Establishing a reproducible protocol for measuring index active extension strength

Matter-Parrat, Valérie; Díaz, J.J. Hidalgo; Collon, Sylvie; Botero, Santiago Salazar; Prunières, Guillaume; Ichihara, Satoshi; Facca, S.; Liverneaux, P.

doi:10.1177/1753193416662427

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Using this, Google launched a new feature for the Google Maps App across 25 US cities that offers predictions about parking difficulty close to users' destination. There also have been studies focused on detecting the availability of parking spaces either using instrument parking infrastructure with special sensors (Chatman and Manville 2014) as well as using crowd-sensing solutions (Chen, Santos-Neto, and Ripeanu 2012;Zheng, Rajasegarar, and Leckie 2015;Pflügler et al 2016;Bock, Attanasio, and Di Martino 2017), but both types of studies rely on the existence of predefined parking spaces or the development of mobile applications such as PhonePark, iPark, and UPDetector (Xu et al 2013; Yang, Fantini, and Jensen 2013; Ma, Wolfson, and Xu. 2014).…”

Section: Related Workmentioning

confidence: 99%

Predicting the spatiotemporal legality of on-street parking using open data and machine learning

Gao

Liang

et al. 2019

Annals of GIS

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Searching for a parking spot in metropolitan areas is a great challenge, especially in highly populated areas such as downtown districts and job centres. On-street parking is often a costeffective choice compared to parking facilities such as garages and parking lots. However, limited space and complex parking regulation rules make the search process of on-street legal parking very difficult. To this end, we propose a data-driven framework for understanding and predicting the spatiotemporal legality of on-street parking using the NYC parking tickets open data, points of interest (POI) data and human mobility data. Four popular types of spatial analysis units (i.e. point, street, census tract, and grid) are used to examine the effects of spatial scale in machine learning predictive models. The results show that random forest works the best with the minimum rootmean-square-error (RMSE) for predicting ticket counts and with the highest accuracy scores for spatiotemporal legality classification across all four spatial analysis scales. Moreover, several prominent categories of places such as those with retail stores, health-care services, accommodation and food services are positively associated with the number of parking violation tickets.

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