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Keywords = jam-absorption driving

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23 pages, 7908 KiB  
Article
Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway
by Haizhen Li, Claudio Roncoli, Weiming Zhao and Yongfeng Ju
Sustainability 2024, 16(8), 3179; https://doi.org/10.3390/su16083179 - 10 Apr 2024
Cited by 5 | Viewed by 2163
Abstract
The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving [...] Read more.
The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving (JAD) strategies on circular and linear scenarios have been evaluated. The manual vehicle is the intelligent driver model (IDM) and human driver model (HDM), respectively. The results suggest that on the ring road, the traffic performance of mixed traffic improves gradually with the increase of the proportion of CAVs under the ACC. Moreover, the traffic performance for the JAD strategy does not improve infinitely with the increase in the number of CAVs. Conversely, the FS strategy suppresses traffic waves at the cost of reducing traffic flow, and more CAVs are not beneficial for mixed traffic. It is interesting to note that under optimal performance in these three strategies, the FS strategy has the lowest number of CAVs, while the ACC strategy has the highest number of CAVs. For the linear road, it demonstrates that the JAD strategy exhibits a superior performance compared to the ACC. However, the FS strategy cannot dissipate traffic waves due to an insufficient buffer gap. Different models have varying effects on different strategies. Full article
(This article belongs to the Special Issue Autonomous Systems and Intelligent Transportation Systems)
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14 pages, 2886 KiB  
Article
A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic
by Haizhen Li, Claudio Roncoli and Yongfeng Ju
Appl. Sci. 2024, 14(4), 1424; https://doi.org/10.3390/app14041424 - 9 Feb 2024
Cited by 4 | Viewed by 1874
Abstract
Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy [...] Read more.
Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway. Full article
(This article belongs to the Special Issue Transportation Planning, Management and Optimization)
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