Resilient perimeter control for hyper-congested two-region networks with MFD dynamics

Gao, Shengling; Li, Daqing; Zheng, Nan; Hu, Ruiqi; She, Zhikun

doi:10.1016/j.trb.2021.12.003

Cited by 23 publications

(7 citation statements)

References 51 publications

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“…Specifically, a microscopic traffic simulator could strongly improve the realism of the application case study, given its ability to emulate more precisely the stochasticity of flow propagation and traffic dynamics. In this regard, during the last years some efforts have been made in order to provide more realistic case studies through the usage of Simulation tools in [55], [56], [57]. Moreover, typically the operating scenarios of these works are not based on real (historical data) data taken from the field, and the provided network infrastructures do not cover the dynamics of a whole city.…”

Section: B Limitations and Challengesmentioning

confidence: 99%

Multiobjective Model Predictive Control Based on Urban and Emission Macroscopic Fundamental Diagrams

Tesone,

Tettamanti,

Varga

et al. 2024

IEEE Access

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Increasing motorization represents a severe problem worldwide, also affecting the emission levels of the road network. Accordingly, congestion management has obtained growing importance because of its strong economic, social, and environmental implications. Macroscopic Fundamental Diagram (MFD) based traffic control is a popular and efficient approach in this scientific field. In our research work the urban network has been divided into homogeneous regions, each of them characterized by its own MFD, and they are regulated using a network-level control scheme. The proposed Multiobjective Model Predictive Control (M-MPC) takes into account the congestion and CO 2 emission levels of the urban network, modelled by the emerging Emission Macroscopic Fundamental Diagram (e-MFD). The applied strategy has been demonstrated in a realistic traffic scenario (Luxembourg City) using validated microscopic traffic simulation. According to the introduced multiobjective approach, the control method can better exploit the road network capacity while efficiently reducing traffic-induced emissions.

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Section: B Limitations and Challengesmentioning

confidence: 99%

Multiobjective Model Predictive Control Based on Urban and Emission Macroscopic Fundamental Diagrams

Tesone,

Tettamanti,

Varga

et al. 2024

IEEE Access

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“…On the basis of the dynamic vehicle distribution rate on the entrance lane of the central intersection in the type-I and type-II groups, the range of vehicle number suitable for high efficient operation in the regional road network, that is, the size of the vehicle capacity of the regional road network can be obtained indirectly. (10) where Qin is the number of vehicles, that is, the capacity of the regional road network; i is the number of each intersection in the regional road network; j is the number of each entrance of the intersection; ηi,j is vehicle distribution rate on the section of entrance j at intersection i; li,j is the section length of entrance j at intersection i; E is the set of all boundary intersections in the regional road network; F is the set of external sections of the regional road network corresponding to each boundary intersection; ρ is the average distance between two adjacent vehicles in the queue.…”

Section: Vehicle Capacity Calculation Of Regional Road Network Based ...mentioning

confidence: 99%

“…According to equation (10), since the vehicle distribution rate on each section is a numerical range, the total number of vehicles that allowed running in the regional road network is also a numerical range. When the vehicle distribution rate on each section takes the minimum value, the total number of vehicles running in the regional road network is the smallest and the traffic is flowing smoothly and steadily.…”

Section: As Shown Inmentioning

confidence: 99%

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Research on the optimal control method of regional road network based on dynamic vehicle distribution

Shen¹,

Li²,

Li³

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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The traffic optimization control of the regional road network often involves two parts: the internal signal optimization control and the boundary flow control. This paper proposes some new methods for the traffic optimization of regional road network. First, the intersections inside the road network are divided to two types of groups by overlapping division method. Then the internal traffic flow characteristics for each type of group are analyzed to determine the vehicle distribution rate on the section. Based on the vehicle distribution rate, the vehicle capacity of the regional road network is calculated indirectly, providing reasonable threshold for boundary intersection flow control. According to previous research, a two-stage fuzzy control scheme is designed for the type-II central intersection, realizing the coordinated control between adjacent intersections and optimizing the operation of traffic flow in the regional road network effectively. Finally, the effectiveness and rationality of each method proposed in this paper are proved by simulation results.

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“…Usually, PC monitors the transfer flow of PN based on a Macroscopic or Network Fundamental Diagram (MFD or NFD), which depicts the low-scatter relation between the outflow (or total travel distance) and accumulation (or total travel time) [12]. PC tries to maximize the outflow of PN by keeping the vehicle accumulation in the critical range of the MFD [7].…”

Section: Introductionmentioning

confidence: 99%

Distributed Signal Control for Multi-Modal Traffic Network: A Deep Reinforcement Learning Approach

Laharotte

Leclercq

2022

SSRN Journal

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Resilient perimeter control for hyper-congested two-region networks with MFD dynamics

Cited by 23 publications

References 51 publications

Multiobjective Model Predictive Control Based on Urban and Emission Macroscopic Fundamental Diagrams

Multiobjective Model Predictive Control Based on Urban and Emission Macroscopic Fundamental Diagrams

Research on the optimal control method of regional road network based on dynamic vehicle distribution

Distributed Signal Control for Multi-Modal Traffic Network: A Deep Reinforcement Learning Approach

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