Just-in-time deliveries happen so fast compared to a decade or two ago that some might even call it magic. But the truth behind the magic is much more complex. It’s rooted in the highly intricate network of trains, trucks and ships that haul goods across entire oceans and over hundreds of miles of roadways and railroad tracks.
America’s freight systems are critical to the fast delivery of goods consumers and businesses have come to expect. At the same time, they disproportionately affect energy consumption, emissions and congestion, and thus closely intertwine with human mobility, health and economy.
Understanding freight movement and the impact new technologies have on it can benefit institutions tasked with managing and planning transportation infrastructure, businesses that rely on freight delivery to buy or sell, and the American economy overall.
To peel back its many layers, the U.S. Department of Energy’s Argonne National Laboratory is creating a new model of freight movement that will integrate with POLARIS, the laboratory’s powerful transportation system simulator. Combined with Argonne vehicle energy consumption tool Autonomie, POLARIS will enable users to evaluate the impact of freight transportation on energy use, emissions and mobility, and quantify changes driven by disruptive technologies.
Features of the tool
The novel model, which characterizes freight movement based on the decisions made by businesses, will extend current POLARIS capabilities.
POLARIS is agent-based, meaning it simulates mobility and traffic flow by predicting the individual behavior of “agents,” representing people, households, organizations, etc.
POLARIS already captures the passenger side of transportation by incorporating models for each of the decisions an agent makes. By integrating the freight model, users will be able to study commercial movement alongside passenger travel. Users will also benefit from the platform’s high speed and resolution to model last-minute decisions and trips made by agents.
“Integrating the freight model into the POLARIS platform will allow us to simulate the decisions that businesses and other agents make, and the impact of those decisions on traffic flow, energy use and mobility throughout an entire transportation network,” said Argonne computational transportation scientist Monique Stinson, the lead developer of the new freight model.
Push-Pull Supply Chains and the Logistics Response
The tool will have the advantage of being able to explicitly recognize the push-pull environment within different supply chains, Stinson says. In push environments, manufacturing is driven from the producer down to the retailer based on forecasts of demand; in pull environments it’s driven by information on consumer demand.
Push-pull dynamics are critical to flow of e-commerce and everything about modern trade today. Fluctuations or imbalances can create problems in supply chains, flooding or shorting markets and altering prices.
Argonne’s tool captures push-pull dynamics by establishing information links to represent which producers have information about demand and which don’t. These links make the tool unique and more robust compared to others, Stinson said.
Along with push pull dynamics, Argonne’s tool can generate a logistics system that supports a newly emerging form of demand – demand for last-mile just-in-time deliveries. Fueled by the rise of e-commerce and consumer data, this type of demand is reshaping supply chains at the wholesale level and driving up the number of distribution centers near urban consumers.
The simulation tool will also be able to richly quantify the interdependencies between commercial and passenger travel. Because passenger and freight agents are fully integrated in the POLARIS framework, users can use the integrated tool to evaluate research questions that relate to both passenger and commercial movement, like assessing how the use of delivery vehicles in e-commerce may affect how frequently passengers travel to stores.
“We’ll be able to explicitly model questions related to e-commerce and the shared economy, where we see passengers and businesses sharing access to goods and services,” Stinson said. “Understanding the interconnectedness of these systems can help us better understand their impacts.”
Applications of the tool
The tool is flexible enough to address a wide range of new research relevant to both private and public sectors. It is robust enough to model all the demands for deliveries within a given region, the volumes of deliveries being made and the different needs being covered. Original equipment manufacturers or parcel delivery companies, for example, can use that information to understand their markets and the kinds of vehicles most useful for serving them, and leverage that insight to optimize their own operations.
Understanding freight markets can also help city planners in their decision-making. If, for example, a logistics park is proposed for a metropolitan area like Chicago, planners could use Argonne’s tool and its experts to project how much tonnage would flow through the proposed location, the volume of traffic it would generate and the impacts of traffic on mobility, among other things.
Evaluating disruptive technologies
The simulation tool can be used to evaluate how e-commerce and disruptive technologies, such as connected and autonomous vehicles and truck platoons, may affect freight movement. The revolution of such technologies has drawn significant research interest over the last 10 or 15 years, Stinson said, and extracting knowledge about them can provide value to businesses investing in new technologies, city organizers looking to design for the future, and many others.
“At face value, it might seem that, because we have more efficient vehicles and delivery methods, everything has become more efficient and convenient. However new technologies can introduce inefficiencies elsewhere. For example, with faster delivery, we actually have more freight traffic which adds to congestion problems,” said Stinson. “This is why we need tools like ours – to get a more accurate picture of what’s going on now, and what may happen in the future.”
This work is being sponsored by the DOE Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. Oak Ridge National Laboratory contributed to this work by providing data input on last-mile deliveries.