Analysis of a Two-Regime Stochastic Car-Following Model: Explaining Capacity Drop and Oscillation Instabilities

Xu, Tu; Laval, Jorge A.

doi:10.1177/0361198119850464

Cited by 23 publications

(15 citation statements)

References 34 publications

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“…In the authors' previous work (10), it was found that if we select m ' 1:2, then the model could replicate both the oscillation and capacity drop. However, the present analysis with the NGSIM data gives a strong indication that the value of m is larger than 3, which means that the acceleration processes of drivers are closer to a Brownian motion than to a geometric Brownian motion.…”

Section: Discussionmentioning

confidence: 93%

“…The model used in this paper is the two-regime stochastic car-following model of Xu and Laval (9,10). It corresponds to an extension of Newell's simplified 1 Georgia Tech, Atlanta, GA car-following theory (11), where vehicle location is given by the minimum of a free-flow term expressing the location (Y ) that the vehicle can achieve when unobstructed by traffic, and a congestion term giving the most downstream location (Z) that the vehicle can safely achieve without colliding with its leader.…”

Section: Introductionmentioning

confidence: 99%

“…which is all we need to evaluate Equation 3 and use maximum likelihood estimation (MLE) to estimate the parameters. For details of the model and parameter estimation, the reader is referred to Xu and Laval (9,10).…”

Section: Introductionmentioning

confidence: 99%

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Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

Laval

2020

Transportation Research Record: Journal of the Transportation R

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This paper analyzes the impact of uphill grades on the acceleration drivers choose to impose on their vehicles. Statistical inference is made based on the maximum likelihood estimation of a two-regime stochastic car-following model using Next Generation SIMulation (NGSIM) data. Previous models assume that the loss in acceleration on uphill grades is given by the effects of gravity. We find evidence that this is not the case for car drivers, who tend to overcome half of the gravitational effects by using more engine power. Truck drivers only compensate for 5% of the loss, possibly because of limited engine power. This indicates not only that current models are severely overestimating the operational impacts that uphill grades have on regular vehicles, but also underestimating their environmental impacts. We also find that car-following model parameters are significantly different among shoulder, median and middle lanes but more data is needed to understand clearly why this happens.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

Laval

2020

Transportation Research Record: Journal of the Transportation R

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“…This is probably the simplest CF model that captures driver random errors while accelerating and produces realistic traffic oscillations. Follow-up models that incorporate human error have also been formulated within this family (142,143) and also for other well-known CF models (144).…”

Section: Randomnessmentioning

confidence: 99%

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Zhou

Laval

Zhou

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

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“…Laval et al (2014) introduced white noise into the Newell model, which then enables the model to reproduce the formation of oscillations. Later, Yuan et al (2018) and Xu and Laval (2019) further improve the model by Laval et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Car following behavioral stochasticity analysis and modeling: Perspective from wave travel time

Tian

Zhu

Chen

et al. 2021

Transportation Research Part B: Methodological

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Analysis of a Two-Regime Stochastic Car-Following Model: Explaining Capacity Drop and Oscillation Instabilities

Abstract: The Standing Committee on Traffic Flow Theory and Characteristics (AHB45) peer-reviewed this paper (19-05914).

Cited by 23 publications

References 34 publications

Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Car following behavioral stochasticity analysis and modeling: Perspective from wave travel time

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