Traffic Operations and Management

In 2017 and 2018, California was severely impacted by a number of devastating wildfires that required thousands of people to evacuate. These evacuations were hampered by poor communication, traffic congestion, and transportation and shelter resource deficiencies. To ensure that all citizens have both transportation and shelter in evacuations, agencies must consider alternative strategies for expanding resources, such as leveraging the sharing economy. To understand the possibility of leveraging the sharing economy to provide housing and transportation in an evacuation, researchers at the...

In an ideal world, all cars along a congested roadway would travel at the same constant average speed; however, this is hardly the case. As soon as one driver brakes, trailing cars must also brake to compensate, leading to “stop and go” traffic waves. This unnecessary braking and accelerating increases fuel consumption (and greenhouse gas emissions) by as much as 67 percent.1 Fortunately, automated vehicles (AVs) — even Level 2 AVs2 which are commercially available today — have the potential to mitigate this problem. By accelerating less than a human would, an AV with flow smoothing...

This paper extends an existing continuum multi-lane formulation for traffic flow, provides a discrete formulation for its numerical solution, and show initial results. The new formulation enables a natural treatment of boundary conditions such as merges, diverges, lane-drops and moving bottlenecks. The proposed model needs few extra parameters and is parsimonious. The look-ahead distance, for example, induces that non-local conditions affect the flow at any time-space point, causing smooth regime changes and fast waves. We find that as the look-ahead distance tends to zero, the solution...

This paper presents a simple approximate procedure for traffic analysis that can be described geometrically without calculus. The procedure, which is graphically intuitive, operates directly on piecewise linear approximations of the N-curves of cumulative vehicle count. Because the N-curves are both readily observable and of direct interest for evaluation purposes (e.g., they yield the total vehicle-hours and vehicle-miles of travel in a time interval, and the vehicular accumulation as a function of time) the predictions made with this method should be practical and easy to test.