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This Leading Edge webinar explores the dynamic relationship between AI and human behavior, uncovering how algorithms influence decision-making, shape societal norms, and even alter cognitive processes.
State-of-the-art information and communication technologies have become absolutely essential for all industries as the world is becoming more and more interconnected and data-driven. This trend has been further accelerated by the COVID pandemic. Where is the digital frontier today and what lies ahead? The annual MIT Information & Communication Technologies (ICT) event explores the latest research from across the Institute and its potential impact across industries. The webinar series will feature three sessions by six MIT faculty on the following topics: wireless communications, low power/edge computing and urban infrastructure. Additionally, a fourth session will feature MIT-connected startups presenting on the same topics.
Advances in materials science and engineering are key components of the innovation process. In this four-part series we highlight areas of materials research driving breakthroughs in technology.
City governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building and economic development strategies. Though walkability has received ample attention in planning literature, most practitioners still lack methods and tools for predicting how development proposals could impact pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments, but not pedestrian impact assessments. In this presentation I discuss a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and planned built environments. I demonstrate its application in Cambridge, MA and Melbourne, Australia, where I compare estimated foot-traffic during lunch and evening peak periods to observed pedestrian counts and show how the model can be used to predict changes in foot-traffic that results from changes in real-estate development.