Parking, Perception, and Retail: Street-Level Determinants of Community Vitality in Harbin

TL;DR

This study uses AI-driven image analysis to explore how street features like parking, greenery, and width influence retail vitality and user satisfaction in Harbin, revealing nuanced effects of vehicle presence and spatial design.

Contribution

It introduces an interpretable, multimodal AI framework to quantify street perception and its impact on commercial vitality, advancing urban analysis methods.

Findings

Moderate vehicle presence can boost commercial access.

Excessive parking reduces walkability and satisfaction.

Greenery and cleanliness correlate with higher satisfaction.

Abstract

The commercial vitality of community-scale streets in Chinese cities is shaped by complex interactions between vehicular accessibility, environmental quality, and pedestrian perception. This study proposes an interpretable, image-based framework to examine how street-level features -- including parked vehicle density, greenery, cleanliness, and street width -- impact retail performance and user satisfaction in Harbin, China. Leveraging street view imagery and a multimodal large language model (VisualGLM-6B), we construct a Community Commercial Vitality Index (CCVI) from Meituan and Dianping data and analyze its relationship with spatial attributes extracted via GPT-4-based perception modeling. Our findings reveal that while moderate vehicle presence may enhance commercial access, excessive on-street parking -- especially in narrow streets -- erodes walkability and reduces both satisfaction and shop-level pricing. In contrast, streets with higher perceived greenery and cleanliness show significantly greater satisfaction scores but only weak associations with pricing. Street width moderates the effects of vehicle presence, underscoring the importance of spatial configuration. These results demonstrate the value of integrating AI-assisted perception with urban morphological analysis to capture non-linear and context-sensitive drivers of commercial success. This study advances both theoretical and methodological frontiers by highlighting the conditional role of vehicle activity in neighborhood commerce and demonstrating the feasibility of multimodal AI for perceptual urban diagnostics. The implications extend to urban design, parking management, and scalable planning tools for community revitalization.