Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

Yi Zhu; Shuyan Chen; Ying Wu; Fengxiang Qiao; Yongfeng Ma

doi:10.7307/ptt.v35i5.209

Authors

Yi Zhu Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University https://orcid.org/0000-0001-6539-2955
Shuyan Chen Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University
Ying Wu Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University
Fengxiang Qiao Innovative Transportation Research Institute, Texas Southern University
Yongfeng Ma Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University

DOI:

https://doi.org/10.7307/ptt.v35i5.209

Keywords:

shared parking, structural equation modelling, neural networks, parking behaviour

Abstract

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking.

References

Geng Y, Cassandras CG. New “Smart Parking” system based on resource allocation and reservations. IEEE Trans Intell Transport Syst. 2013;14(3):1129–1139. DOI: 10.1109/TITS.2013.2252428.

Shoup DC. Cruising for parking. Transport Policy. 2006;13(6):479–486. DOI: 10.1016/j.tranpol.2006.05.005.

Tsai M-T, Chu C-P. Evaluating parking reservation policy in urban areas: An environmental perspective. Transportation Research Part D: Transport and Environment. 2012;17(2):145–148. DOI: 10.1016/j.trd.2011.10.006.

Shao C, et al. A simple reservation and allocation model of shared parking lots. Transportation Research Part C: Emerging Technologies. 2016;71:303–312. DOI: 10.1016/j.trc.2016.08.010.

Xu SX, et al. Private parking slot sharing. Transportation Research Part B: Methodological. 2016;93:596–617. DOI: 10.1016/j.trb.2016.08.017.

Jiang B, Fan Z-P. Optimal allocation of shared parking slots considering parking unpunctuality under a platform-based management approach. Transportation Research Part E: Logistics and Transportation Review. 2020;142:102062. DOI: 10.1016/j.tre.2020.102062.

Kong XTR, et al. IoT-enabled parking space sharing and allocation mechanisms. IEEE Trans Automat Sci Eng. 2018;15(4):1654–1664. DOI: 10.1109/TASE.2017.2785241.

Duan M, et al. Bi-level programming model for resource-shared parking lots allocation. Transportation Letters. 2020;12(7):501–511. DOI: 10.1080/19427867.2019.1631596.

Zhang C, et al. Predicting owners’ willingness to share private residential parking spots. Transportation Research Record. 2018;2672(8):930–941. DOI: 10.1177/0361198118772947.

Lambe TA. Driver choice of parking in the city. Socio-Economic Planning Sciences. 1996;30(3):207–219. DOI: 10.1016/0038-0121(96)00008-0.

Hu J. Model of parking choice behavior in city. International Conference on Transportation Engineering 2009, Southwest Jiaotong University, Chengdu, China. American Society of Civil Engineers; 2009. p. 421–426. DOI: 10.1061/41039(345)70.

Bonsall P, Palmer I. Modelling drivers’ car parking behaviour using data from a travel choice simulator. Transportation Research Part C: Emerging Technologies. 2004;12(5):321–347. DOI: 10.1016/j.trc.2004.07.013.

He P, et al. Parking Choice Behavior for Shared Parking Based on Parking Purposes. AMM. 2015;743:439–444. DOI: 10.4028/www.scientific.net/AMM.743.439.

Gu Z, et al. Macroscopic parking dynamics modeling and optimal real-time pricing considering cruising-for-parking. Transportation Research Part C: Emerging Technologies. 2020;118:102714. DOI: 10.1016/j.trc.2020.102714.

Kotb AO, et al. iParker—A new smart car-parking system based on dynamic resource allocation and pricing. IEEE Trans Intell Transport Syst. 2016;17(9):2637–2647. DOI: 10.1109/TITS.2016.2531636.

Nourinejad M, Roorda MJ. Impact of hourly parking pricing on travel demand. Transportation Research Part A: Policy and Practice. 2017;98:28–45. DOI: 10.1016/j.tra.2017.01.023.

Tian Q, et al. Dynamic pricing for reservation-based parking system: A revenue management method. Transport Policy. 2018;71:36–44. DOI: 10.1016/j.tranpol.2018.07.007.

Wang J, et al. A hybrid management scheme with parking pricing and parking permit for a many-to-one park and ride network. Transportation Research Part C: Emerging Technologies. 2020;112:153–179. DOI: 10.1016/j.trc.2020.01.020.

Ibeas A, et al. Modelling parking choices considering user heterogeneity. Transportation Research Part A: Policy and Practice. 2014;70:41–49. DOI: 10.1016/j.tra.2014.10.001.

Bollen KA, Noble MD. Structural equation models and the quantification of behavior. Proc Natl Acad Sci USA. 2011;108:15639–15646. DOI: 10.1073/pnas.1010661108.

Kline RB. Principles and practice of structural equation modeling. 3rd ed. New York: Guilford Press; 2011. DOI: 10.15353/cgjsc.v1i1.3787.

Tarka P. An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Qual Quant. 2018;52:313–354. DOI: 10.1007/s11135-017-0469-8.