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Preliminary Results using Quasi-Dynamic Traffic Assignment for the Los Angeles Region

Computation of Metropolitan-Scale, Quasi-Dynamic Traffic Assignment Models Using High Performance Computing

Connectivity enabled by telecommunications systems has introduced the opportunity to implement active control of vehicle routing across connected fleets. Static traffic assignment, which is used to estimate traffic states, does not have a notion of time dynamics and would not be able to represent these complex dynamics.

Chicago Transit Authority

CTA Transit Network Efficiency and the Changing Mobility Landscape

The introduction of new mobility options has changed travel behaviors in U.S. cities, resulting in major shocks to transit agencies’ core operating principles and bottom lines. These changes have resulted in some benefits, but have also put a strain on transit ridership, infrastructure, and ongoing efforts to curb emissions and energy use.

Geometric blue background

High Performance Computing for Mobility

Traffic planners often use some instantiation of a static traffic assignment problem to estimate traffic states in their cities. To accommodate changes in the demand profile over an entire day, the problem might be broken up into time slots of interest and static traffic assignment solutions are run for each slot.

Aerial city view of streets

Reinforcement Learning-Based Traffic Control to Optimize Energy Usage and Throughout

The US roadways are critical to meeting the mobility and economic needs of the nation. The United States uses 28% of its energy in moving goods and people, with approximately 60% of that used by cars, light trucks, and motorcycles. Thus, improved transportation efficiency is vital to America’s economic progress.