Impact of next-nearest leading vehicles on followers' driving behaviours and traffic stability in mixed traffic

Nagahama, Akihito; Yanagisawa, Daichi; Nishinari, Katsuhiro

doi:10.1016/j.jtte.2019.01.007

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Compared to traditional cars, connected and autonomous vehicles enabled in vehicle-to-vehicle and vehicle-toinfrastructure communication technology are promising in improving traffic efficiency and safety [3]. However, one can foresee that within a certain period of time, regular vehicles and autonomous vehicles coexist on the road [4]. is means a certain level of cooperation is needed between human drivers and autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Discretionary Lane-Changing Decision-Making Mechanism Incorporating Drivers’ Heterogeneity: A Signalling Game-Based Approach

Shao

Zhang

Feng

et al. 2020

Journal of Advanced Transportation

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This paper attempts to propose a discretionary lane-changing decision-making model based on signalling game in the context of mixed traffic flow of autonomous and regular vehicles. The effects of the heterogeneity among different drivers and the endogeneity of same drivers in lane-changing behaviours, e.g., aggressive or conservative, are incorporated through the specification of different payoff functions under different scenarios. The model is calibrated and validated using the NGSIM dataset with a bilevel calibration framework, including two kinds of methods, genetic algorithm and perfect Bayesian equilibrium. Comparative results based on simulation show that the signalling game-based model outperforms the traditional space-based lane-changing model in the sense that the proposed model yields relatively stable reciprocal of time to collision and higher success rate of lane-changing under different traffic densities. Finally, a sensitivity analysis is performed to test the robustness of the proposed model, which indicates that the signalling game-based model is stable to the varying ratios of driver type.

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Section: Introductionmentioning

confidence: 99%

A Discretionary Lane-Changing Decision-Making Mechanism Incorporating Drivers’ Heterogeneity: A Signalling Game-Based Approach

Shao

Zhang

Feng

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…In addition to driving-style differences, studies also showed that car-following behaviors are affected by leader-follower compositions (22,23). Statistical analyses showed that maximum velocity, acceleration, deceleration, and operational delay of the followers are significantly affected by the presence of immediate leaders (24). Some studies evidenced that followers would have smaller speed variations when following heavy vehicles (22,23,25,26).…”

mentioning

confidence: 99%

Modeling Car-Following Heterogeneities by Considering Leader–Follower Compositions and Driving Style Differences

Sun

Yao

Qin

et al. 2021

Transportation Research Record: Journal of the Transportation R

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To better understand the behavioral heterogeneities of human-operated vehicles, the paper proposes a method to distinguish car-following behaviors in specific leader–follower contexts. Using the Next-Generation Simulation dataset, the car-following data are first classified into four leader–follower compositions, namely, truck–car, car–car, car–truck, and truck–truck. Based on the classified data, we calibrate the parameters of a few well-known car-following models, including Full Velocity Difference model, Intelligent Driver Model, and Gazis–Herman–Rothery model. Principal component analysis and clustering analysis are then applied to the calibrated parameters to discover the behavioral patterns and to find the probabilistic distributions of the parameters for the classified car-following (CCF) models. Simulation results show that compared with the unified car-following models, the estimation errors of calibrated CCF models are reduced by 20.79% to 49.05%, which indicates that the proposed method provides a more accurate description of car-following heterogeneities. The proposed framework could help highway traffic operators better know the traffic users.

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Car-following characteristics of various vehicle types in respective driving phases

Nagahama

Yanagisawa

Nishinari

2020

Transportmetrica B: Transport Dynamics

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Impact of next-nearest leading vehicles on followers' driving behaviours and traffic stability in mixed traffic

Cited by 5 publications

References 26 publications

A Discretionary Lane-Changing Decision-Making Mechanism Incorporating Drivers’ Heterogeneity: A Signalling Game-Based Approach

A Discretionary Lane-Changing Decision-Making Mechanism Incorporating Drivers’ Heterogeneity: A Signalling Game-Based Approach

Modeling Car-Following Heterogeneities by Considering Leader–Follower Compositions and Driving Style Differences

Car-following characteristics of various vehicle types in respective driving phases

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