Urban Data Analytics to Improve Mobility for Growing Cities in the Context of Mega Events
Urban Data Analytics to Improve Mobility for Growing Cities in the Context of Mega Events

Information technologies at the start of this project could inform each of us about the best alternatives for shortest paths from origins to destinations, but they could not contain incentives or alternatives that manage the information efficiently to get collective benefits. To obtain such benefits, one would need to have not only good estimates of how the traffic is formed but also to have target strategies to reduce enough vehicles from the best possible roads in a feasible way.

Moreover, to reach the target vehicle reduction is not trivial, it requires individual sacrifices such as some drivers taking alternative routes, shifts in departure times or even changes in modes of transportation. The opportunity is that during large events (Carnivals, Festivals, Sports events, etc.) the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for social good.

This project focused on understanding the impact of large-scale events and city growth to the traffic in the city and people’s commuting, and sequentially proposing reasonable and feasible travel demand management strategy to mitigate the traffic congestion in the future. This project took a fast growing city, Doha, Qatar as testbed. Traffic in Doha is notoriously bad and the population is growing very fast. Doha will host the FIFA World Cup in 2022, which will definitely attract a great number of tourists, and increase the pressure of the road network. To meet these challenges, this project used big data resources to understand the impact of World Cup and assist the policy maker with more reasonable planning strategy.

The project estimated the travel demand of local population using Bluetooth data and census data. The demand was assigned to the road network and the travel time of each trip estimated.

Principal Investigators
Sofiane Abbar (Social Computing, QCRI)
Marta González (HumNet Lab, MIT)