This project involves three main sub-projects:

1) Congestion Matrix & Spiral Visualization
Harvesting open source & cloud-based data and crowd-souring mobile applications proves the huge potential of capturing traffic patterns in congested mega cities, like Greater Cairo. On the left is a map showing the extent of data harvesting of roads in Cairo spanning more than 200 major roads across 2000+ km. On the right is a spiral visualization of traffic congestion data every 5 mins for one full year. This spatial and temporal BIG DATA enables identifying bottlenecks, seasonalities, trends, effect of events (accidents, games, rain, road work closure, service disruption, infrastructure failure / maintenance) on the overall traffic performance of the City.

2) Incident Matrix and Text Mining:
Unstructured data can be challenging to digest, but carry in itself valuable information - if properly mined and synthesized. Through text mining algorithms, Arabic-text harvested from crowd-sourcing mobile applications was used to quantify accidents, understand their context, location, and time of occurrence. An interactive visualization tool was then developed to identify most dangerous roads, frequency of accidents, as well as time of year, and day of week patterns.

3) SMART Decision Support Tool & Visualization
A multi-tier data collection effort on 4 dimensions: Safety, Mobility, Accessibility, and Reliability of Transport (SMART for short) was undertaken to harvest open-source and crowd-sourced data.  The variability of data sources reinforced the need for a unified scoring system for each dimension – as displayed on the map. To make more informed decisions, an interactive decision support tool was customized – via assigning a weight to each dimension - to enable policy sensitivity analysis.