SMART: Future Urban Mobility (FM)

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Assortment of our research

Featured: Research Paper

Title: Estimating savings in parking demand using shared vehicles for home-work commuting

Abstract: The increasing availability and adoption of shared vehicles as an alternative to personally-owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature.

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Intra-FM Training




Malika is a postdoctoral scholar with Autonomous Vehicle group at Singapore-MIT Alliance for Research and Technology (SMART). She was awarded SMART Scholarship in 2017 for her research proposal on autonomous fleet management using heterogeneous robots and path planning for self-driving cars. She received a PhD degree in Computer Science from McGill University, Canada in early 2017. In 2016, her work on "Multi-Target Rendezvous Search", was nominated as the finalist for the best paper award at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). A start-up proposal based on her work, titled, “Multi-Agent Rendezvous on street networks”, won her the NSERC Canadian Field Robotics Network, Strategic Network Enhancement Initiative Award in 2015.

Robot Operating System - A hands on experience

Instructor: Dr. Malika Meghjani

In this tutorial, I provided an overview of the Robot Operating System (ROS), an open-source robotics middleware. ROS is a widely used software framework for integrating heterogeneous sensors and hardware through low-level device control, message passing between processes and package management. The fundamentals of ROS were discussed through understanding of debugging and command-line tools along with catkin build and launch systems. Each of the basic concepts of ROS, such as topics, nodes and messages, was learned through simultaneous programming with the participants. A hands on interfacing experience was also provided to the participants with a small scale autonomous vehicle.




Shashwat VERMA was an intern with Singapore-MIT Alliance for Research & Technology Centre. He is currently a Software Engineer working on the perception in autonomous vehicles under the Autonomous Mobility on Demand project.

Deep Learning

Instructor: Shashwat VERMA

In this course, the fundamental elements of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus is on the application of these algorithms and explore other alternative use cases.


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Events & Seminars

Lunch Seminar May 15
    Modeling of urban mobility to solve environmental and...
    Abstract: Heat waves and heavy traffic have being increasingly become severe problems for people living in urban areas especially in mega cities such as Hong Kong and Singapore.
Lunch Seminar June 12
    Transportation Network Flow Game: Proactive Planning...
    Abstract: The increasing number of automated devices associated with intersection management (e.g., traffic light controllers) in urban transportation...

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#01-13 Enterprise Wing, Singapore: 138602