Traffic Contraception is a system that loads location based data from various different cities to get meaningful insights on traffic modeling and management.
I take data handling from 2 vantage points. Visualization and Statistical modelling. Visualization is mandatory before performing machine learning operations or statistical modelling, and visualization help us familiarize with the data. After visualizing the data I modeled the data on statistical models. After modelling statistically the data was visualized on graphs for domain experts and city planners to analyze and make prudent changes to the traffic system.
Such changes will have potentials to optimize the traffic flow thereby giving a better commute to load-intensive highways. The app and the statistical models can be tried out from my github repo.
Additional Items: N/A
Developer(s): Ayoob Nazeem, Informatics Institute of Technology in Sri Lanka, Participant, Global IoT Datathon hosted by Terbine
Data Feeds Employed:
Open Source Tools:
Original Posting Date: 10 September 2020