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.
Endor: First do-it-yourself predictive behavioral analytics platform Kebotix: Advanced materials and chemicals invented with AI speed Immunai: Reprogram Immunity Jeeva Wireless: Automating consumable product replenishment with real-time data JETCOOL: Cooling for today’s high power electronics Hosta Labs: Merging intelligence and infrastructure Meter: Intuitive inspection equipment for engineers and manufacturers OpenSpace: Your jobsite, fully captured. Just tap record and go Sourcemap: Technology to achieve 100% traceable, transparent supply chains iSee: AI for advanced autonomous yard truck solutions OnSpecta: Unique Virtualization Technology for Best Inference Hardware Performance AirWorks: Automatically turning aerial data into maps and engineering plans Leela AI: Breakthrough AI for Causal Video Understanding Volta Labs: Biological automation as agile and scalable as digital electronics
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.