Cooperative decision-making for mixed traffic: A ramp merging example

Sun, Zhanbo; Huang, Tianyu; Peitong, Zhang

doi:10.1016/j.trc.2020.102764

Cited by 75 publications

(29 citation statements)

References 27 publications

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“…Second, the authors are particularly interested in using the proposed CCF framework for better prediction of the behaviors of humanoperated vehicles; for example, see Zhao et al (53) for short-term trajectory prediction. This will serve as key inputs to the problem of cooperative decision-making in mixed traffic; for example, see Sun et al (2). Last but not least, although the paper does not specifically focus on real-time applications, the authors do believe that with the help of internet of vehicles (connected vehicles), long-term (historical data by driver, and by time of day) and short-term (e.g., data collected during the last 30 s) driving data including location, speed, acceleration, spacing, and steering angle can be collected to feed real-time applications.…”

Section: Discussionmentioning

confidence: 99%

“…According to Litman's prediction, by 2040, 50% of traffic will be CAVs (1). The emergence of CAVs has led to the problem of mixed traffic, that is, traffic comprising conventional human-operated vehicles (HVs) and CAVs (2). In mixed traffic, the decisionmaking and/or control of CAVs largely depends on accurate description and prediction of HVs' behaviors (3)(4)(5)(6).…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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

Self Cite

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“…Xu et al (2021) formulate the merging sequence choice as an optimization problem combining the mainline travel time and the merging throughput and solve the problem with generic algorithms. Alternatively, Jing et al (2019), Chen et al (2020) and Sun et al (2020) integrate the gap choice and path planning of merging vehicles into a single optimization model. The model compares the path costs to lead a ramp vehicle into different gaps, so as to find the optimal gap and the corresponding path with the lowest cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

Zhu

Tasic

2022

Multimodal Transportation

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“…Typically, trajectory optimization strategies are categorized into two aspects: trajectory optimization strategies for urban intersections [9][10][11][12][13][14], and trajectory optimization strategies for freeway [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The existence of freeway ramp metering always results in speed changes, traffic congestion, and collision [15][16][17][18]30,31]. In general, the methods proposed for merging behaviors on the freeway ramp have similar characteristics, which aim to regulate the traffic flow from the on-ramp to the freeway network.…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road

Jiang

Zhou

et al. 2021

Sustainability

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The phenomenon of stop-and-go traffic and its environmental impact has become a crucial issue that needs to be tackled, in terms of the junctions between freeway and urban road networks, which consist of freeway off-ramps, downstream intersections, and the junction section. The development of Connected and Automated Vehicles (CAVs) has provided promising solutions to tackle the difficulties that arise along intersections and freeway off-ramps separately. However, several problems still exist that need to be handled in terms of junction structure, including vehicle merging trajectory optimization, vehicle crossing trajectory optimization, and heterogeneous decision-making. In this paper, a two-stage CAV trajectory optimization strategy is presented to improve fuel economy and to reduce delays through a joint framework. The first stage considers an approach to determine travel time considering the different topological structures of each subarea to ensure maximum capacity. In the second stage, Pontryagin’s Minimum Principle (PMP) is employed to construct Hamiltonian equations to smooth vehicle trajectory under the requirements of vehicle dynamics and safety. Targeted methods are devised to avoid driving backwards and to ensure an optimal vehicle gap, which make up for the shortcomings of the PMP theory. Finally, simulation experiments are designed to verify the effectiveness of the proposed strategy. The evaluation results show that our strategy could effectively militate travel delays and fuel consumption.

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Cooperative decision-making for mixed traffic: A ramp merging example

Cited by 75 publications

References 27 publications

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

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

Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road

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