New MIT research confirms people visit places more frequently when they have to travel shorter distances to get there. The study establishes a “visitation law” and could help in urban planning.
Credits: Image courtesy of the Singapore-MIT Alliance for Research and Technology.
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Researchers from the Low Energy Electronic Systems (LEES) interdisciplinary research group at the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, together with MIT and National University of Singapore (NUS), have discovered a new way to control light emission from materials.
Controlling the properties of materials has been the driving force behind many modern technologies from solar panels to computers, smart vehicles, and lifesaving hospital equipment. But materials properties have traditionally been adjusted based on their composition, structure, and sometimes size, and most practical devices that produce or generate light use layers of materials of different compositions that can often be difficult to grow.
Credit: SMART FM
E-scooters as a new micro-mobility service: SMART researchers explore the potential of e-scooter sharing as a replacement for short-distance transit in Singapore
SMART researchers found that e-scooters are not only a last-mile solution to complement transit services, but also provide a mobility service for short-distance transit trips
- Through a stated preference survey and mixed logit models, SMART researchers found that fare, transit transfer, and transit walking distance are significant factors driving the use of e-scooters as an alternative means of transit. The uncertainty is higher in predicting e-scooter usage preferences of male, young and high-income groups.
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Researchers at the Future Urban Mobility (FM) interdisciplinary research group atSingapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, have created a synthetic framework known as theory-based residual neural network (TB-ResNet), which combines discrete choice models (DCMs) and deep neural networks (DNNs), also known as deep learning, to improve individual decision-making analysis used in travel behavior research.
Transportation Research: Part B, SMART researchers explain their developed TB-ResNet framework and demonstrate the strength of combining the DCMs and DNNs methods, proving that they are highly complementary.
As machine learning is increasingly used in the field of transportation, the two disparate research concepts, DCMs and DNNs, have long been viewed as conflicting methods of research.
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IMAGE: Spatial distribution changes in PT supply during the competition: (left) Routes with supply decrease; (right) Routes with supply increase view more
Credit: Zhejing Cao and Baichuan Mo
Singapore, 5 May 2021 - The rapid advancement of Autonomous Vehicles (AV) technology in recent years has changed transport systems and consumer habits globally. As countries worldwide see a surge in AV usage, the rise of shared Autonomous Mobility on Demand (AMoD) service is likely to be next on the cards. Public Transit (PT), a critical component of urban transportation, will inevitably be impacted by the upcoming influx of AMoD and the question remains unanswered on whether AMoD would co-exist with or threaten the PT system.