scholarly journals Cyber‐physical description and CPS‐based pinning approach of mixed traffic

Author(s):  
Zhongcheng Liu ◽  
Dihua Sun ◽  
Min Zhao ◽  
Hang Zhao ◽  
Shihui Wang ◽  
...  
2014 ◽  
Vol 24 (8) ◽  
pp. 2151-2164 ◽  
Author(s):  
Shu-Sheng WEN ◽  
Jiong HUANG ◽  
Ting SHU ◽  
Wei-Qiang XU ◽  
Ya-Ming WANG

2021 ◽  
Vol 152 ◽  
pp. 106006
Author(s):  
Iman Mahdinia ◽  
Amin Mohammadnazar ◽  
Ramin Arvin ◽  
Asad J. Khattak

Author(s):  
Paolo Delle Site

For networks with human-driven vehicles (HDVs) only, pricing with arc-specific tolls has been proposed to achieve minimization of travel times in a decentralized way. However, the policy is hardly feasible from a technical viewpoint without connectivity. Therefore, for networks with mixed traffic of HDVs and connected and autonomous vehicles (CAVs), this paper considers pricing in a scenario where only CAVs are charged. In contrast to HDVs, CAVs can be managed as individual vehicles or as a fleet. In the latter case, CAVs can be routed to minimize the travel time of the fleet of CAVs or that of the entire fleet of HDVs and CAVs. We have a selfish user behavior in the first case, a private monopolist behavior in the second, a social planner behavior in the third. Pricing achieves in a decentralized way the social planner optimum. Tolls are not unique and can take both positive and negative values. Marginal cost pricing is one solution. The valid toll set is provided, and tolls are then computed according to two schemes: one with positive tolls only and minimum toll expenditure, and one with both tolls and subsidies and zero net expenditure. Convergent algorithms are used for the mixed-behavior equilibrium (simplicial decomposition algorithm) and toll determination (cutting plane algorithm). The computational experience with three networks: a two-arc network representative of the classic town bypass case, the Nguyen-Dupuis network, and the Anaheim network, provides useful policy insight.


2021 ◽  
Vol 11 (6) ◽  
pp. 2574
Author(s):  
Filip Vrbanić ◽  
Edouard Ivanjko ◽  
Krešimir Kušić ◽  
Dino Čakija

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions.


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