Freight Forward – Modelling in the Here and Now

As business paradigms and supply chains evolve in an increasingly digital world, freight patterns and models are also changing.

Rick Donnelly, a global leader in strategic modelling and WSP Technical Director shares how government agencies in the US have benefited from adopting a big picture perspective. Here, he explores the impacts of big data and questions how industry proponents and modelling science will need to adapt to changing business needs, particularly as the rate of start-ups and emerging companies rises.


The Need for a Bigger Picture

Understanding the big picture and how our freight networks operate in an integrated way is an important part of ensuring that transport networks run efficiently, both at a national and local level. Currently in Australia there is no national flow model for freight, which makes it difficult to build a local model without a bigger picture context – every state has to recreate it themselves meaning significantly more time and effort is expended.


Mr Donnelly says, “In the US, the Federal Highway Administration assembled the resources necessary to create a national framework, which has really helped in our understanding of how the network operates. While it’s still a black box to most practitioners, it’s a good starting point.


“The other elements that are working well are big data approaches, which have given us access to things like GPS tracking data to better understand commercial vehicle origins and destinations. We now have the ability to assess the routes that freight vehicles took over the course of a month, from which we can distil truck tour and trip information used in transport planning. We have data on many of these trucks for months, and sometime years, giving insight into how patterns are changing over time.


Big Data to Plan for the Future

Now more than ever, we are living in a time of big data and comprehensive data sets. Big data helps us understand past and present patterns, from which we can extrapolate possible near-future scenarios. This provides policy makers with a greater level of confidence in our analyses, and ultimately in their decision making, as patterns in the data match real world patterns better than traditional transport planning models. But in a world that is increasingly driven by entrepreneurs and start-ups, how do we make this data relevant to their business and freight needs?


“Almost all the freight issues we are seeing now are short-term pains compared to the lifespans of infrastructure that we typically build,” explains Mr Donnelly. “Companies are really concerned with freight bottlenecks and what’s happening today. We’re talking about organisations whose long-range business plan is three years into the future, not the 20-30 year timespans considered by public agencies when assessing transport policies and infrastructure investments.


“Having a data-driven approach that talks about today is a lot more effective than talking about something that is strategically important many years into the future. While we deal with design, construction, and operation of transport and freight facilities, it’s important to remember that such infrastructure and services are just links in supply chains. Moreover, many major shippers are willing to accept sub-optimal transport costs in order to achieve a lower overall logistics cost, of which the former is only one part.


Another challenge is that their supply chains will change three times in the same period that it takes to build traditional models. How do we anticipate these trends? A good example can be seen in the distribution centres that are on the fringes of urban areas and near ports, which became quite popular to put into models a decade ago. Companies that were focused on same-day deliveries are now repurposing abandoned strip malls and existing retail stores as agile distribution centres to achieve that. The trend towards micro-manufacturing, 3D printing and quickly reconfigurable robotics will further change distribution patterns in the next few years.


The best success we’ve had in North America is to work with these companies to understand major industry and distribution trends and how they expect their businesses to respond," says Mr Donnelly. "They understand their patterns and the near future faster than we can keep up with traditional data gathering and strategic modelling. The result is far better information for public policymakers and the decisions Governments make, and better engagement of the freight community in our transport planning processes."


Rick Donnelly is recognised as a leader in travel modelling and simulation with more than 30 years of experience. Based in the US for the past ten years, he has led the design of state-wide travel forecasting models in Arizona, Maryland and North Carolina. His most recent focus has been on activity-based modelling of person and freight flows, along with agent-based simulation of defence logistics systems. Mr Donnelly is currently leading the design of a bilevel integrated economic-transport model for Ontario (Canada) as well as high speed rail forecasts for the Texas Department of Transportation.


A  co-chair of the Transportation Research Board’s Committee on Travel Forecasting Resources and a member of the Association for European Transport’s freight and logistics committee, Mr Donnelly has a thorough understanding of the global transport market.


To stay abreast of our latest news, publications, videos and posts, please follow us on LinkedIn.