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.Read more
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.
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.
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.
FM has won the intra-CREATE seed collaboration grant entitled "A Data-Driven Optimization Approach to Improve the Resilience of the Singapore Mass Rapid Transit Network”.
FM's Postdoctoral Associate and Research Scientist have received the respective awards: Best Paper Award & Best Simulation Application Paper Award from the Transportation Research Board 97th Annual Meeting.
Washington, D.C. (USA)