Big data analytics for smart transportation (1)

Abstract

In this technologically advanced world, Big data has benefited multiple organizations to gain valuable insights into the different facets that help them make better decisions. In Singapore, urban mobility is important to achieve a balance between economic growth and a sustainable environment. Moving forward, Singapore is working towards faster car-lite transportation by 2040. Therefore, to support the vision of a car-lite Singapore, improving walk modes of transport can be done through the discovery of walkway hotspots using crowdsensing data. This project consists of a series of steps to discover new walkway areas. Firstly, data cleaning is done on the crowdsensing dataset. Secondly, uncharted walkway areas were estimated using an ellipse model. Thirdly, estimated walkway areas were further refined by using a weighting scheme known as the bivariate Gaussian model. Lastly, analyzed data were visualized on a visualization platform using the Django framework web application. As a result, this will provide informative walkway areas for the end-users.