Intelligent Control Strategies for Vehicle Departure in Urban Complex Parking Lots of the Jinding Area in Shanghai, China

被引:0
|
作者
Jiang, Shengchuan [1 ]
Wang, Jindong [1 ,2 ]
Du, Zhouyang [3 ]
机构
[1] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Shanghai 201804, Peoples R China
[2] Shanghai Jinqiao Grp Co Ltd, Shanghai 201206, Peoples R China
[3] Shanghai Pudong Dev Grp Co Ltd, Shanghai 201204, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 17期
关键词
smart parking; cooperative control; control strategy; SUMO simulation;
D O I
10.3390/app12178781
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application This study introduces ramp control into parking management and proposes intelligent control strategies for vehicle departure in urban complex parking lots. Specifically, in these strategies, the frequency of gate lever lift is optimized using timing control and inductive control. The proposed intelligent control strategies are applied in the Jinding area in Shanghai, China, with large underground parking lots. Compared to non-controlled situations, the driving efficiency of the ground-level traffic is improved. The entrances and exits of underground parking lots of large complexes are the key nodes for the conversion between ground-level dynamic traffic and underground static traffic. Since congestion is caused by a large number of vehicles leaving parking lots at peak hours, the departure control strategy can effectively manage vehicle departure and reduce the congestion of ground-level traffic. In this study, we introduce cooperative control in ramp control into parking lot exit management. The frequency of parking lot exit gate lever lift is used as the control and optimization variable. To ensure the efficiency of regional traffic, we designed timing and inductive control strategies to control the speed of departing vehicles. In an experimental model, we took Shanghai Jinding super-large underground parking lot as an example. The changes in the external road network were simulated when different strategies were implemented on the Simulation of Urban Mobility (SUMO) simulation platform. The experimental results show that the proposed control strategies can significantly ease the congestion of the regional road network, improve the average speed of dynamic traffic, and reduce the queue length at intersections.
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页数:11
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  • [1] Exploring the Solution Space of Self-Automated Parking Lots: An Empirical Evaluation of Vehicle Control Strategies
    d'Orey, Pedro M.
    Azevedo, Jose
    Ferreira, Michel
    [J]. 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2016, : 1134 - 1140
  • [2] Levels, profiles and gas-particle distribution of atmospheric PCDD/Fs in vehicle parking lots of a South China metropolitan area
    Li, Huiru
    Zhou, Lin
    Ren, Man
    Sheng, Guoying
    Fu, Jiamo
    Peng, Ping'an
    [J]. CHEMOSPHERE, 2014, 94 : 128 - 134
  • [3] An intelligent green vehicle management system for urban food reliably delivery:A case study of Shanghai, China
    Fu, Zhengtang
    Dong, Peiwu
    Ju, Yanbing
    Gan, Zhenkun
    Zhu, Min
    [J]. ENERGY, 2022, 257
  • [4] How blockchain renovate the electric vehicle charging services in the urban area? A case study of Shanghai, China
    Fu, Zhengtang
    Dong, Peiwu
    Li, Siyao
    Ju, Yanbing
    Liu, Hanbo
    [J]. JOURNAL OF CLEANER PRODUCTION, 2021, 315
  • [5] Ammonia in urban atmosphere can be substantially reduced by vehicle emission control: A case study in Shanghai, China
    Wu, Can
    Lv, Shaojun
    Wang, Fanglin
    Liu, Xiaodi
    Li, Jin
    Liu, Lang
    Zhang, Si
    Du, Wei
    Liu, Shijie
    Zhang, Fan
    Li, Jianjun
    Meng, Jingjing
    Wang, Gehui
    [J]. JOURNAL OF ENVIRONMENTAL SCIENCES, 2023, 126 : 754 - 760
  • [6] Ammonia in urban atmosphere can be substantially reduced by vehicle emission control:A case study in Shanghai, China
    Can Wu
    Shaojun Lv
    Fanglin Wang
    Xiaodi Liu
    Jin Li
    Lang Liu
    Si Zhang
    Wei Du
    Shijie Liu
    Fan Zhang
    Jianjun Li
    Jingjing Meng
    Gehui Wang
    [J]. Journal of Environmental Sciences, 2023, (04) : 754 - 760