Pramath Joshi
ABSTRACT
This paper describes the development of a prototype website for traffic, parking and transport in a smart city. Machine Learning (ML) tools are applied to open datasets from the City of Melbourne, Australia to develop a set of Application Programming Interfaces (APIs) that provide useful information for the city's managers and citizens. The APIs accessed from this website enable users to query the ML models and obtain answers to questions such as: which parts of the city have the greatest pedestrian traffic, or the availability and cost of parking spots. The freeware tool RStudio was used for Big Data analytics while Machine Learning with Plumber was used to wrap the R code into APIs and Swagger to specify and document them. Postman and Swagger were used for testing while Docker was employed to package the APIs into standard containers for cloud deployment. The prototype website was developed using Wix and deployed on the Nectar cloud. The resulting website provides predictive models for COM traffic, parking, and transport and demonstrates the application of online smart city services for city planners and managers.
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