With the increased emphasis on sustainability, several logistics companies have purchased alternative fuel vehicles, like Compressed Natural Gas (CNG) trucks, to support their delivery operations. However, CNG trucks face several challenges on their way to replacing traditional diesel trucks such as limited mile range and small load capacity. Without effective deployment strategies for fueling stations it is not clear that these initiatives will result in net savings in emissions. Many of the local freight delivery operations are performed by independent truckers who are too small to manage their own fueling station, while the public fueling stations are limited. This research develops efficient deployment strategies for these carriers.

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International trade continues to grow, increasing container traffic at seaports worldwide. The Port of Los Angeles saw a 20% increase in throughput from 2010-2018. Typically, loaded containers arriving at the ports are transported by truck to inland importers, emptied, and then returned to the port. The empty containers are then driven to inland exporters to be loaded and returned to the port for shipping. This lack of direct container exchange between importers and exporters, known as the empty container reuse problem, adds many truck trips to Southern California highways and adds significant expenses for shipping companies. Trucks transporting empty containers contribute to traffic congestion, greenhouse gas emissions, and local air pollution.

Moving empty containers more efficiently could significantly reduce truck trips and the associated congestion and pollution. Researchers at the University of Southern California developed an optimization-based vehicle scheduling model that allows for a “street exchange” in which empty containers can go directly from importers to exporters without returning to the port. The model satisfies the current day’s known, or deterministic, demand while also accounting for the next day’s unpredictable, or stochastic, demand. The model is solved iteratively each day using this two-day time horizon to provide a routing plan for the current day’s demand. The model was tested using container demand data from the Ports of Los Angeles and Long Beach as well as randomly generated data sets. This research brief summarizes findings from that project.

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In major metropolitan areas such as Los Angeles County, ride-sharing systems can help reduce traffic congestion and increase the efficiency of the transportation system. This research project proposes three different solution approaches for solving the ride share routing problem with flexible pickup and drop-off points. The first is a dynamic programming-based route enumeration procedure that can be used to solve small-sized problems; the other two are branch and price-based heuristics for solving large problems. The researchers first provide a mixed integer nonlinear model for routing and pickup and drop-off points selection which they later decompose into a master and subproblem for solving. To validate the performance of their approaches and gather valuable insights about the ridesharing system, the researchers perform numerical experiments on a San Francisco Taxicab dataset. Results show that the approaches are efficient, solving instances with up to 300 nodes within 130 CPU seconds. For these datasets, incorporating flexible meeting points (i.e., pickup and drop-off points) can reduce the total travel time of the rideshare system by 18%. Sensitivity analysis shows that it can also decrease the time passengers wait time for rides by 43%. The methodologies in this study can help transportation planners design more efficient rideshare systems with less waiting, better passenger service, and less travel time.

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Traffic congestion is a significant problem in major metropolitan areas in the United States. According to the Urban Mobility Report, in 2019 commuters on average lost about 54 hours in traffic congestion. To combat this, major infrastructure projects have been undertaken. However, expansion projects cannot keep up with the increase in usage of personal vehicles and thus fail to address the traffic congestion problem. Carpool ridesharing has shown some promise in combatting this traffic congestion problem. In this system, the drivers are regular commuters who take detours to pick up and drop off passengers to decrease their transportation costs. This system increases the efficiency of the transportation system by providing flexible commutes to people, thus reducing the need for each commuter to use their own personal vehicle. The researchers developed three approaches to rideshare routing. The researchers conducted a computational study using a San Francisco taxicab dataset to determine the effectiveness of the three approaches. To show the impact of flexible meeting points, the researchers also conducted experimental simulations with and without walking and performed sensitivity analyses.

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California has a long history of reducing greenhouse gas (GHG) emissions, and has been working to accelerate the adoption of battery electric heavy-duty trucks (BEHDTs). Unlike diesel heavy-duty trucks (DHDTs), which have hundreds of miles of range per refill, BEHDTs have a restricted, load-dependent driving range, which makes charging planning an important role in the use of BEHDTs as an alternative to DHDTs. This research study investigates a mixed fleet drayage routing problem (MFDRP) with non-linear charging times. The study extends existing mixed fleet drayage routing models by considering multiple charging locations and allowing for more flexible routes for freight pickup and delivery. We formulate the MFDRP as a mixed integer programming model. After linearization and variable elimination, the model can be solved by commercial optimization solvers. However, the model becomes inefficient to solve when the problem size increases. Therefore, we develop a modified adaptive large neighborhood search algorithm, which can solve the problem with hundreds of units of demand in a few CPU minutes. Finally, we simulate one-day drayage operations with different BEHDT shares in the fleet for the years 2022, 2025, and 2030 to assess the potential for substituting DHDTs with BEHDTs. The numerical experiments indicate that employing BEHDTs as substitutes for DHDTs will increase the fleet size under the same level of demand. To reach the maximum share of BEHDTs in the truck fleet, the fleet size increases by 47.2%, 3.4%, and 3.4% in 2022, 2025, and 2030, respectively. Over 50% (90%) CO2 (NOx) emission reductions can be achieved by employing BEHDTs to the maximum share in the fleet.

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Ridesharing can help reduce traffic congestion, greenhouse gas emissions and increase accessibility to transportation in major metropolitan areas across the United States. A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties. Instead of assuming a probability distribution, the approach learns the underlying distribution in travel times and passenger cancellations from historical data. The authors first provide a mathematical model of the problem. Later they propose a stochastic average approximation approach for solving the routing and flexible pickup and drop-off selection problem. They also propose a Branch-and-Price heuristic and Adaptive Large Neighborhood Search-basedmetaheuristic to solve the underlying rideshare routing problem. To validate the approach, the authors construct test cases based on the New York City taxicab dataset. Numerical results show that the proposed branch and price-based solution approach can efficiently solve small instances while being close to the true optimum. On the other hand, the ALNS-based approach can solve medium to large instances with a small computational time budget while being robust to uncertainties. The proposed approach can help transportation officials and rideshare planners design more robust rideshare systems to alleviate traffic congestion in California.

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In the United States, the transportation sector  is the largest single source of greenhouse gas (GHG) and nitrogen oxide (NOx) emissions, and heavy-duty trucks contribute a disproportionately large share. Therefore, the trucking industry has been seeking ways to minimize emissions, such as adopting zero-emission vehicles and improving truck operating strategies to reduce truck miles. Battery-powered vehicles have different limitations than those with internal combustion engines. In this study, researchers from the University of Southern California investigated the adoption of battery electric heavy-duty trucks (BEHDTs) in the short-haul freight movement sector and the drayage industry. Drayage is a short-haul pickup and delivery service for transporting freight among ports, warehouses, and other facilities. With drayage routing, vehicles have limited weight and volume capacities and often make many stops. Routing involves optimizing for multiple factors, like fuel, distance traveled, and timeliness. This brief summarizes the findings from that research and provides implications for the field.

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In 2015, the Ports of Los Angeles and Long Beach moved 15.3 million twenty-foot Equivalent Units (TEU). There is a significant body of work on moving loaded containers efficiently, however there has been little research on the movement of empty containers. Out of the 15.3 million TEUs, about 30% or 4.3 million TEUs were empty containers. Empty container movement is increasing greatly because of the enormous inconvenience for companies to coordinate with each other to exchange empty containers. This problem is known as the Empty Container Problem. This study proposes a mathematical model that solves the empty container problem using double and single container trucks. The model discretizes time and ensures demand is met. By solving the empty container problem, congestion can be reduced since fewer truck trips would be needed to satisfy demand. Furthermore, since double container trucks can deliver two containers per truck trip, the quantity of trucks needed to satisfy the demand is decreased even more, further reducing congestion. The model was tested using data from the Ports of Los Angeles and Long Beach. The results are promising and show that the number of miles and trucks can be significantly reduced by increasing the number of street exchanges, and further reduced by using double container trucks. This report shows that using a single container policy instead of the current policy would reduce truck miles by about 12%, and would reduce significant truck trips to and from the port. The double container policy reduces truck miles by about 55% compared to the current policy, which is a noteworthy reduction. This could potentially reduce congestion substantially, lessening the impact of container freight movement on the environment.

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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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