Research on parking allocation strategy of stereo garage based on time cluster reasoning
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Affiliation:

1.School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;2.Key Laboratory of Four Power BIM Engineering and Intelligent Application Railway Industry, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China

Clc Number:

U491.7

Fund Project:

Industry Research Innovation Fund of Chinese Universities(2021LDA07002),Natural Science Foundation of Gansu Province (20JR5RA396), “Innovation Star” Excellent Postgraduates Project of Gansu Province Education Department (2022CXZX-620), and Open Fund of Key Laboratory of Four Power BIM Engineering and Intelligent Application Railway Industry(BIMKF-2021-06).

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    Abstract:

    Based on the arrival-departure time data of vehicles in stereo garage, k-means clustering method was used to classify vehicles according to the arrival frequency of access vehicles in different periods, and cubic cluster criterion was used as the evaluation index to evaluate the classification credibility. Based on the reasoning results of vehicle arrival-departure time division and the relationship between the total service time of equipment from I/O to the parking space and the length of stay time, a mathematical model of parking space partition allocation in stereo garage is established. With defining the average customer waiting time as stereo garage efficiency evaluation index, the efficiency index simulations of the nearby allocation and the proposed partition clustering reasoning allocation were carried out. The simulation results show that the proposed allocation strategy, compared with nearby allocation strategy, can effectively shorten the customer waiting time, and the customer waiting time reduced by 9.5%. The results provide reference for the parking space allocation process of such garages, and provide decision support for improving the operation efficiency of garages.

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马尚鹏,李建国,杨波.基于时间聚类推理的立体车库车位分配策略研究[J].重庆大学学报,2024,47(8):47~54

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History
  • Received:December 27,2021
  • Revised:
  • Adopted:
  • Online: September 02,2024
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