evaluation of Microscopic Car-Following Behavior

Panwai

Dia

2005

IEEE Trans. Intell. Transport. Syst.

253

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Abstract-Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Stadten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative "drift and goal-seeking behavior" test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.

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Section: Brief Review Of Car-following Modelsmentioning

confidence: 99%

Comparative Evaluation of Microscopic Car-Following Behavior

Panwai

Dia

2005

IEEE Trans. Intell. Transport. Syst.

253

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“…The following process of vehicles is a continuous field energy changing process. The repellent field energy increases when longitudinal distance decreases (closer to the safe-distance), and the followed vehicle may take action to decelerate or overtake [13][14][15]. On the contrary, when longitudinal distance is apparently farther than the safe-distance, the follower will accelerate due to the attractive potential energy affection.…”

Section: A Brief Review Of Classic Carmentioning

confidence: 99%

APF-Based Car Following Behavior Considering Lateral Distance

Yang

Yao

et al. 2013

Advances in Mechanical Engineering

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Considering the influence of lateral distance on consecutive vehicles, this paper proposes a new car following model based on the artificial potential field theory (APF). Traditional car following behaviors all assume that the vehicles are driving along the middle of a lane. Different from the traditional car following principles, this incorporation of APF offers a potential breakthrough in the fields of car following theory. The individual vehicle can be represented as a unit point charge in electric field, and the interaction of the attractive potential energy and the repellent potential energy between vehicles simplifies the various influence factors on the target vehicle in actual following behavior. Consequently, it can make a better analysis of the following behavior under the lateral separation. Then, the proposed model has been demonstrated in simulation environment, through which the space-time trajectories and the potential energy change regulation are obtained. Simulations verify that the following vehicle's behavior is vulnerable to be affected by lateral distance, where the attractive potential energy tends to become repellent potential energy as the longitudinal distance decreases. The search results prove that the proposed model quantifies the relations between headway and potential energy and better reflects the following process in real-world situation.

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“…Although it became a much-cited article, there was considerable scepticism as well. The review generated four commentaries (Boer, 1999;Hancock, 1999;Ranney, 1999;Van Winsum, 1999). Ranney (1999) criticized that Brackstone and McDonald made very sophisticated assumptions that were not well-motivated from a human perspective.…”

Section: Adaptive Control Modelsmentioning

confidence: 99%

“…The review generated four commentaries (Boer, 1999;Hancock, 1999;Ranney, 1999;Van Winsum, 1999). Ranney (1999) criticized that Brackstone and McDonald made very sophisticated assumptions that were not well-motivated from a human perspective. Many factors that are known to influence car-following, such as weather, road conditions, age, gender, motivations for driving, strategic aspects, and the notion that drivers may be satisfied with a range of conditions and do not behave optimally, were not covered.…”

Section: Adaptive Control Modelsmentioning

confidence: 99%