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%.
References
Aggarwal CC, Zhai C (2012) A survey of text clustering algorithms. In: Aggarwal CC, Zhai C (eds) Mining text data. Springer, New York, pp 77–128. https://doi.org/10.1007/978-1-4614-3223-4_4
Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Arribas-Bel D, Bakens J (2019) Use and validation of location-based services in urban research: an example with Dutch restaurants. Urban Stud 56(5):868–884. https://doi.org/10.1177/0042098018779554
Bamberg S, Kühnel SM, Schmidt P (1999) The impact of general attitude on decisions. Ration Soc 11(1):5–25. https://doi.org/10.1177/104346399011001001
Bates D, Mächler M, Bolker B, Walker S (2014) Fitting linear mixed-effects models using lme4. ArXiv: 1406.5823 [Stat]
Bates D, Maechler M, Bolker B, Walker S, Christensen RHB, Singmann H, Dai B, Grothendieck G, Green P (2017) lme4: linear mixed-effects models using “Eigen” and S4. https://cran.r-project.org/web/packages/lme4/index.html
Börjesson M, Hamilton CJ, Näsman P, Papaix C (2015) Factors driving public support for road congestion reduction policies: congestion charging, free public transport and more roads in Stockholm, Helsinki and Lyon. Transp Res A Policy Pract 78:452–462. https://doi.org/10.1016/j.tra.2015.06.008
Bureau of Transportation Statistics (2018) Transportation statistics annual report. Bureau of Transportation Statistics. https://www.bts.dot.gov/sites/bts.dot.gov/files/docs/browse-statistical-products-and-data/transportation-statistics-annual-reports/Preliminary-TSAR-Full-2018-a.pdf
Caragea C, Squicciarini A, Stehle S, Neppalli K, Tapia AH (2014) Mapping moods: geo-mapped sentiment analysis during hurricane sandy. In: ISCRAM 2014 conference proceedings—11th international conference on information systems for crisis response and management, pp 642–651. https://pennstate.pure.elsevier.com/en/publications/mapping-moods-geo-mapped-sentiment-analysis-during-hurricane-sand
Charlotte Planning, Design, & Development Department (2017) City of Charlotte Zoning Ordinance
Chen C, Gong H, Lawson C, Bialostozky E (2010) Evaluating the feasibility of a passive travel survey collection in a complex urban environment: lessons learned from the New York City case study. Transp Res A Policy Pract 44(10):830–840. https://doi.org/10.1016/j.tra.2010.08.004
Christiansen P, Engebretsen Ø, Fearnley N, Usterud Hanssen J (2017) Parking facilities and the built environment: impacts on travel behaviour. Transp Res A Policy Pract 95:198–206. https://doi.org/10.1016/j.tra.2016.10.025
City of Toronto Council (2018) City of Toronto—zoning by-law
Clark County Department of Comprehensive Planning (2017) Comprehensive master plan for Clark County, Nevada
Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the international conference on web search and web data mining—WSDM ’08, p 231. https://doi.org/10.1145/1341531.1341561
Dittmar H, Ohland G (2012) The new transit town: best practices in transit-oriented development. Island Press, Washington D.C
ESRI (2018) How IDW works
Evans L, Saker M (2017) Location-based social media: space, time and identity. Springer, Cham
Ewing R, Cervero R (2001) Travel and the built environment: a synthesis. Transp Res Rec J Transp Res Board 1780(1):87–114. https://doi.org/10.3141/1780-10
Ewing R, Cervero R (2010) Travel and the built environment: a meta-analysis. J Am Plan Assoc 76(3):265–294. https://doi.org/10.1080/01944361003766766
Feinerer I (2018) Introduction to the “tm” Package Text Mining in R
Feuerriegel S, Proellochs N (2018) Package “SentimentAnalysis”
Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley Pub. Co., Boston
Forrest T, Pearson D (2005) Comparison of trip determination methods in household travel surveys enhanced by a global positioning system. Transp Res Rec J Transp Res Board 1917:63–71. https://doi.org/10.3141/1917-08
Griffioen-Young HJ, Janssen HJW, Van Amelsfoort DJC, Langefeld JJ (2004) The psychology of parking. In: Proceedings of the ECOMM 2004 conference
Hess DB (2012) Walking to the bus: perceived versus actual walking distance to bus stops for older adults. Transportation 39(2):247–266. https://doi.org/10.1007/s11116-011-9341-1
Inci E (2015) A review of the economics of parking. Econ Transp 4(1–2):50–63. https://doi.org/10.1016/j.ecotra.2014.11.001
INRIX (2017) The impact of parking pain in the US, UK and Germany. http://www2.inrix.com/research-parking-2017
Jiang Z, Mondschein A (2019) Examining the effects of proximity to rail transit on travel to non-work destinations: evidence from Yelp data for cities in North America and Europe. J Transp Land Use 12(1):303–326. https://doi.org/10.5198/jtlu.2019.1409
Kambele Z, Li G, Zhou Z (2015) Travelers’ information-seeking behaviors. J Travel Tour Mark 32(1–2):141–152. https://doi.org/10.1080/10548408.2014.986017
Kaplan J (2020) predictrace: predict the race of a given surname using census data (1.2.1) [Computer software]. https://CRAN.R-project.org/package=predictrace
Keadle SK, McKinnon R, Graubard BI, Troiano RP (2016) Prevalence and trends in physical activity among older adults in the United States: a comparison across three national surveys. Prev Med 89:37–43. https://doi.org/10.1016/j.ypmed.2016.05.009
Khanna K (2020) Wru: who are you? Bayesian prediction of racial category using surname and geolocation (0.1–10) [Computer software]. https://CRAN.R-project.org/package=wru
King D, Manville M, Shoup D (2007) The political calculus of congestion pricing. Transp Policy 14(2):111–123. https://doi.org/10.1016/j.tranpol.2006.11.002
Litman T (2006) Parking management best practices. American Planning Association, Chicago
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Manville M, Shoup D (2005) Parking, people, and cities. J Urban Plan Dev 131(4):233–245
Marsden G (2006) The evidence base for parking policies—a review. Transp Policy 13(6):447–457. https://doi.org/10.1016/j.tranpol.2006.05.009
Mcculloch C, Neuhaus J (2001) Generalized linear mixed models. Wiley, New York
McCulloch CE, Neuhaus JM (2005) Generalized linear mixed models. In: Armitage P, Colton T (eds) Encyclopedia of biostatistics. Wiley, New York, p b2a10021
Millard-Ball A, Weinberger RR, Hampshire RC (2014) Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transp Res A Policy Pract 63:76–92
Mitchell L, Frank MR, Harris KD, Dodds PS, Danforth CM (2013) The geography of happiness: connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE 8(5):e64417. https://doi.org/10.1371/journal.pone.0064417
Mjahed B, Lama AM, Archak E, Amr M, Hani S, Chen Y (2017) Exploring the role of social media platforms in informing trip planning. Transp Res Rec J Transp Res Board 2666:1–9. https://doi.org/10.3141/2666-01
Mondschein A (2015) Five-star transportation: using online activity reviews to examine mode choice to non-work destinations. Transportation 42(4):707–722. https://doi.org/10.1007/s11116-015-9600-7
Mondschein A, King DA, Hoehne C, Jiang Z, Chester M (2020a) Using social media to evaluate associations between parking supply and parking sentiment. Transp Res Interdiscip Perspect. https://doi.org/10.1016/j.trip.2019.100085
Mondschein A, King DA, Hoehne C, Jiang Z, Chester M (2020b) Using social media to evaluate associations between parking supply and parking sentiment. Transp Res Interdiscip Perspect 4:100085. https://doi.org/10.1016/j.trip.2019.100085
Mullen L (2020) gender: predict gender from names using historical data (R package version 0.5.4) [Computer software]. https://github.com/ropensci/gender
Ord JK, Getis A (2010) Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27(4):286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
Pandhe A, March A (2012) Parking availability influences on travel mode: Melbourne CBD offices. Aust Plan 49(2):161–171. https://doi.org/10.1080/07293682.2011.616177
Parkany E, Gallagher R, Viveiros P (2004) Are attitudes important in travel choice? Transp Res Rec J Transp Res Board 1894(1):127–139. https://doi.org/10.3141/1894-14
Phoenix City Council (2015) PlanPHX general plan update
Purifoye GY (2015) Nice-nastiness and other raced social interactions on public transport systems. City Community 14(3):286–310. https://doi.org/10.1111/cico.12116
Quantcast (2017) Yelp audience insights and demographic analytics. https://www.quantcast.com/yelp.com/demographics/WEB?country=US
Roberts H, Sadler J, Chapman L (2019) The value of Twitter data for determining the emotional responses of people to urban green spaces: a case study and critical evaluation. Urban Stud 56(4):818–835. https://doi.org/10.1177/0042098017748544
Rose MH (1990) Interstate: express highway politics, 1939–1989 (Rev. ed). University of Tennessee Press, Knoxville
Sarriera JM, Álvarez GE, Blynn K, Alesbury A, Scully T, Zhao J (2017) To share or not to share: investigating the social aspects of dynamic ridesharing. Transp Res Rec J Transp Res Board 2605(1):109–117. https://doi.org/10.3141/2605-11
Saxena A, Chaturvedi KR, Rakesh S (2018) Analysing customers reactions on social media promotional campaigns: a text-mining approach. Paradigm 22(1):80–99. https://doi.org/10.1177/0971890718759163
Schober P, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126(5):1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Sekar A, Chen RB, Cruzat A, Nagappan M (2017) Digital narratives of place: learning about neighborhood sense of place and travel through online responses. Transp Res Rec J Transp Res Board 2666(1):10–18. https://doi.org/10.3141/2666-02
Shoup DC (2006) Cruising for parking. Transp Policy 13(6):479–486. https://doi.org/10.1016/j.tranpol.2006.05.005
Shoup DC (2011) The high cost of free parking (updated). Planners Press, American Planning Association, Chicago
Shoup D (2018) Parking and the city. Routledge, New York
Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar A, Kang B (eds) AI 2006: advances in artificial intelligence, vol 4304. Springer, Berlin, pp 1015–1021. https://doi.org/10.1007/11941439_114
Statistics Canada (2016) Data products, 2016 census
Stevens MR (2017) Does compact development make people drive less? J Am Plan Assoc 83(1):7–18. https://doi.org/10.1080/01944363.2016.1240044
Stone PJ, Bales RF, Namenwirth JZ, Ogilvie DM (2007) The general inquirer: a computer system for content analysis and retrieval based on the sentence as a unit of information. Behav Sci 7(4):484–498. https://doi.org/10.1002/bs.3830070412
Stopher PR, Greaves SP (2007) Household travel surveys: where are we going? Transp Res A Policy Pract 41(5):367–381. https://doi.org/10.1016/j.tra.2006.09.005
Stopher P, Jiang Q, FitzGerald C (2005) Processing GPS data from travel surveys. Australasian Transport Research Forum (ATRF), 28th, 2005, Sydney, New South Wales, Australia, p 28
Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307. https://doi.org/10.1162/COLI_a_00049
Tung E (2015) Automatically categorizing Yelp businesses
United States Environmental Protection Agency (2014) Smart location database. https://edg.epa.gov/data/Public/OP/SLD/SmartLocationDB.zip
U.S. Census Bureau (2016) U.S. Census Bureau: American community survey, 2016 5-year estimates
Verplanken B, Aarts H (1999) Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity? Eur Rev Soc Psychol 10(1):101–134. https://doi.org/10.1080/14792779943000035
Weinberger R, Kaehny J, Rufo M (2010) US parking policies: an overview of management strategies. The TRIS and ITRD database
Wijayaratna S, Wijayaratna KP (2016) Quantifying the impact of on-street parking on road capacity: a case study of Sydney arterial roads. In: TRB 95th annual meeting compendium of papers
Yelp (2018a) API 2.0: all category list. Yelp for Developers
Yelp (2018b) Yelp dataset challenge
Yelp (2019) Yelp Factsheet. https://www.yelp.com/factsheet
Zhang X, Zhou Y, Ma Y, Chen BC, Zhang L, Agarwal D (2016) GLMix: generalized linear mixed models for large-scale response prediction. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 363–372. https://doi.org/10.1145/2939672.2939684
Zhou X, Wang M, Li D (2017) From stay to play—a travel planning tool based on crowdsourcing user-generated contents. Appl Geogr 78:1–11. https://doi.org/10.1016/j.apgeog.2016.10.002
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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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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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DOI: https://doi.org/10.1007/s42421-021-00036-1