Managing our lifelines: Using smarter transportation tools to move goods during a pandemic
April 21, 2020
April 21, 2020
Safeguarding communities by studying isolation factors, identifying secondary routes, and using data and analytics to ensure freight arrives in time
Crises have a way of putting mundane things we normally take for granted under a microscope. The COVID-19 pandemic has given birth to a hectic, unpredictable situation that has manifested itself in unexpected ways.
Case in point: the threat of diminishing access to goods led to a scarcity of toilet paper, and the closure of restaurants resulted in personal stockpiling and empty shelves at grocery stores.
In a wide-reaching pandemic, natural disaster, or other significant emergency that derails transportation systems, communities lose essential lifelines. As the world copes with COVID-19, the safe delivery of freight, food, and hospital supplies is imperative to keep people alive.
To ensure their resiliency through unexpected crises, communities need to examine and eliminate isolation factors—circumstances or influences that might limit access to the supply chain. In our current situation with COVID-19, necessary supplies simply don’t exist to meet the demand. Our former global supply chain became a single stream in the blink of an eye. While our normal global supply chains are being strained, communities need to take a hard look at where their necessities come from and how they are delivered. Resiliency takes planning. It’s critical that we continue to have a lifeline to the freight deliveries we need to support society’s basic and fundamental needs.
Data has an enormous role to play here, yet few communities are aware of the value of data as a tool in understanding their freight transportation systems. Using data to identify isolation factors, find alternate routes, and ensure flexibility in the transportation system can help get supplies to people when they’re needed.
Is there a single primary route that brings goods into your community? I've seen instances where large snowfalls impact highways over mountain passes, which affects the delivery of goods to certain areas. For example, I-80 crosses the United States from San Francisco to Teaneck, New Jersey. This single highway connects many US markets to the supply of goods that arrive via the Port of Oakland. Average snowfall at the peak of I-80 near Truckee, California, exceeds 200 inches per year. So, naturally, there are many winter days and evenings where trucks are held on one side of the mountain pass or the other until it is safe to cross. According to a national survey, bad weather accounts for 15% of highway bottlenecks nationwide. Communities can withstand delays in delivery of essential goods for a few days but not a long period of time.
Using data to analyze freight movement is both understudied and relatively inexpensive to do.
Similarly, if there’s a bridge on the one roadway that acts as a supply line, and that bridge fails, wide-ranging impacts are a certainty. When Ontario’s Nipigon River bridge failed in 2016—severing the Trans-Canada Highway and the connection between Manitoba and Ontario—all commercial traffic was rerouted through Minnesota and back around to Ontario.
It’s been estimated that 1,300 trucks carrying $100 million worth of goods take this route daily. A month and a half after the failure, the bridge opened to two lanes of traffic, with all four lanes available again two-and-a-half years later. For a long while, not only was a significant link in the network missing, the considerable congestion and delays to the truck traffic resulted in potentially significant impacts on air quality and the natural environment. Further, the additional costs of the freight delivered was passed on to the consumer.
Local and regional authorities need to take a holistic view to freight planning. While a single bridge or roadway may have the capacity to support routine truck volume, that one link is fragile and presents a risk to the entire network.
Years prior to the Nipigon River Bridge incident, members of our Toronto transportation team (before joining Stantec) conducted a multiyear study collecting freight data to model the movement of commercial goods for the Province of Ontario. That data enabled the province to quantify the economic impacts of the bridge closure.
What can we learn here? Here’s the first thing: plan for the unexpected. To maintain the health and safety of their residents, communities should begin preplanning alternative routes to maintain resilience for future worst-case scenarios. Stantec’s transportation work in the Lake Tahoe Basin highlighted the fragility of the roadway system to accommodate tens of millions of annual visitors and how the Basin communities could respond in a crisis, such as a wildfire. The potential for isolation is significant, and planning actions taken now will help communities build flexibility and reliability into their transportation networks.
Many of the principles we apply in planning for vehicles, transit, and active transportation can be tailored to the goods movement context. For instance, during the development of a transportation master plan in Orillia, a suburb of Toronto, Stantec practitioners identified isolation areas for active transportation, resulting in a recommendation for a new active transportation highway crossing. The same type of approach can be used for freight planning.
Here’s another lesson: Communities need to examine their isolation factors, if any, and whether they have different route options built into their transportation network. COVID-19 has taught us all the importance of freight reliability, and this should be forefront in the mindset of municipal agencies today. If agencies can reduce the stress of uncertainty related to freight delivery, then the fear of losing access to essential goods will be one less concern for community leaders.
Our teams have used location data from mobile devices for years to inform numerous transportation planning efforts on behalf of municipal agencies. Trucks transporting freight are equipped with smart devices for GPS tracking, and the trucks can be isolated from the general population of GPS tracking devices in several ways to map and visualize the transportation routes of all freight deliveries serving individual communities.
By geofencing an area, which is a virtual geographic boundary, software flags all mobile devices entering or leaving. We can track these deliveries by point of origin using a combination of US Census NAICS data (the North American standard for identifying goods), zoning maps, and assessment information to isolate trucking operations. This information, coupled with other tools, provides a high degree of certainty that the location-based data is freight specific. We then use this origin-destination information to explore other routes to develop alternative networks.
Most municipalities haven’t considered doing this, nor are they aware that you can. Using data to analyze freight movement is both understudied and relatively inexpensive to do. Freight has different needs and requirements than other modes of transportation and it’s necessary to quantify that difference to address community and business concerns. Freight is typically planned to enhance operational safety and efficiency. New technology is available to address the safe delivery of goods from large trucks to neighborhood destinations but only if the large trucks can get to the communities in the first place. In the event of a crisis, that is not a certainty.
Municipalities need to build flexibility into the movement of goods planning now more than ever. They need to be strategic about identifying and developing alternative freight delivery options to create community resiliency, ensure food security, and build public trust in leadership.
Make sure your community is as resilient as possible in the event of a natural disaster, unnatural disaster, or a future pandemic. It’s imperative to address the reliable movement of goods via freight now so that we may be swift in addressing issues at the worst of times.
This article was also published in Smart Cities Dive.