Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 3907-3917.doi: 10.13229/j.cnki.jdxbgxb.20240484

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Regional parking allocation model based on improved Kepler optimization algorithm

Zhe WANG1(),Wen-bo FAN1(),Xin LIU1,Huan YANG2,Xian-min SONG2,Bai-ting YANG3   

  1. 1.Center for Big Data and Network Management，Jilin University，Changchun 130022，China
    2.College of Transportation，Jilin University，Changchun 130022，China
    3.Changchun Polytechnic，Changchun 130033，China
  • Received:2024-05-06 Online:2025-12-01 Published:2026-02-03
  • Contact: Wen-bo FAN E-mail:wangzhe@jlu.edu.cn;fwb@jlu.edu.cn

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Abstract:

In order to improve the utilization efficiency of existing parking resources and reduce the traffic congestion， exhaust emissions and other problems caused by travelers' invalid parking space search behavior， a multi-objective nonlinear integer programming model for optimizing the allocation of parking resources was proposed. Firstly， based on the comprehensive consideration of user walking distance， parking fees， the balance of utilization of various parking lots in the area， and the additional traffic pressure attached to parking behaviors， the personal cost and social cost functions of parking allocation were established. Then， with the optimization objective of minimizing the comprehensive cost of the system， a multi-parking intelligent parking allocation model was constructed， and at the same time， considering the complexity of model solving， an improved Kepler optimization algorithm integrating multiple strategies was designed for model solving. Finally， in order to test the effectiveness of the model， numerical experiments under different parking supply and demand situations were designed， and the proposed model and algorithm were compared and analyzed with the classical allocation model and the traditional solution algorithms. The results show that the proposed model reduces the individual cost by 4.4% on average， and the balance of utilization of each parking lot is significantly improved. Meanwhile， the proposed model has obvious advantages in reducing the additional traffic pressure of the road network caused by parking groups， with a maximum reduction of 33.6% in the impedance growth rate of the road segment. Compared with traditional genetic algorithm and simulated annealing algorithm， the proposed improved Kepler optimization algorithm has faster convergence speed and a better ability to search for optimal solutions.

Key words: intelligent transportation, parking lots allocation, system comprehensive cost, integral programing, improved Kepler optimization algorithm

CLC Number: 

  • U491.2

Fig.1

Diagram of intelligent parking lots assignment system"

Fig.2

IKOA flow"

Fig.3

Experimental road network information"

Fig.4

Parameter ω impact analysis"

Table 1

Parameter ω optimal value"

停车需求数总停车场供给数
1 0001 6002 000
6000.001 200.000 730.000 33
8000.000 930.000 650.000 84
1 0000.006 200.000 500.000 56

Fig.5

Parameter ω fitting of surface results"

Fig.6

Comparison of evaluation indicators after allocation for different demand scenarios"

Fig.7

Comparison of the degree of congestion after allocation of different demand scenarios"

Fig.8

V/C Comparison chart by segment"

Table 2

Impedance change rate of different road sections"

路段

id

位置分配模型
OS-PAM-PAO-PA
2-5A7.59.35.6
4-513.817.116.7
5-23.63.63.6
5-43.63.63.6
5-6A&D6.110.613.7
6-5A&D9.815.89.9
6-11B&D9.84530.4
11-6B&D6.112.86.1
10-11B10.38.18.3
11-103.63.63.6
11-123.63.63.6
12-1133.219.37.5
7-8C10.77.117.9
8-73.63.63.6
8-93.620.17
8-153.63.63.6
9-83.613.937.2
15-832.231.747.4
6-7D9.89.89.8
6-99.812.316.4
7-66.16.16.1
9-66.111.97.8
10-66.16.16.1

Fig.9

Algorithm performance comparison chart"

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