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Special Effects

Research Projects - FM

Lead Investigator: Christopher ZEGRAS (MIT)

The FM IRG will focus on the following three cross-cutting, and inter-dependent research, which further develop the seminal work done in the first phase of the project both in terms of scope and depth:

Pillar 1: Modeling, Simulation and Assessment

will concentrate on the development of models for simulating and assessing the short- and long-term effectiveness of the new concepts and approaches for urban mobility. At the core of this pillar is the development of a uniquely sophisticated and comprehensive simulation suite, called SimMobility, which will serve as a common virtual testbed connecting all efforts in this project.

SimMobility Pax

MIT Investigator(s): Moshe BEN-AKIVA, Joseph FERREIRA, Christopher ZEGRAS

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.

SimMobility Freight​​

MIT Investigator(s): Moshe BEN-AKIVA, Joseph FERREIRA, Christopher ZEGRAS

Develop agent-based models for the movement of goods and materials in the urban environment. Commodities will be traced through their entire life cycle; from its production, its distribution through various channels and its consumption by an end-consumer, to its final disposal or recycling. All relevant transport and logistics choices will be simulated using behavioral models, estimated based on innovative data collection methods.

DynaMIT 2.0

MIT Investigator(s): Moshe BEN-AKIVA


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.

Next-generation Traffic Control

MIT Investigator(s): Emilio FRAZZOLI

Develop an optimal vehicle traffic management system and the corresponding autonomous 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.

Mobility Management

MIT Investigator(s): Jinhua ZHAO

Combine behavioral science and transportation technology to envision a future urban mobility system for Singapore that combines public transit, walking and bicycling, shared mobility and autonomous vehicles.

Pillar 2: Control, Optimization and Planning

will aim at the development of new, scalable, robust, and efficient real-time algorithms long-term policies to improve the operations of transportation systems in a multi-modal, city-wide setting. Efficiency, robustness to disruptions, customer satisfaction will be key drivers in this pillar, as well as sustainability, environmental impact, and flexibility to technological, social, and geopolitical changes.


MIT Investigator(s): Christopher ZEGRAS

Create a standalone platform, including surveyor and tracker capabilities, project builder, credential system, data visualization and survey monitoring capability; continue to promote deployment of the technology in a range of settings; finalize the analysis of its capability for walk auditing in Singapore; develop new collaborative uses in Singapore; seek spin-off opportunities

Data-Driven Traffic Modeling and On-Demand Route Guidance under Uncertainty

MIT Investigator(s): Patrick JAILLET

Develop novel algorithms using data from many heterogeneous sources in order to (i) track and predict paths in dynamic transportation networks, (ii) provide on-demand route guidance under uncertainty, and (iii) address operational fleet management strategies (taxi fleet, location and routing of emergency vehicles), based on a combination of optimization, data-fusion, machine learning, and novel behavioral techniques.

Mobility on Demand

MIT Investigator(s): Emilio FRAZZOLI, Daniela RUS

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.

Pillar 3: Devices and Systems

will seek to develop novel technologies, including hardware devices, with embedded software, and integrated systems — such as autonomous, self-driving vehicles that would enable dramatically new approaches to urban mobility.

Technologies of Autonomy

MIT Investigator(s): Daniela RUS, Sertac KARAMAN


Assess and demonstrate the role of autonomy in mobility-on-demand and its impact in terms of feasibility, safety, and efficiency through modeling and simulation, algorithm development and experimental demonstration (“Autonomy for Mobility-on-Demand”).

LIVE Singapore! 2.0

MIT Investigator(s): Carlo RATTI

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): Moshe BEN-AKIVA, Christopher ZEGRAS

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 and related preferences and satisfaction information; and, to provide a platform for providing feedback to users for behavior modification to, e.g., reduce energy use and environmental impacts.

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