Crowdsourced reviews reveal substantial disparities in public perceptions of parking

TL;DR

This study leverages crowdsourced online reviews and advanced NLP techniques to analyze public perceptions of parking across the U.S., revealing disparities linked to socio-spatial factors and urban characteristics.

Contribution

It introduces a novel, cost-effective approach using crowdsourced reviews and BERT-based sentiment analysis to measure parking perceptions at a large scale.

Findings

Significant variation in parking sentiment across POI types and regions.

Negative parking sentiment correlates with higher proportions of African Americans and Hispanics.

More parking supply does not necessarily lead to better parking experiences.

Abstract

Due to increased reliance on private vehicles and growing travel demand, parking remains a longstanding urban challenge globally. Quantifying parking perceptions is paramount as it enables decision-makers to identify problematic areas and make informed decisions on parking management. This study introduces a cost-effective and widely accessible data source, crowdsourced online reviews, to investigate public perceptions of parking across the U.S. Specifically, we examine 4,987,483 parking-related reviews for 1,129,460 points of interest (POIs) across 911 core-based statistical areas (CBSAs) sourced from Google Maps. We employ the Bidirectional Encoder Representations from Transformers (BERT) model to classify the parking sentiment and conduct regression analyses to explore its relationships with socio-spatial factors. Findings reveal significant variations in parking sentiment across POI types and CBSAs, with Restaurant POIs showing the most negative. Regression results further indicate that denser urban areas with higher proportions of African Americans and Hispanics and lower socioeconomic status are more likely to exhibit negative parking sentiment. Interestingly, an opposite relationship between parking supply and sentiment is observed, indicating increasing supply does not necessarily improve parking experiences. Finally, our textual analysis identifies keywords associated with positive or negative sentiments and highlights disparities between urban and rural areas. Overall, this study demonstrates the potential of a novel data source and methodological framework in measuring parking sentiment, offering valuable insights that help identify hyperlocal parking issues and guide targeted parking management strategies.