Research Projects
Updated: Apr 2016

Research Projects
Lead Investigator: Christopher ZEGRAS (MIT)
The FM IRG will focus on the following three cross-cutting, and inter-dependent research “themes”, which further develop the seminal work done in the first phase of the project both in terms of scope and depth:

Theme 1: People and Urban Mobility will seek to understand what drives and shapes the demand for mobility both by individuals and by businesses, and how the availability of transportation affects their life choices such as housing, work, and educational or recreational options.

MIT Investigator(s): Moshe BEN-AKIVA (LEAD), Joseph FERREIRA, Christopher ZEGRAS, Li-Shiuan PEH
Develop and integrate state-of-the-art behavioral models with simulation tools to predict the impact of different mobility portfolios, including flexible mobility on demand services and autonomous mobility, on travel demand and activities, both for passengers and freight, and on transportation networks and land-use.

LIVE Singapore! 2.0

MIT Investigator(s): Carlo RATTI (LEAD)
A platform for the collection, analyzing, distribution and visualization of urban mobility data from different sources in Singapore that can serve as the active application of a semantic web platform to the management of the city, and form the basis for crowd sourced open application development.

Future Mobility Sensing (FMS)
MIT Investigator(s): Christopher ZEGRAS (LEAD), Moshe BEN-AKIVA
Development of a next generation individualized mobility sensing system that leverages advanced mobile technologies and machine learning techniques to capture high resolution, multi-day human behaviour and vehicular and freight movements as well as related preferences and satisfaction information.

Theme 2: Real-Time Traffic Estimation and Control will enable better exploitation of real-time information that could help traffic operators to increase network-wide capacity and reduce travel times, and help travelers in making better decisions.

Infrastructure-Less ITS with Next-Generation Devices
MIT Investigator(s): Li-Shiuan PEH (LEAD)
Most ITS systems today require the deployment of costly physical roadside infrastructure such as gantries, traffic signals, signs, and sensors embedded within the fixed transportation infrastructure. As a result, deployment and maintenance of ITS systems remains highly costly, and tends to be limited to selected regions rather than island-wide. Next-generation devices will comprise sufficient computing, networking, sensing hardware to enable the realization of truly infrastructure-less ITS, realized entirely with on-board or mobile devices/wearables.

DynaMIT 2.0
MIT Investigator(s): Moshe BEN-AKIVA (LEAD)
Develop a multi-modal network state estimation and prediction system that utilizes heterogeneous real-time data from a variety of sources to assess the impact of congestion-causing planned and unplanned events and optimize interventions/network management strategies to facilitate the real-time deployment of measures to mitigate congestion.

Data-Driven, Traffic-Aware, Real-Time Routing
MIT Investigator(s): Patrick JAILLET (LEAD)
Algorithms that use real-time data from many heterogeneous sources in order to (i) track and predict paths in dynamic transportation networks, and (ii) provide on-demand route guidance under uncertainty, based on a combination of optimization, data-fusion, machine learning, and novel behavioral techniques.

Next-Generation Traffic Control
MIT Investigator(s): Emilio FRAZZOLI (LEAD)
Develop an optimal vehicle traffic management system and the corresponding control mechanism so that the transportation infrastructure can support the maximum amount of traffic with minimal traffic congestion throughout the system. Such a system is likely to include mechanisms for traffic scheduling, routing, and flow control.

Theme 3: Demand-Responsive Mobility will develop new approaches and new algorithms to ensure that supply and demand for mobility are matched to the maximum extent.

Mobility on Demand
MIT Investigator(s): Daniela RUS (LEAD), Sertac Karaman
Develop models and algorithms to configure dynamically portions of the public transportation service network to meet mobility demands in real- time; the objective is to provide passenger-centric, timely service while minimizing costs and maximizing system efficiency.

Mobility Management
MIT Investigator(s): Jinhua ZHAO (LEAD)
Envision a future scenario for Singapore in which the urban mobility service provided as a public utility that combines public transit, walking and bicycling, and autonomous vehicles.