Trucks

Distribution Strategies that Minimize Transportation and Inventory Costs

Burns, Lawrence D.
Hall, Randolph W.
Blumenfeld, Dennis E.
Carlos Daganzo
1985

This paper develops an analytic method for minimizing the cost of distributing freight by truck from a supplier to many customers. It derives formulas for transportation and inventory costs, and determines the optimal trade-off between these costs. The paper analyzes and compares two distribution strategies: direct shipping (i.e., shipping separate loads to each customer) and peddling (i.e., dispatching trucks that deliver items to more than one customer per load). The cost trade-off in each strategy depends on shipment size. Our results indicate that, for direct shipping, the optimal...

Distributionally Robust and Data-Driven Solutions to Commercial Vehicle Routing Problems

Keyantuo, Patrick
Wang, Ruiting
Zeng, Teng
Vishwanath, Aashrith
Borhan, Hoseinali
Scott Moura
2023

In this paper, we study the routing of commercial electric trucks through an application of distributionally robust optimization (DRO) for route planning and dispatch. This approach aims to minimize total cost of operation for the fleet, and considers the variability in energy consumption due to uncertain road conditions, traffic, weather and driving behavior. Furthermore, we augment the distributionally robust energy minimizing vehicle routing problem by learning the energy efficiency distribution over a horizon. We show that convergence to the true distribution is achieved while learning...

Truck Platooning Action on Asphalt Pavements: A case study

Leiva-Padilla, Paulina
Blanc, Juliette
Salgado, Aitor
Angel Mateos
Hammoum, Ferhat
Honrych, Pierre
2023

The reduced distances that can be achieved by truck platooning allow the reduction of drag resistance and therefore fuel consumption, gas emissions and costs for the transporters. However, this new way to configure trucks represents a new type of loading for pavement structures. This new type of loading is characterized by: (1) multiple load solicitations in a reduced period of time, (2) reduced inter-truck time gaps/distances, and (3) reduced lateral deviation of the vehicles forming the platoon, and therefore a more localized pavement damage. This paper investigates these effects and...

The Impact of Truck Platooning Action on Asphalt Pavement: A Parametric Study

Leiva-Padilla, Paulina
Blanc, Juliette
Hammoum, Ferhat
Salgado, Aitor
Chailleux, Emmanuel
Angel Mateos
Hornych, Pierre
2023

Partially/fully self-driven trucks in platoon configurations promise to increase transport efficiency, reduce fuel consumption/gas emissions and improve road safety through the use of connectivity technologies and automated driving support systems. However, truck platooning means the introduction of new types of loads on pavements which are characterised by: multiple loads, generated by the multi-axle configurations of the different trucks forming the platoon, traffic channelisation by the reduction of the lateral deviation of the trucks, and reduced inter-truck time gaps, which may reduce...

The Shake With Freight: The Impact of the Loma Prieta Earthquake On Bay Area Truckers

Mark Hansen
Sutter, Jacob
1990

The impacts of the Loma Prieta earthquake on Bay Area trucking firms, based on a combination of in-depth interviews and a random survey, are reported. The earthquake's primary impact on truckers derived from the closure of major roadway facilities, which necessitated circuitous routings and increased congestion on the facilities that remained open. The vast majority of truckers rated the impacts from increased congestion and circuitous routings as moderate or severe. Other impacts affecting substantial proportions of truckers involved communication and dispatching efficiency. The most...

Reducing Freight Greenhouse Gas Emissions in the California Corridor: The potential of short sea shipping

Zou, Bo
Smirti, Megan
Mark Hansen
2008

Greenhouse gases (GHG), the gases that cause climate change, are a major global concern. In the transportation sector, GHG reduction initiatives focus on passenger travel over goods movement, despite increasing freight demand and related emissions. California, with its recent GHG reduction legislation and large freight centers and corridors, provides a unique case study to evaluate the introduction of an alternative freight mode. Short sea shipping (SSS) offers a low GHG emission alternative to overland modes such as heavy-duty trucks. Analysis shows that this service is justifiable from a...

Back on Track? Reassessing Rail Transport for California's Perishable Produce

Seeherman, Joshua
Karen Trapenberg Frick
Caicedo, Juan
Mark Hansen
2018

Moving perishable produce by rail, rather than by truck, could provide significant benefits for Californians.

Encouraging Mode Shift from Truck to Rail for California Produce

Seeherman, Joshua
Caicedo, Juan
Jung, Jae Esther
Mark Hansen
2018

California is one of the largest producers of perishable produce in the world. This sector supports a large transportation industry that handles the exports of these goods. Starting from the 1950’s, the export of produce has gradually shifted modes from rail to truck. This project builds on the initial work from the “Rail and the California Economy” project by examining the potential of shifting the movement of perishable produce in California from truck to rail. The final report provides a review of the state of the California rail system in terms of perishable produce transport and where...

UAV Scheduling Strategies in Multi-modal Last-Mile Urban Parcel Delivery

Li, Ang
Mark Hansen
Zou, Bo
2023

Urban parcel delivery has emerged as a high growth market, and the resulting delivery traffic can pose great challenges in dense urban areas. There is growing interest in supplanting the conventional model of a dedicated delivery person operating a van to alternatives featuring new classes of vehicles such as drones, autonomous ground vehicles, cargo bikes and non-motorized vehicles. This work proposes combined delivery strategies using trucks, cargo bikes and drones. We develop and compare multi-modal delivery strategies with various mode combinations. We work on zone-based multi-modal...

Deep Truck : A Deep Neural Network Model for Longitudinal Dynamics of Heavy Duty Trucks

Albeaik, Saleh
Chou, Fang-Chieh
Lu, Xiao-Yun
Alexandre Bayen
2019

This article demonstrates the use of deep neural networks (NN) and deep reinforcement learning (deep-RL) for modeling and control of longitudinal heavy duty truck dynamics. Instead of explicit use of analytical model derived information or parameters about the truck, the deep NN model is fitted to data using a brief set of historical data collected from an arbitrary driving cycle. The deep model is used in this article to design a cruise controller for the truck using model-free deep-RL. The deep model and the control loop performances are demonstrated both using state-of-the-art...