Izvorni znanstveni članak
https://doi.org/10.7307/ptt.v35i5.209
Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour
Yi Zhu
; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University
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
Sažetak
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.
Ključne riječi
shared parking; structural equation modelling; neural networks; parking behaviour
Hrčak ID:
309460
URI
Datum izdavanja:
30.10.2023.
Posjeta: 430 *