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    Wyświetlanie 1-3 z 3
    Tytuł:
    Simulation and Comparison of Two Fusion Methods for Macroscopic Fundamental Diagram Estimation
    Autorzy:
    Lin, Xiaohui
    Xu, Jianmin
    Cao, Chengtao
    Powiązania:
    https://bibliotekanauki.pl/articles/224073.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic engineering
    Inżynieria ruchu
    Opis:
    Accurate estimation of macroscopic fundamental diagram (MFD) is the precondition of MFD’s application. At present, there are two traditional estimation methods of road network’s MFD, such as the loop detector data (LDD) estimation method and the floating car data (FCD) estimation method, but there are limitations in both traditional estimation methods. In order to improve the accuracy of road network MFD estimation, a few scholars have studied the fusion method of road network MFD estimation, but there are still some shortcomings on the whole. However, based on the research of adaptive weighted averaging (AWA) fusion method for MFD estimation of road network, I propose to use the MFD’s two parameters of road network obtained by LDD estimation method and FCD estimation method, and establish a back-propagation neural network data fusion model for MFD parameters of road network (BPNN estimation fusion method), and then the micro-traffic simulation model of connected-vehicle network based on Vissim software is established by taking the intersection group of the core road network in Tianhe District of Guangzhou as the simulation experimental area, finally, compared and analyzed two MFD estimation fusion methods of road network, in order to determine the best MFD estimation fusion method of road network. The results show that the mean absolute percent error (MAPE) of the parameters of road network’s MFD and the average absolute values of difference values of the state ratio of road network’s MFD are both the smallest after BPNN estimation fusion, which is the closest to the standard MFD of road network. It can be seen that the result of BPNN estimation fusion method is better than that of AWA estimation fusion method, which can improve the accuracy of road network MFD estimation effectively.
    Źródło:
    Archives of Transport; 2019, 51, 3; 35-48
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Road network partitioning method based on Canopy-Kmeans clustering algorithm
    Autorzy:
    Lin, Xiaohui
    Xu, Jianmin
    Powiązania:
    https://bibliotekanauki.pl/articles/949836.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic engineering
    road network
    road network partition
    Canopy-Kmeans algorithm
    macroscopic fundamental diagram
    inżynieria ruchu
    sieć drogowa
    sterowanie siecią drogową
    algorytm Canopy-Kmeans
    Opis:
    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.
    Źródło:
    Archives of Transport; 2020, 54, 2; 95-106
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram.
    Autorzy:
    Lin, Xiaohui
    Xu, Jianmin
    Powiązania:
    https://bibliotekanauki.pl/articles/224037.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic engineering
    oversaturated intersection
    multilayer boundary
    macroscopic fundamental diagram
    feedforward feedback
    iterative control
    inżynieria ruchu
    przesycone skrzyżowanie
    granica wielowarstwowa
    makroskopowy diagram fundamentalny
    sprzężenie zwrotne
    Opis:
    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.
    Źródło:
    Archives of Transport; 2020, 53, 1; 67-87
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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