Big Data & Planning for Smart Cities

  1. Events
  2. Big Data & Planning for Smart Cities

Events Search and Views Navigation

Event Views Navigation

Today

NYCDOT Smart Routing Hackathon

Hybrid Hybrid Event

Traffic accidents are among the leading causes of death for people aged 5–35 worldwide, causing transport externalities and thus unsustainability. At the same time, the introduction of almost fully connected and automated vehicles (CAVs) (levels 3–4 of SAE) is already a reality. Although CAVs go in the direction of smart mobility, their sustainability is still questionable because their deployment in open traffic introduces unexplored risks. The seminar will present how virtual reality and simulation are being used as a tool to replace naturalistic observations in the real-world and their pros and cons, in three different research projects on CAVs safety among which is a European Research Council Grant.

Optimal dispatching of electric vehicles for providing charging on- demand service leveraging charging-on-the-move technology

Virtual Event Virtual Event

Dr. Lili Du will discuss the EP fleet management problem, which is mathematically modeled as a vehicle routing problem (i.e., mE2-VRP), aiming to optimally dispatch the minimum number of EPs to approach and serve the EDs using different proportions of EV flows to save EDs’ travel time and mitigate traffic congestion to different extents in different network congestion and charging station coverage scenarios. She will also discuss suggestions for improving the service efficiency of CaaS + .

Learning from big and small data for transportation planning and resilience analysis

C2SMART Center Viz Lab 6 Metrotech Center, Room 460, Brooklyn
Virtual Event Hybrid Event

COVID has exacerbated two emerging trends in transportation analysis: (1) the rise of passively-generated big data; and (2) the increasing need to deal with the “unexpected” disruptions. This talk emphasizes the need for learning big and small data for transportation planning and resilience analysis. Different ways of learning are described, with applications ranging from long-term planning analysis to rapid responses under disruptions.

Deep Neural Networks for Choice Analysis

C2SMART Center Viz Lab 6 Metrotech Center, Room 460, Brooklyn
Virtual Event Hybrid Event

This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision.

Summer Webinar Series

Virtual Event Virtual Event

Presented by the Transportation Research Board (TRB) subcommittee AEP30(2) Route Choice and Spatio-Temporal Behavior Speaker: Professor Marcela A. Munizaga, Universidad de Chile.