City Dynamics

In making decisions about infrastructure development and resource allocation, city planners rely on models of how people move through their cities. Those models are largely based on surveys of residents’ travel habits, with an update rate ranging from months to decades, and typically cover only a tiny fraction of a city’s population. An ideal alternative approach would be to place a trackable GPS device on every car in the city, but such an approach would be infeasible (cost, ethics, privacy, etc.). So what would be the next best thing? There is an emerging literature on the use of cellphone location data to infer urban mobility patterns. This approach holds the promise of not only more accurate and timely data about urban mobility, but also the ability to quickly determine whether particular attempts to address cities’ transportation needs are working.

In this project we seek to use such data to understand the specific patterns of mobility behavior within the Kingdom of Saudi Arabia’s cities. The projects span many domains - from energy to mobility - in an attempt to understand and prove new methods for modeling the urban environment. 

Related and selected content:

• Alhasoun, F., Almaatouq, A., Greco, K., Campari, R., Alfaris, A. and Ratti, C., 2014. The City Browser: Utilizing Massive Call Data to Infer City Mobility Dynamics. In the Proceedings of the 3rd International Workshop on Urban Computing (UrbComp 2014). UrbComp: New York, NY.

• Almaatouq, A., Alhasoun, F., Campari, R. and Alfaris, A., 2013, October. The influence of social norms on synchronous versus asynchronous communication technologies. In Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia (pp. 39-42). 

• The Human Mobility and Networks Lab at MIT, which combines models from statistical physics and machine learning to understand cities, social networks, and mobility.