New urban mobility
There is increasing potential to chart and design urban interactions as individuals leave clear digital traces of their everyday activities. Among a vast array of digital urban activity, people communicate via mobile phones and through online social networks, produce query logs in search engines, use swipecards for public transportation and smart energy metering systems, and more. These massive datasets of collective human activity, when properly analyzed, can provide unprecedented insights into the interactions between people, the physical spaces they traverse, and the urban services they depend on. The focus of this proposal is to address a key challenge of big data: uncovering the patterns within these vast data streams and applying them to the urgent societal issues of our time.
Collaboration between MIT Senseable City Lab and Enel Foundation has developed two projects, both of them focused on new visions of urban mobility in which new technologies based on real time data and autonomous driving (i.e. self driving vehicles) could change the functions of the urban spaces and the way people and goods move around in cities:
- A Networked-based Approach to Car/Taxi Sharing Analysis
- The impact of Autonomous Driving on transportation of people and goods in cities
In both projects the benefits related to a progressive shift from traditional to electric mobility are being analyzed, considering environmental, technological and economical aspects. In the last stage of the research, the effects of such urban mobility models will be investigated in a case study on the city of Santiago in Chile.
Quantifying the benefits of vehicle pooling (Source: pnas)
Using GPS Fleet Data to Assess Taxi Sharing (Source: igi-global)
Biennal International Workshop Advances in Energy Studies 2015, Stockholm 2015 (Source BIWAES) and BIWAES 2015 (Source: amazon)
Revisiting street intersections using Slot-based systems (Source: journals)
The Road Ahead: the future of Transportation and Mobility – MIT SenseAble City lab, Boston 2014 (Source: MIT)