Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
Dynamic flow networks are a class of useful models for a variety of engineering systems including transportation systems, production lines and communication networks. This session will introduce its basic concepts, mathematical modeling and control strategies. The application will be illustrated with ramp metering, a typical strategy for freeway management.
During a public transit service disruption, passengers usually need path recommendations to find alternative routes. In this webinar, MIT PhD Candidate Baichuan Mo will discuss his proposal for a mixed-integer
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.
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 + .
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.
This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision.
In this talk, Professor Fosgerau presents results from application to large datasets (1,337,096 GPS traces of car trips, 280,000 GPS traces of bicycle trips) in Copenhagen.
Presented by: Joseph Chow, Associate Professor, NYU Haggai Davis, PHD Candidate, NYU Tuesday, June 13, 2023: 12:00pm - 1:00pm ET | Virtual
Presented by the Transportation Research Board (TRB) subcommittee AEP30(2) Route Choice and Spatio-Temporal Behavior Speaker: Professor Marcela A. Munizaga, Universidad de Chile.