The transportation sector is responsible for 28% of US greenhouse emissions, with a considerable amount being generated by medium- and heavy-duty trucks. Multiple strategies will be needed to improve efficiency and reduce carbon dioxide (CO2) emissions in the trucking industry. Researchers at the University of Southern California developed a truck routing model that minimizes fuel consumption and reduces emissions while explicitly accounting for parking availability and hours-of-service constraints. The researchers used the model to test various scenarios that reflect the practical constraints faced by drivers.

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The transportation sector is responsible for a significant part of the U.S.’s greenhouse emissions, with a considerable amount being generated by medium-and heavy-duty trucks. However, when it comes to the trucking industry, ‘green’ routing studies do not consider other critical practical factors, like working hours regulations and parking availability. Due to parking shortages, routes and schedules that do not account for parking availability may lead to last-minute changes that make them more polluting than expected. Similarly, working hours regulations influence the timing of required rest stops, which may force drivers to deviate from initially selected routes and schedules with negative consequences to fuel consumption and emissions.

This study addresses a variant of the shortest path and truck driver scheduling problem under parking availability constraints which focuses on optimizing fuel consumption and emissions by controlling the truck's travel speed and accounting for time-dependent traffic conditions. As it is impossible to be absolutely certain about the future parking availability of any location during planning, the case of stochastic parking availability was also studied. When studying the trade-offs between prioritizing emissions reduction or trip duration, it was found that although focusing on emissions reduction can increase trip duration significantly, this impact is greatly reduced when considering scenarios with limited parking availability. The problem formulation was further extended to model drivers’ possible recourse actions when unable to find parking and the ensuing costs. This formulation was used to study how the solutions are affected by the level of information provided to drivers. It was found that ignoring uncertainty in parking availability results in inconsistent performance even when restricting parking to periods when probability of finding parking is high. Furthermore, results might not reflect the intent of the cost function used, e.g., minimizing illegal parking events and/or the priority assigned to emissions reduction. Giving drivers full information about the probability of finding parking at any time/location significantly improves performance and reduces illegal parking-related risks, but also substantially increase problem complexity and computation time. Using full information regarding parking availability but restricting the parking times to high availability time-windows can reduce complexity while maintaining consistent, although reduced, performance.

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Due to hours-of-service (HOS) regulations, commercial drivers are required to stop and rest regularly, thus reducing fatigue-related crashes. Nevertheless, if the parking infrastructure cannot cope with the demand generated by these required stops, new issues arise. In particular, this is the case for long-haul trucking, which is the focus of this work. Drivers often have difficulty finding appropriate parking, leading to illegal parking, safety risks, and increased pollution and costs. In this project, the researchers consider the issue of coordinating the parking decisions of a large number of long-haul trucks. More specifically, how to model the behavior of a region’s driver population and how it could be influenced. Understanding how truck parking demand is affected by the interaction of individual drivers’ selfish planning behaviors (in the sense that they minimize their own costs, not the overall system cost) and how parking prices affect optimal schedules are important steps in developing a system able to balance demand. The study presents a formulation that uses a modified TDSP (Truck Driver Scheduling Problem) mixed-integer programming model which tracks parking usage by dividing time into time-slots and charging drivers per time slot used. Results show that if truck drivers are following optimal schedules, then parking prices would be effective in changing which locations and time slots would be chosen by each driver. However, price adjustments can cause demand to shift in unexpected and not always beneficial ways, likely due to HOS regulations and client constraints limiting the possible alternative schedules. Therefore, further study is required to better understand the system’s properties and how to avoid or dampen these oscillations. Furthermore, due to HOS rules and client constraints, it might be impossible to divert demand from specific time slots and locations sufficiently. Nevertheless, this model could still aid in identifying these spots and contribute to the evaluation of infrastructure investment needs.

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Truck parking has been identified as a major issue both in the USA and E.U. and has been selected by the American Transportation Research Institute as the most important research need for the trucking industry in 2015. The lack of appropriate and convenient parking locations has been the cause of several safety issues over the past years as drivers might be forced to either drive while tired and increase the risk of accidents or park illegally in unsafe locations, which might also pose a safety hazard to them and other drivers. Additionally, the parking shortage also impacts the shipment costs and the environment as the drivers might spend more fuel looking for parking or idling for power when parked in inappropriate locations.

This project’s objective was to study the truck parking problem, generate useful information and parking assist algorithms that could assist truck drivers in better planning their trips. By providing information about parking availability to truck drivers, the researchers expect to induce them to better distribute themselves among existing rest areas. This would decrease the peak demand in the most popular truck stops and attenuate the problems caused by the parking shortage.

In this project, several parking availability prediction algorithms were tested using data from a company’s private truck stops reservation system. The prediction MSE (mean squared error) and classification (full/available) sensitivity and specificity plots were evaluated for different experiments. It is shown that none of the tested algorithms is absolutely better than the others and has superior performance in all situations. The results presented show that a more efficient way would be to combine them and use the most appropriate one according to the situation. A model assignment according to current time of the day and target time for prediction is proposed based on the experiment data.

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In the last few decades, efforts to reduce emissions from heavy-duty diesel trucks (HDDTs) and their health impacts have been focused on imposing increasingly stringent emissions standards. This has led to significant advancements in emission control technologies and alternative fuel vehicle technologies. While these technologies are effective at reducing emissions from HDDTs, the turnover of the existing HDDT population to these advanced technologies would require a large amount of investment and along time. In the near term, other efforts to reduce emissions of the existing HDDTs and mitigate their impacts on communities are needed. Many studies have shown the promise of intelligent transportation systems (ITS) technologies in reducing the energy consumption and environmental footprint of people and goods movement through various means. This research is aimed at developing and evaluating eco-friendly ITS strategies for freight vehicles and traffic, with a focus on strategies that are applicable to the transportation systems in the South Coast Air Basin. Four specific strategies are examined in this research, including: 1) connected eco-driving, 2) truck eco-routing, 3) integrated traffic control, and 4) intelligent parking assist. This report describes the evaluation of each strategy, discusses results, and provide recommendations for future implementation.

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