Fuzzy peak hour for urban road traffic network

被引:14
|
作者
Tian, Zhao
Jia, Li-Min [1 ]
Dong, Hong-Hui
机构
[1] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
来源
MODERN PHYSICS LETTERS B | 2015年 / 29卷 / 15期
关键词
Fuzzy peak hour; urban traffic; possibility; probability; complex network; TRAPEZOIDAL APPROXIMATIONS; TRIANGULAR APPROXIMATIONS; COMPLEX NETWORKS; NEAREST INTERVAL; PROBABILITY; SEGMENTATION; ALGORITHMS; NUMBERS; MODEL;
D O I
10.1142/S0217984915500748
中图分类号
O59 [应用物理学];
学科分类号
摘要
Traffic congestion is now nearly ubiquitous in many urban areas and frequently occurs during rush hour periods. Rush hour avoidance is an effective way to ease traffic congestion. It is significant to calculate the rush hour for alleviating traffic congestion. This paper provides a method to calculate the fuzzy peak hour of the urban traffic network considering the flow, speed and occupancy. The process of calculation is based on betweenness centrality of network theory, optimal separation method, time period weighting, probability-possibility transformations and trapezoidal approximations of fuzzy numbers. The fuzzy peak hour of the urban road traffic network (URTN) is a trapezoidal fuzzy number [m(1), m(2), m(3), m(4)]. It helps us (i) to confirm a more detailed traffic condition at each moment, (ii) to distinguish the five traffic states of the traffic network in one day, (iii) to analyze the characteristic of appearance and disappearance processes of the each traffic state and (iv) to find out the time pattern of residents travel in one city.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Still stuck in traffic: Coping with peak-hour traffic congestion
    Chatman, DG
    [J]. URBAN STUDIES, 2005, 42 (12) : 2329 - 2331
  • [43] A method of traffic variable estimation based on neuro-fuzzy on urban road
    Fu, HY
    [J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 530 - 534
  • [44] Fuzzy evaluation model of urban road traffic capacity of anti-congestion
    Liu, Xin-Min
    Li, Zhi-Peng
    Ding, Li-Li
    Yan, Chun
    [J]. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2013, 13 (04): : 114 - 119
  • [45] Research on the Traffic Situation Identification Method of the Urban Periphery Road Network
    Ca, Jinjin
    Jia, Yuanhua
    Shu, Zhiqiang
    [J]. ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL III, PROCEEDINGS, 2009, : 720 - +
  • [46] A Dynamic Algorithm of Partitioning Urban Road Network into Traffic Control Subareas
    Gong, Bowen
    Bie, Yiming
    Liu, Zhiyuan
    [J]. SUSTAINABLE DEVELOPMENT AND ENVIRONMENT II, PTS 1 AND 2, 2013, 409-410 : 1398 - +
  • [47] Bayesian spatial joint modeling of traffic crashes on an urban road network
    Zeng, Qiang
    Huang, Helai
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2014, 67 : 105 - 112
  • [48] Sequential Graph Neural Network for Urban Road Traffic Speed Prediction
    Xie, Zhipu
    Lv, Weifeng
    Huang, Shangfo
    Lu, Zhilong
    Du, Bowen
    Huang, Runhe
    [J]. IEEE ACCESS, 2020, 8 : 63349 - 63358
  • [49] Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management
    Faye, Sebastien
    Chaudet, Claude
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 65 (07) : 5720 - 5725
  • [50] Approaches to control acyclic traffic lights in an exemplary urban road network
    Ruchaj, Marcin
    Stanislawski, Rafal
    [J]. 2011 16TH INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS, 2011, : 387 - 392