Research Report

This research assessed the changes in Metropolitan Atlanta Rapid Transit Authority (MARTA) service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files.

This study proposes a mathematical model that solves the empty container problem using double and single container trucks.

n this work, the authors developed a scheduling assignment for loaded and empty containers that builds on earlier models but incorporates stochastic (random) future demand. This report shows that the truck miles needed to satisfy the demand at all locations is reduced by about 4-7% when considering future stochastic demand as opposed to only considering today’s demand.

With rapid population growth and urban development, traffic congestion has become an inescapable issue, especially in large cities. Many congestion reduction strategies have been proposed in the past, ranging from roadway extension to transportation demand management programs. In particular, congestion pricing schemes have been used as negative reinforcements for traffic control. This project studies a different approach of offering positive incentives to drivers to take alternative routes.

This paper examines mechanisms for autonomous vehicle-induced increases in vehicle miles traveled (VMT) – some of which are expected to have a disproportionately larger impact on rural VMT – and outlines five methods that have been used to study VMT changes: travel demand equalization; travel demand elasticity; travel demand models; and stated and revealed preference surveys.

Some field studies have observed reductions in travel time by coordinating freeway ramps with adjacent arterial signals. To advance the investigation, the researchers propose an integrated traffic management strategy that involves variable speed limit, lane change, ramp metering for freeway traffic flow control, and a traffic-responsive signal control scheme for adjacent traffic light intersections.

This study investigates the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. The authors formulated a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

This report presents a detailed analysis of vehicle manufacturing costs and market-level TCO accounting for technology uncertainties, behavioral heterogeneity, and key decision parameters of automakers. Using the estimates of the vehicle manufacturing costs, the cost of electrification of California’s LDV fleet is estimated to achieve the state’s net-zero emission goal by 2045.

In this project, the research team developed and tested a deep learning–based trajectory-planning algorithm for Eco-Approach and Departure.

This research studied the feasibility of the deployment of renewable hydrogen fueling for fuel cell electric vehicles and DC fast charging stations for plug-in electric vehicles at highway safety roadside rest areas and the integration of stations with the electricity grid in California.