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Saturated arterial coordinate control strategy optimization considering macroscopic fundamental diagram

Lin Xuanhua, Lin Xiaohui, Chen Kelian

Archives of Transport

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2022

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Vol. 62, iss. 2

73--90

EN

MFD is widely used in traffic state evaluation because of its description of the macro level of urban road network. Aiming at the control strategy optimization problem of urban arterial road network under saturated traffic flow state, this study analyzes the MFD characteristics of a typical three-segment "ascending-stable-descending segment" and its advantages in characterizing the macroscopic operation efficiency of the road network, a arterial coordination control strategy considering MFD is proposed. According to the characteristics of MFD, it is proposed that the slope of the ascending segment and the capacity of the road network represent the operating efficiency of the free flow and saturated flow of the road network respectively. The traffic flow and density data of road segment are obtained by the road detector through Vissim simulation software. Aiming at the problem that the MFD is too discrete due to unreasonable control strategy or traffic condition, and in order to extract the MFD optimization target indicators, it is proposed to extract the key boundary points of the MFD by the “tic-tac-toe” method and divide the MFD state by Gaussian mixture clustering. The genetic algorithm integrates the multi-objective particle swarm algorithm as the solution algorithm, and the simulation iterative process is completed through Python programming and the com interface of Vissim software. In order to verify the validity of the model and algorithm, the actual three-intersections arterial road network is used for verification, and the model in this study is compared with the optimization model without considering MFD, the model solved by traditional algebraic method, and the optimization model solved by typical multi-objective particle swarm. Results show that the model in this research performs well in efficiency indicators such as total delay, average delay, and queue coefficient. At the same time, the MFD form has highest stability, the control effect is the best in the saturated state. The solution algorithm GA-MOPSO also has a better solution effect.

2

Road network partitioning method based on Canopy-Kmeans clustering algorithm

Lin Xiaohui, Xu Jianmin

Archives of Transport

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2020

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Vol. 54, iss. 2

95--106

EN

With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the characteristics of traffic flow. However, road network partition can be regarded as a clustering process of the division of road segments with similar attributes, and thus, the clustering algorithm can be used to divide the sub-areas of road network, but when Kmeans clustering algorithm is used in road network partitioning, it is easy to fall into the local optimal solution. Therefore, we proposed a road network partitioning method based on the Canopy-Kmeans clustering algorithm based on the real-time data collected from the central longitude and latitude of a road segment, average speed of a road segment, and average density of a road segment. Moreover, a vehicle network simulation platform based on Vissim simulation software is constructed by taking the real-time collected data of central longitude and latitude, average speed and average density of road segments as sample data. Kmeans and Canopy-Kmeans algorithms are used to partition the platform road network. Finally, the quantitative evaluation method of road network partition based on macroscopic fundamental diagram is used to evaluate the results of road network partition, so as to determine the optimal road network partition algorithm. Results show that these two algorithms have divided the road network into four sub-areas, but the sections contained in each sub-area are slightly different. Determining the optimal algorithm on the surface is impossible. However, Canopy-Kmeans clustering algorithm is superior to Kmeans clustering algorithm based on the quantitative evaluation index (e.g. the sum of squares for error and the R-Square) of the results of the subareas. Canopy-Kmeans clustering algorithm can effectively partition the road network, thereby laying a foundation for the subsequent road network boundary control.

3

Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram.

Lin Xiaohui, Xu Jianmin

Archives of Transport

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2020

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Vol. 53, iss. 1

67--87

EN

The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feed forward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.