A macro-model for traffic flow with consideration of driver’s reaction time and distance

Davoodi, Nooshin; Soheili, Ali Reza; Hashemi, S. Mehdi

doi:10.1007/s11071-015-2435-0

Cited by 31 publications

(12 citation statements)

References 32 publications

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“…where AðG iþ 1 2 Þ is the Jacobian matrix at the segment boundary, and G iþ 1 2 is the vector of data variables at the boundary obtained using Roe's technique. The flux approximates the change in traffic density and flow at the segment boundary.…”

Section: Roe Decompositionmentioning

confidence: 99%

“…the time to react and align (harmonize) to the forward traffic. The time required to react is known as the reaction time, and the time for traffic alignment (harmonization) is known as the transition time [1] . The reaction distance is the distance travelled during the reaction time, while the transition distance is the distance covered during the transition time.…”

Section: Introductionmentioning

confidence: 99%

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A macroscopic traffic model for traffic flow harmonization

Khan

Gulliver

2018

Eur. Transp. Res. Rev.

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Traffic flow will harmonize to forward conditions. The time and distance required for harmonization can have a significant effect on the traffic density behavior. The flow can evolve into clusters of vehicles or become uniform depending on parameters such as safe time headway and safe distance headway. In this paper, a new model is presented to provide a realistic characterization of traffic behavior during the harmonization period. Results are presented for a discontinuous density distribution on a circular road which shows that this model produces more realistic traffic behavior than other models in the literature.

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Section: Roe Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A macroscopic traffic model for traffic flow harmonization

Khan

Gulliver

2018

Eur. Transp. Res. Rev.

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“…The case that the headway, the velocity and the velocity difference are all evaluated at the delayed time is mainly discussed in the autonomous cruise control literatures [42,43]. For other traffic flow models with time delay, we refer to [44][45][46][47][48][49].…”

Section: Modelmentioning

confidence: 99%

A new multi-anticipative car-following model with consideration of the desired following distance

et al. 2016

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Noname manuscript No. (will be inserted by the editor)A new multi-anticipative car-following model with consideration of the desired following distanceReceived: date / Accepted: date Abstract We propose in this paper an extension of the multi-anticipative optimal velocity car-following model to consider explicitly the desired following distance. The model on the following vehicle's acceleration is formulated as a linear function of the optimal velocity and the desired distance, with reactiontime delay in elements. The linear stability condition of the model is derived. The results demonstrate that the stability of traffic flow is improved by introducing the desired following distance, increasing the time gap in the desired following distance or decreasing the reaction-time delay. The simulation results show that by taking into account the desired following distance as well as the optimal velocity, the multi-anticipative model allows longer reaction-time delay in achieving stable traffic flows.

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“…Based on the vehicle speed and acceleration, Kumagai established a driving model to predict whether the driver could stop in front of the red lamps with Dynamic Bayesian Networks [32]. In addition, fuzzy neural networks and fuzzy inference methods have been widely used in driver behavior analyses [33,34]. Among them, the support vector machine (SVM) has a good application effect.…”

Section: Introductionmentioning

confidence: 99%

A driver’s car-following behavior prediction model based on multi-sensors data

Wang

et al. 2020

J Wireless Com Network

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The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver's car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

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A macro-model for traffic flow with consideration of driver’s reaction time and distance

Cited by 31 publications

References 32 publications

A macroscopic traffic model for traffic flow harmonization

A macroscopic traffic model for traffic flow harmonization

A new multi-anticipative car-following model with consideration of the desired following distance

A driver’s car-following behavior prediction model based on multi-sensors data

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