Analyzing Parking Sentiment and its Relationship to Parking Supply and the Built Environment Using Online Reviews

Jiang, Zhiqiu; Mondschein, Andrew

doi:10.1007/s42421-021-00036-1

Original Paper
Published: 05 March 2021

Analyzing Parking Sentiment and its Relationship to Parking Supply and the Built Environment Using Online Reviews

Journal of Big Data Analytics in Transportation volume 3, pages 61–79 (2021)Cite this article

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Abstract

This study examines positive and negative sentiments associated with parking experiences reported in online Yelp reviews for four metropolitan areas in North America, leveraging large location-based social network (LBSN) data to understand parking sentiment as a measure of parking search or post-parking experiences. Demand from travelers and business owners for more parking is a significant issue for local transportation planners and decision-makers, but to date, there has been little study of how local parking management strategies or built environment characteristics modify parking experiences and sentiments. To understand this relationship, we first conduct a sentiment analysis (SA) to identify the emotional, affective content of parking-related reviews embedded in the Yelp reviews. We then use generalized mixed effects (GLME) models to examine the associations between parking sentiment and (a) parking management practices, and (b) characteristics of the built environment. The SA results show that positive and negative parking sentiments are significantly spatially clustered in each metropolitan area. GLME models show that sentiments are significantly associated with destination activity types, parking management strategies, and several built environment factors. The results of this study indicate how different interventions advocated by transportation policies may influence perceptions of parking in commercial and mixed-use districts with implications for overall support for neighborhood transportation planning best practice. Furthermore, the findings represent that emerging data mining and statistical methods can successfully leverage big data to reveal travel experiences and their relationship to urban contexts, providing an effective solution to obtaining experiential transportation information.

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Some or all data, models, or materials that support the findings of this study are available from the corresponding author upon request.

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Notes

In terms of the variety of Yelp business types, we calculated the percentage of each business category for all the businesses. The result shows that the percentages of Active Life, Arts, Automotive, Health, Hotels & Travel, Nightlife, Other, Restaurants, Service, and Shopping are 2.45%, 3.03%, 5.38%, 7.25%, 2.86%, 8.65%, 1.58%, 30.52%, 20.92%, 17.36%. “Restaurants” category is the category with the largest number of businesses (45.51%).
The percentage of each business category in all the reviews is: Active Life 1.36%, Art 2.33%s, Automotive 3.32%, Health 3.32%, Hotels & Travel 4.35%, Nightlife 20.68%, Other 0.49%, Restaurants 45.51%, Service 10.37%, and Shopping 8.27%.

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Department of Urban and Environmental Planning, University of Virginia, Charlottesville, VA, USA
Zhiqiu Jiang & Andrew Mondschein

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Zhiqiu Jiang
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The authors confirm contribution to the paper as follows: ZJ, ASM: study conception and design; ZJ, ASM: analysis and interpretation of results; ZJ, ASM: draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Zhiqiu Jiang.

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Table 6 Summary statistics of built environment variables

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Jiang, Z., Mondschein, A. Analyzing Parking Sentiment and its Relationship to Parking Supply and the Built Environment Using Online Reviews. J. Big Data Anal. Transp. 3, 61–79 (2021). https://doi.org/10.1007/s42421-021-00036-1

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Received: 17 September 2020
Revised: 26 January 2021
Accepted: 08 February 2021
Published: 05 March 2021
Issue Date: April 2021
DOI: https://doi.org/10.1007/s42421-021-00036-1

Keywords

Sentiment analysis
Parking
Non-work activity
Travel behavior
Built environment
Text mining