[1] ZHANG Chu, CHEN Kehan, CHEN Jun, CHEN Jiayi, et al. Mixed parking demand assignment in hub parking lots based on regression modeling [J]. Journal of Southeast University (English Edition), 2025, 41 (3): 270-277. [doi:10.3969/j.issn.1003-7985.2025.03.002]

Copy

Mixed parking demand assignment in hub parking lots based on regression modeling()

基于回归建模的枢纽停车场内混合停放需求分配

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

41

Issue:

2025 3

Page:

270-277

Research Field:

Traffic and Transportation Engineering

Publishing date:

2025-09-11

Info

Title:

Mixed parking demand assignment in hub parking lots based on regression modeling

基于回归建模的枢纽停车场内混合停放需求分配

Author(s):

ZHANG Chu^1,2, CHEN Kehan¹, CHEN Jun², CHEN Jiayi¹

1.School of Transportation, Southeast University, Nanjing 211189, China
2.Jiangsu Key Laboratory of Comprehensive Transportation Planning and Simulation, Southeast University, Nanjing 211189, China

张楚^1,2, 陈可涵¹, 陈峻², 陈嘉毅¹

1.东南大学交通学院，南京 211189
2.东南大学江苏省综合交通运输规划与仿真重点实验室，南京 211189

Keywords:

parking areas assignment;  hub parking lot;  regression-based modeling;  extreme gradient boosting (XGBoost)

停车区域分配; 枢纽停车场; 回归建模; 极致梯度提升（XGBoost）

PACS:

U495

DOI:

10.3969/j.issn.1003-7985.2025.03.002

Abstract:

To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs, a mixed parking demand assignment method based on regression modeling is proposed. First, an optimal model aiming to minimize total time expenditure is constructed. It incorporates parking search time, walking time, and departure time, focusing on short-term parking features. Then, the information dimensions that the parking lot can obtain are evaluated, and three assignment strategies based on three types of regression models—linear regression (LR), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP)—are proposed. A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection, model training, and case studies. Finally, the performance of the three strategies is compared, revealing that the XGBoost-based strategy performs the best in case parking lots, which reduces time expenditure by 29.3% and 37.2%, respectively, compared with the MLP-based strategy and LR-based strategy. This method offers diverse options for practical parking management.

为了适应客运枢纽配建停车场的特殊供需特征，提出了一种基于回归建模的枢纽停车场内部混合停放需求分配方法。首先，充分考虑寻泊、步行及离场时间，并关注短时停放特性，建立以综合时间成本最小为目标的分配模型。其次，估计停车场可获取的信息维度，提出3种基于回归建模的分配策略，回归模型分别采用线性回归、XGBoost和多层感知机3类模型。同时，利用交通仿真软件SUMO构建停车场停放过程仿真模型，为数据收集、模型训练及案例研究提供有力支持。最后，通过在案例中比较3种策略效果发现，基于XGBoost回归的分配策略在停车场中实现了最优分配，消耗的时间成本较基于多层感知机回归和线性回归的策略分别下降29.3%和37.2%。这为实际应用中的停车管理提供了多样化的选择。

References:

[1]ZOU B, KAFLE N, WOLFSON O, et al. A mechanism design based approach to solving parking slot assignment in the information era[J]. Transportation Research Part B: Methodological, 2015, 81: 631-653.
[2]KIM O T T, TRAN N H, PHAM C, et al. Parking assignment: Minimizing parking expenses and balancing parking demand among multiple parking lots[J]. IEEE Transactions on Automation Science and Engineering, 2020, 17(3): 1320-1331.
[3]SHAO C Y, YANG H, ZHANG Y, et al. A simple reservation and allocation model of shared parking lots[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 303-312.
[4]WANG S F, LI Z H, XIE N. A reservation and allocation model for shared-parking addressing the uncertainty in drivers’ arrival/departure time[J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103484.
[5]SUN C, YIN H W, SONG M C, et al. Rolling time domain allocation of regional shared parking considering potential demand[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 141-152. (in Chinese)
[6]AYALA D, WOLFSON O, XU B, et al. Parking slot assignment games[C]//Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Chicago, IL, USA, 2011: 299-308.
[7]MEJRI N, AYARI M, LANGAR R, et al. Cooperation versus competition towards an efficient parking assignment solution[C]//2014 IEEE International Conference on Communications (ICC). Sydney, NSW, Australia, 2014: 2915-2920.
[8]SHEN J X, LIU Q X, ZHENG C J, et al. Bus fleet replacement optimization considering life-cycle carbon emissions and total cost of ownership [J]. Journal of Southeast University (English Edition), 2024, 40(2): 185-192.
[9]ADLER N, BRUDNER A, PROOST S. A review of transport market modeling using game-theoretic principles[J]. European Journal of Operational Research, 2021, 291(3): 808-829.
[10]CARRESE S, D’ANDREAGIOVANNI F, GIACCHETTI T, et al. An optimization model and genetic-based matheuristic for parking slot rent optimization to carsharing[J]. Research in Transportation Economics, 2021, 85: 100962.
[11]SHIN J H, JUN H B, KIM J G. Dynamic control of intelligent parking guidance using neural network predictive control[J]. Computers & Industrial Engineering, 2018, 120: 15-30.
[12]JONKERS E, VAN NOORT M, VAN DER VEEN J L. Parking guidance: Modelling, simulation and impact assessment[C/OL]//2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC). Washington, DC, USA, 2011. https://ieeexplore.ieee.org/document/6082804.
[13]VITAL F, IOANNOU P. Scheduling and shortest path for trucks with working hours and parking availability constraints[J]. Transportation Research Part B: Methodological, 2021, 148: 1-37.
[14]XIE M H, ZHANG X Y, WU Z H, et al. A shared parking optimization framework based on dynamic resource allocation and path planning[J]. Physica A: Statistical Mechanics and Its Applications, 2023, 616: 128649.
[15]ZHAO X, CHU W H, REN G, et al. Low-carbon school bus routing optimization model considering vehicle detour[J]. Journal of Southeast University (Natural Science Edition), 2024, 54(1): 192-199. (in Chinese)
[16]ZENG C, MA C X, WANG K, et al. Predicting vacant parking space availability: A DWT-Bi-LSTM model[J]. Physica A: Statistical Mechanics and Its Applications, 2022, 599: 127498.
[17]YUAN T, ZHAO X, LIU R, et al. Speed prediction model at urban intersections considering traffic participants[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(2): 326-333. (in Chinese)
[18]ZHANG X Y, ZHAO C, LIAO F X, et al. Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach[J]. Transportation Research Part C: Emerging Technologies, 2022, 138: 103624.
[19]LU E H, LIAO C H. Prediction-based parking allocation framework in urban environments[J]. International Journal of Geographical Information Science, 2020, 34(9): 1873-1901.
[20]CHEN T Q, GUESTRIN C, CHEN T Q, et al. XGBoost: A scalable tree boosting system [C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA, USA, 2016: 785-794.
[21]POPESCU M C, BALAS V E, PERESCU-POPESCU L, et al. Multilayer perceptron and neural networks[J]. WSEAS Transactions on Circuits and Systems, 2009, 8(7): 579-588.

Memo

Memo:

Received 2024-11-10,Revised 2025-01-30.
Biographies: Zhang Chu (1989—), female, doctor;Chen Jun (corresponding author), male, doctor, professor, chenjun@seu.edu.cn.
Foundation items:The National Natural Science Foundation of China (No.52302388), the Natural Science Foundation of Jiangsu Province (No. BK20230853).
Citation:ZHANG Chu,CHEN Kehan,CHEN Jun,et al.Mixed parking demand assignment in hub parking lots based on regression modeling[J].Journal of Southeast University (English Edition),2025,41(3):270-277.DOI:10.3969/j.issn.1003-7985.2025.03.002.DOI:10.3969/j.issn.1003-7985.2025.03.002

Common functions