Traffic congestion causes significant economic costs, wasted time, and public health risks. Ride-sharing, defined as a joint trip of more than two participants who share a vehicle that requires coordination among itineraries, has the potential to help mitigate congestion. A good ride-sharing system should provide quick response to passenger requests while identifying routes with minimum travel time. This is not an easy task, especially with dynamic passenger requests, variable request times, and cancelations of existing requests. One way to mitigate the effect of these uncertainties is to allow passengers to walk to a designated pick-up spot while waiting for the drivers, which can improve the system’s efficiency. Taking advantage of ride-sharing incentives such as High Occupancy Vehicle (HOV) lanes can also reduce travel time and make ride-sharing more appealing. Researchers at the University of Southern California developed a two-stage algorithm to solve the routing problem in real-time within a context where ride-sharing drivers are traveling toward their own destinations while making detours to serve passengers with flexible pickup and drop-off locations. The researchers also simulated operation of a ride-sharing system with and without HOV lanes and passenger meeting points, to determine the impact of these two factors on the operation of the system. This research brief summarizes the findings from that research and provides research implications.

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The research report explored the use of High Occupancy Vehicle (HOV) lanes and meeting points in a ride-sharing system where drivers have their own origin and destination. A two-stage heuristic algorithm is proposed, which consists of an insertion heuristic to solve the pickup and delivery problem (PDP) problem and a second stage algorithm that can solve the meeting points problem optimally in polynomial time. The experimental results show that the HOV lanes and meeting points can increase the efficiency of a dynamic ride-sharing system.

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This project establishes models to optimize the balance of freight demand across rail and truck modes. In real life situations, trains often travel at different speeds (i.e. passenger trains and freight trains share the same rail network). This incurs train delay whereby reducing the efficiency of the rail network. To provide a solution for this problem, we develop heuristic algorithms to improve conventional dispatching rules to reduce the average train delay. Then we build a control model and provide the solution procedure to adapt a dynamic headway concept inspired by new signaling technology like Positive Train Control (PTC). Rail network data of the Southern California region is collected to perform a detailed simulation analysis. The simulation results show significant improvement of network efficiency brought by our model and algorithms: as high as 21% reduction in average train delay with our best dispatching policy while with the dynamic headway control model, the average train delay is reduced by 40%. The railway network is therefore shown to have the potential to increase throughput capacity by 20%.

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Railway has always been an effective mode to transport both people and goods. Freight trains are about four times more fuel efficient than trucks and passenger trains and are popular because of their blend of efficiency, speed and low emissions. Increasing rail network capacity, however, can be difficult and expensive. Finding more efficient ways to utilize existing rail network capacity can mitigate the impacts of growing freight demand. 

New communication technologies, such as Positive Train Control (PTC), have the potential to improve efficiency and minimize delays in freight and passenger railway operations. PTC enables trains to communicate and share critical information such as speed and location with each other in real time. This research brief highlights findings from the project, "Integrated Management of Truck and Rail Systems in Los Angeles," which simulated the complex, busy freight and passenger rail corridor between downtown Los Angeles and Pomona to evaluate the effectiveness of proposed new scheduling and dispatching algorithms using PTC.

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The potential for zero emission heavy duty trucks (ZEHDTs) is examined via simulation modeling, case studies, interviews and a survey. Impacts of ZEHDTs on freight operations are assessed. Costs and benefits of using diesel, natural gas hybrid and battery electric vehicles are compared for 2020, 2025, 2030. ZE applications are limited in the near term due to range and charging limitations, but as ZE performance improves and prices go down, they are viable for a larger segment of the market. Hybrid vehicles are the most cost effective alternative for reducing air toxics, but ZEHDTs reduce air toxics the most by 2025. The report presents recommendations for promoting and increasing the market share of ZEHDTs and hybrids.

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California is pursuing multiple policies to spur the transition to a cleaner heavy-duty vehicle fleet. Achieving these targets will require massive change in the heavy-duty short-haul industry, and many unanswered questions remain. How do battery-electric heavy-duty trucks compare to diesel in everyday short-haul operations? How much would it cost to transition to a zero-emission truck fleet, and who would pay?

To begin to answer these questions, researchers at the National Center for Sustainable Transportation conducted a comprehensive analysis of the potential for zero-emission or near-zero-emission heavy-duty trucks to be used in short-haul drayage services. The researchers considered operational, economic, and environmental impacts through simulation modeling, interviews, case studies, stated preference surveys, and cost-effectiveness analyses.

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