Work

Influence of Driver Factors on On-Street Parking Choice: Evidence from a Hybrid SP–RP Survey with Binary Logistic Analysis

Year: 2025

Type: article

Abstract: This study investigates the influence of driver-related factors on on-street parking choice by integrating stated preference (SP) and revealed preference (RP) survey methods. A hybrid SP–RP survey was designed to simulate realistic parking scenarios, and 423 valid questionnaires were collected online and offline. Key factors affecting parking choice were identified through descriptive analysis, including user acceptance of differentiated pricing and satisfaction with existing policies. The Kaiser–Meyer–Olkin (KMO = 0.904) and Bartlett’s test (p < 0.001) confirmed data suitability for factor analysis. A binary logistic regression model was developed to quantify variable effects under different travel purposes. Key findings include the following: monthly parking fee had the strongest effect (OR = 6.691, p = 0.010) on parking choice for shopping/entertainment trips; model prediction accuracy ranged from 80.87% to 83.56% across travel purposes; and goodness-of-fit metrics were strong (McFadden R2 = 0.630, Nagelkerke R2 = 0.772). The results provide empirical evidence on parking choice determinants and support the design of demand-responsive parking policies through dynamic and differentiated pricing strategies.

Source: Applied Sciences

DOI: 10.3390/app151910715

Authors: Wenxin Jiang, Xiaoqian Liu, Yining Ren, Yunyi Liang, Zhizhou Wu

Institutions: Xinjiang University, Tongji University, University of Shanghai for Science and Technology

Language: English

Topic: Smart Parking Systems Research

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896