This page contains a short description of ongoing research activities, and includes links to some of the recent publications. Note that some of the projects started recently, or are primarily aimed at infrastructure/software development, and have not resulted in published work yet.

A list of the research projects follows:

 

 

LIVE Singapore!

A platform for the collection, fusion, distribution and visualization of real-time data from different sources in Singapore that can serve as the active application of a semantic web platform for the management of the city, and form the basis for crowd-sourced open application development.

  • S. Phithakkitnukoon and C. Ratti. Inferring Asymmetry of Inhabitant Flow using Call Detail Records. Journal of Advances in Information Technology, 2(2), pp. 1-11, 2011.
  • M. Schatzmann, R. E. Britter. Quality assurance and improvement of micro-scale meteorological models. International Journal of Environment and Pollution 44 1-4, pp. 139-146. 2011
  • P. Kumar, M. Ketzel, S. Vardoulakis and R. Britter. Dynamics and dispersion modeling of nanoparticles from road traffic in the urban atmospheric environment - a review. To appear in the Journal of Aerosol Science.
  • K. Kloeckl, O. Senn, C. Ratti. Enabling the real-time city: LIVE Singapore! Submitted to Journal of Urban Technology, 2011
  • F. Calabrese, Z. Smoreda, V. D. Blondel, and C. Ratti. "Interplay between telecommunications and face-to-face interactions - a study using mobile phone data." To appear in PLoS ONE.
  • X. Li , R. Britter, L. Norford, T. Koh, and D. Entekhabi. "Flow and pollutant transport in urban street canyons of different aspect ratios with ground heating." To appear in Boundary Layer Meteorology.
  • E. Velasco, M. Roth, R. Britter and L. Norford. "Review of Singapore's air quality and greenhouse gas emissions: current situation and future possibilities." Submitted to Atmospheric Environment.
  • M. Martino, R. Britter, C. Outram, C. Zacharias, A. Biderman and C. Ratti. Book chapter in 'Digital Urban Modelling and Simulation'. In Press.
  • B. Resch, R. Britter and C. Ratti. "Live Urbanism - Towards the Senseable Cities and Beyond." Book chapter in Pardalos, P. and Rassia, S. (Eds.) Sustainable Architectural Design: Impacts on Health, Springer, In Press.
  • K. Kloeckl, G. Di Lorenzo, O. Senn, and C. Ratti. "LIVE Singapore! - An urban platform for real-time data to program the city". Proceedings from Computers in Urban Planning and Urban Management 2011 Conference, Lake Louise, Canada.
  • E. Velasco , M. Roth, R. Britter and L. Norford, "Does Singapore have Clean Air?" Better Air Quality (BAQ)-2010 Clean Air Initiatives for Asian Cities, Singapore, 2011.

 

 

Real-time Control and Learning for Sustainable Transportation

This project focuses on the application of automatic control, machine learning, and algorithmic apporaches to the real-time control of dynamic, large-scale transportation networks. Initial work focused on the analysis of stability and robustness of traffic equilibria when the drivers' route choice decisions are influenced by information at multiple temporal and spatial scales, including responses to global information about the network, as well as instantaneous observations of the immediate driver's surroundings. We show that equilibria are not in general robust, leading to new research on mechanisms aimed at ensuring drivers collectively converge to robust equilibria. Other current directions of research include congestion-aware routing, mechanism design for dynamic pricing, and real-time control of traffic signals.

 

 

Real-Time Model System for Network Management and Emergency Responses

Develop an Integrated suite of models to estimate the impact of alternative interventions and support the real-time deployment of such interventions to mitigate urban mobility problems as they occur on a daily basis.

  • F. C. Pereira "Profiling Public Events from Mobility Data", 19th Triennial Conference of the International Federation of Operational Research Societies (IFORS2011). Melbourne, Australia, July 2011

Coordinated Allocation of Resources for Optimizing and Analyzing Urban Traffic

Develop, implement, and evaluate novel decentralized control algorithms for allocating resources in urban traffic. We focus on two types of problems: (1) deploying mobile communication relay nodes for guaranteed communication coverage; and (2) servicing tasks, such as deliveries or uploads. The goal of the first task is to ensure communication coverage in the system and of the second to optimize deliveries in multi-agent systems

 

 

Mobility on Demand: Dynamic, Demand-Responsive Transportation Services

Models and algorithms to configure dynamically portions of public or private transportation service networks to meet mobility demands in real-time; the objective is to provide passenger-centric, timely service while minimizing costs and maximizing system efficiency. Topics addressed are: (1) optimal combinations of local and express (limited stop) bus services; (2) the Last Mile Problem, i.e., transporting travelers between home or work and a preferred node of the public transportation system; and (3) controlling and dispatching taxicab fleets.

 

 

Real-time Paths Tracking/Predictions and On-Demand Route Guidance Under Uncertainty

Development of novel algorithms, using real-time data from many heterogeneous sources, for (i) tracking and predicting paths in dynamic transportation networks, and (ii) providing on-demand route guidance under uncertainty. Based on a combination of optimization, data-fusion, machine learning, and novel behavioral techniques, the aim is to develop rigorous data-driven algorithms and methodologies with provable properties, and practical implementations.

  • P. Varakantham, S-F Cheng, and T. D. Nguyen. "Decentralized decision support for an agent population in dynamic and uncertain domains." 10th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-11). Taipei, Taiwan, May 2011.
  • J. Dauwels, E. F. Loshani, and P. Jaillet. "Fast and accurate prediction of traffic flow using loop detectors and GPS sensors." 3rd International Conference on Social Informatics (SocInfo'11). Singapore, 6 - 8 October 2011
  • J. Dauwels, E. F. Loshani, and P. Jaillet: "Prediction of traffic flow and speed by support vector regression." 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, March 25-30, 2012.
  • M. Lin, and P. Jaillet. "Study of a new mechanism for traffic coordination and a time-dependent model of network congestion games." Working paper, MIT Operations Research Center, May 2011.

 

 

Autonomy in Mobility-on-Demand Systems

The purpose of this effort is to assess and demonstrate the role of autonomy in mobility-on-demand through modeling, algorithm development and experimental demonstration. Since the beginning of the project, we have instrumented a golf cart and demonstrated basic autonomous driving capabilities, using a minimal sensor package, demonstrating reliable navigation in a GPS-denied environment, and safe interaction with automotive and pedestrian traffic. In addition, we have developed algorithms for fleet control and rebalancing in mobility-on-demand applications.

  • Marco Pavone, Stephen Smith, Emilio Frazzoli, Daniela Rus: "Robotic Load Balancing for Mobility-on-Demand Systems," Int. J. of Robotics Research. To appear.
  • Z. J. Chong, B. Qin, T. Bandyopadhyay, T. Wongpiromsarn, E. S. Rankin, M. H. Ang Jr., E. Frazzoli, D. Rus, D. Hsu, K. H. Low: "Autonomous Personal Vehicle for the First- and Last-Mile Transportation Services," 5th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and 5th IEEE International Conference on Robotics, Automation and Mechatronics (RAM), Qingdao, China.
  • T. Wongpiromsarn, Sertac Karaman, Emilio Frazzoli: "Synthesis of Provably Correct Controllers for Autonomous Vehicles in Urban Environments," 14th International IEEE Annual Conference on Intelligent Transportation Systems, 2011.

 

 

Cooperative Perception for Situational Awareness and Vehicle Control

The objectieve of this project is the development of a sensor package enabling perception (navigation and situational awareness) for an autonomous vehicle, at a cost that is a fraction of that of current systems with similar capabilities; reliance on wireless communication with the fixed infrastructure, and with other vehicles. This work is strongly related to the previous project. Developed navigation techniques relying on off-board sensors, such as traffic cameras, to augment an autonomous vehicle's ability to detect pedestrians and other vehicles; and techniques for cooperative localization that improve on a vehicle's GPS position fix, using other vehicle's pseudo-range information.

  • Z. J. Chong, B. Qin, T. Bandyopadhyay, T. Wongpiromsarn, E. S. Rankin, M. H. Ang Jr., E. Frazzoli, D. Rus, D. Hsu, K. H. Low: "Autonomous Navigation in Crowded Campus Environment", IROS Workshop on Perception and Navigation for Autonomous Vehicles in Human Environment, 2011.

 

 

Phone-Driven Transportation

While phones have enormous computing power, they are rarely used for more than individual computing. Most transportation services rely on Internet network connectivity to servers, where heavy computing is needed. We investigate new programming models and middleware, along with novel applications, that can harness many phones as a collaborative computing platform for directly hosting transportation services.

 

 

Application-guided Network Design

Design fast, energy-efficient networking of mobile devices to facilitate direct or multi-hop phone-to-phone connectivity, leveraging application characteristics of transportation services. Two transportation application services are being developed: (1) Bus crowd estimation system, (2) Collaborative taxi dispatch and advisory system.

 

 

Scalable and Robust Mobile Video

Today's mobile video suffers from two limitations: 1) it cannot reduce bandwidth consumption by leveraging wireless broadcast to multicast popular content to interested receivers, and 2) it lacks robustness to wireless errors. This project develops SoftCast, a cross-layer design for mobile video that addresses both limitations. SoftCast enables a video source to multicast a single stream that each receiver decodes to a video quality commensurate with its channel quality.

  • Szymon Jakubcza and Dina Katabi, "A Cross-Layer Design for Scalable Mobile Video," ACM MOBICOM, 2011.

 

 

Zero-Effort Wireless Security

Trends in wireless security are driven by two phenomena: First, ordinary users often struggle with the security setup of their devices and, when faced with the task of password generation or verification, a significant percentage simply skips security activation. Second, there is a proliferation of wireless gadgets and sensors that do not support an interface for entering a password. This project aims to provide effortless wireless security mechanisms that do not require users to participate in password generation or verification. We leverage the properties of the wireless medium to automatically generate and exchange secret keys in a manner robust to a man-in-the-middle attack.

  • Shyamnath Gollakota, Nabeel Ahmad, Nickolai Zeldovich, and Dina Katabi, "Secure In-Band Wireless Pairing," USENIX Security Symposium, 2011.
    •

 

 

Integrated Simulation Platform: SimMobility

Integrate and link together various mobility-sensitive behavioral models with state-of-the-art simulators to predict impacts of mobility demands on transportation networks, services and vehicular emissions. Integration will make it possible to simulate the effects of a portfolio of technology, policy and investment options under alternative future scenarios.

A first-cut version of SimMobility Short Term module is running and will be demo-ed at the 2012 FM Symposium. Detailed architecture of the Mid-Term and Long-Term modules are being designed.The team aims for several novel contributions both in transportation simulation and software design. In simulation, it aims to be the first fully-integrated, agent-behavioral, multi-level transportation simulator, enabling explorations that cannot be previously done. In software design, it aims to be the first massively parallel transportation simulator that will leverage state-of-the-art many-core computers for high, scalable performance

 

 

Development and Testing of Network-Enabled Data Collection Techniques

The ubiquity of technologies related to Networked Computing and Control (NCC) provides a range of new close-to-real-time data for urban mobility planning and management. The challenge lies in capturing and deploying the relevant information, using it for real-time control, improved user service, and longer-term strategic planning. The objective here is to implement a broad data collection effort using smart phones matched with web-based surveying tools to infer (through machine learning) household and firm activities, including mobility and location choices. The smartphones data collection project is the first totally stand-alone large scale activity-based surveying system for travel behavior. LTA is planning to apply it to a 1000 respondents sample for their Household Interviews for Travel Survey to be run in July 2012 (HITS2012). It merges pervasive computing, machine learning, interaction design together with transport modeling to comprehensively capture activities, their locations and durations, routes and modes. The platform is currently under pilot testing.

  • F.C. Pereira, C. Cottrill, C. Zegras, M. Abou-Zeid, Y. Xiang, I. Dias, J. Santos, M. Ben-Akiva, J.A. Silva, "Integrated Transportation Activity-Travel Smartphone Survey". Proceedings from the 9th International Conference on Transport Survey Methods. Termas de Puyehue, Chile. 14-18 November 2011.
  • C. Cottrill "Location Privacy: Who Protects?", URISA Journal. Forthcoming.
  • F.C. Pereira, F. Rodrigues, M. Ben-Akiva, "Internet as Sensor: Case Study with Special Events". In Proceedings of the Transportation Research Board 91st Annual Meeting. Washington, D. C. 2012
  • V. Pattabhiraman, M. Ben-Akiva, and M. Abou-Zeid, "A needs-based utility maximizing model of activity location, duration, and frequency". In Proceedings of the 91st annual meeting of the Transportation Research Board, Washington, DC, January 2012.
  • A. Alves, F. Rodrigues, F.C. Pereira, "Tagging Space from Information Extraction and Popularity of Points of Interest". Proceedings of the International Joint Conference on Ambient Intelligence. Amsterdam, 16-18 November, 2011. Lecture Notes in Computer Science 7040 Springer 2011, ISBN 978-3-642-25166-5

 

 

Behavioral Models for Land Use, Mobility and Energy and Resource Uses

To plan sustainable future urban mobility systems, we need a set of forecasting tools to help make well-informed, consistent assessments of future conditions under various scenarios. Behavioral models are at the heart of the approach. The objective is to develop state-of-the-art models to understand and forecast different behavioral rationales of households and firms.

  • C. Andris, J. Ferreira, "Challenges in Measuring Social Distance," submitted to Environment and Planning B (2011).
  • F. Calabrese, M. Diao, G. DiLorenzo, J. Ferreira, C. Ratti , "Understanding individual Mobility Patterns with Urban Sensing Data: An Example of Mobile Phone Traces," submitted to Transportation Research Part C (2011).

 

 

Real-Time Regulation of Mobility Services

Cheap and abundant data streams generated by modern mobility systems hold the promise of changing regulatory and policy making frameworks to enable more efficient, effective, intermodal mobility services, with greater transparency and accountability. The potential uses of ITS automated data in regulators, transport operators, planners, and the public raise a number of questions related to: data access and ownership, new types of performance measures, contractual relationships, appropriate regulatory structures, and structuring incentives for mobility innovations.

  • C. Brakewood, F. Rojas, and C. Zegras, "Responding to Real-Time Information," 7th Conference on Virtual Cities and Territories, Lisbon, October, 2011
             

    All Rights Reservered . future urban mobility
©2012