A novel control strategy for balancing traffic flow in urban traffic network based on iterative learning control

Yan, Fei; Yan, Gaowei; Ren, Mifeng; Tian, Jianyan; Shi, Zhongke

doi:10.1016/j.physa.2018.05.134

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…MFD [46] is a type of traffic flow fundamental diagram that relates space-mean flow, density, and speed of an entire network with n number of links. e experiments are illustrated in Figure 11 and show that the better efficiency of the proposed method is in comparison to the proposed method by Yan et al [45]. e results of the CTM model are presented in Figure 12.…”

Section: Results Of the Simulation Scenariomentioning

confidence: 92%

“…Scenarios 1,2,4,and 5. rough this section, the results of simulation scenarios 1, 2, 4, and 5 in a 4 × 4 network is presented. e parameters of scenarios 1 and 2 are taken from the seminal work of Yan et al [45]. e simulation scenarios 4 and 5 are simulated by higher traffic demands, with two and three times more traffic demand than scenario 2, respectively.…”

Section: Results Of the Simulation Scenariomentioning

confidence: 99%

“…e simulation scenarios 4 and 5 are simulated by higher traffic demands, with two and three times more traffic demand than scenario 2, respectively. e reported results by Yan et al [45], which include 5 run with different random seeds (MFD1, . .…”

Section: Results Of the Simulation Scenariomentioning

confidence: 99%

“…To evaluate the proposed method and make a justified comparison, two networks are deployed from literature [44,45]. e networks are squared two-way grid networks similar to Figure 6 with different sizes: 8 × 8, 5 × 5, and 4 × 4.…”

Section: Traffic Networkmentioning

confidence: 99%

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An Innovative Eigenvector-Based Method for Traffic Light Scheduling

Soltani-Sarvestani

Azimifar

Wong

et al. 2020

Journal of Advanced Transportation

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This paper introduces two traffic light strategies to control traffic and avoid traffic jam in urban networks. One strategy is a new traffic light scheduling system, which controls traffic light using local variables (waiting time and number of vehicle on links) but has a global impact on the traffic, using shared variables between neighbour intersections. The proposed traffic light scheduling system is designed based on eigenvector centrality of intersection relation matrix. The intersection relation matrix is a new representation of a junction which indicates the traffic relation between intersection’s links and adjacent intersections. The second contribution is expanding a new dual mode traffic light strategy (namely, Exit Status Traffic Light (ETL)), which notifies the drivers whether they are allowed to exit a street or not. In other words, vehicles are allowed to enter a street in both red and green ETL, but they are not allowed to exit the street for a long time in red ETL (while traffic is heavy in the subnetwork). The ETL gives a chance to relax traffic in a subnetwork and avoid traffic jam. The effectiveness of the proposed strategy is analysed and evaluated by a number of simulations on three-way grid networks. Two-way rectangular grid networks are modelled via a cell transmission model (CTM). The macroscopic fundamental diagram (MFD) and the number of jammed cells are compared with two state-of-the-art methods.

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Section: Results Of the Simulation Scenariomentioning

confidence: 92%

Section: Results Of the Simulation Scenariomentioning

confidence: 99%

Section: Results Of the Simulation Scenariomentioning

confidence: 99%

Section: Traffic Networkmentioning

confidence: 99%

See 2 more Smart Citations

An Innovative Eigenvector-Based Method for Traffic Light Scheduling

Soltani-Sarvestani

Azimifar

Wong

et al. 2020

Journal of Advanced Transportation

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“…In this domain, Yan et al [88] created an experiment which used MATLAB and the VISSIM simulator in a 4 × 4 grid road network environment. This paper proposed a method of novel model‐free iterative learning control (ILC) for balancing traffic flows.…”

Section: Literature Analysismentioning

confidence: 99%

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Rydzewski¹,

Czarnul²

2020

IET intell. transp. syst

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Over the past few decades, the increasing number of vehicles and imperfect road traffic management have been sources of congestion in cities and reasons for deteriorating health of its inhabitants. With the help of computer simulations, transport engineers optimise and improve the capacity of city streets. However, with an enormous number of possible simulation types, it is difficult to grasp valuable, innovative solutions which are of the greatest value to city citizens. In this work, the authors expose various problems within this area having reviewed and analysed over 130 papers selected out of 1200 works in the field of urban simulations. The study describes the selection process of important papers and highlights characteristics of microsimulations, macrosimulations, computation optimisations and other approaches found in the literature which are especially useful and should be further built on in the future. They present and compare results provided in reviewed works in terms of throughput improvement, queue, waiting and travel time reduction, vehicle speed increase, speed‐ups as well as assumed simulation parameters. Finally, they focus on research gaps, such as a small number of works considering crisis simulations, few real‐world scale simulations as well as on software architectural changes and low‐level optimizations.

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Iterative learning heterogeneous trajectory tracking for partially interdependent networks

Guo

Chai

et al. 2021

Asian Journal of Control

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Trajectory tracking is a hot topic in the field of complex systems. In this paper, based on the Lyapunov stability theory, combined with the adaptive iterative learning control method, the heterogeneous trajectory tracking of partially interdependent networks is investigated in a given finite time interval. The considered network consists of two subnets, where only the first m nodes are intercoupled. Firstly, some suitable iterative learning controllers, as well as the adaptive laws and the coupling strength learning laws, are designed for each node. Secondly, the boundedness and the convergence of the signals are proved using the proposed control strategy. Finally, some simulations are presented to illustrate the effectiveness and feasibility of the theoretical results presented in this paper. Generally, the accuracy trajectory tracking is found almost proportional to the number of iterations. When the number of iterations is fixed, with the increasing number of nodes having interdependence, the time required to track target accurately is reduced.

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A novel control strategy for balancing traffic flow in urban traffic network based on iterative learning control

Cited by 9 publications

References 18 publications

An Innovative Eigenvector-Based Method for Traffic Light Scheduling

An Innovative Eigenvector-Based Method for Traffic Light Scheduling

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Iterative learning heterogeneous trajectory tracking for partially interdependent networks

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