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




Daniel Kondor received his MSc and PhD degrees in physics from the Eötvös Loránd University in Budapest, Hungary. Previously, he worked as a postdoctoral researcher at the Senseable City Lab at MIT in Cambridge, MA USA, before joining SMART FM as a postdoctoral associate. His research focused on social and economic networks during his PhD studies, working on data from Twitter and Bitcoin. During his time at SCL, he participated in several research projects focusing on urban human mobility and related topics, making use of cell phone, transportation and social media data.

Fundamental of C++ & HPC

Instructor: Dr. Daniel KONDOR

This intra-FM classes will focus on C++ and HPC [High Performance Computing]. The main theme of the C++ course would be the relatively new features of C++, using these and writing modern C++ with focus on scientific applications. This would require a basic knowledge of C++; if there is a demand, we could organize a separate, beginners' course. As we move on to the subject of HPC, I will teach about writing (simple) multi-threaded programs (using C++11 features, but also other languages / frameworks), and some overview on data parallelism (e.g. CUDA, OpenCL).




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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