# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 1 Introduction  
The extent of road travel in the U.S. has substantially increased due to various disruptive effects. These include the impact of technology, the emergence of connected and automated vehicles (CAVs), social and demographic changes, urbanization and globalization, environmental and energy trends, economic and workforce changes, and political and fiscal trends. The increase in vehicle miles traveled, together with limited roadway infrastructure expansion, has led to severe traffic congestion. Traffic congestion creates significant economic losses and negative environmental impacts. As a result of congestion, according to [1], people in the U.S. traveled an extra 6.9 billion hours and purchased an additional 3.1 billion gallons of fuel, resulting in congestion costs of up to USD 160 billion. Thus, traffic congestion is a critical issue for traffic managers, decision makers, and road users. As reported in [2], by 2045, the U.S. population is anticipated to rise from the existing 321 million to 389 million, and the size of the U.S. economy is projected to double from its 2017 size. With the growth of the population and economic activity within the U.S., freight transportation has also grown significantly. In 2015, the U.S. transportation system carried a daily average of approximately 49.3 million tons of freight, with a worth of more than USD 52.5 billion. One study [3] estimated that the tonnage would increase at approximately 1.4% per year between 2015 and 2045. A strong economy and the evolution of time-sensitive-freight services have significantly increased the number of trucks on the nation's highways. According to [4], truck trips are predicted to increase from 557,000 per day in 2014 to over 1 million per day by 2040. These higher truck volumes have a significant effect on the level of congestion and air quality in many regions. Despite the availability of other transportation modes, trucks are the principal means for moving freight, and about 69% of the total tonnage shipped nationally is carried by truck [5]. According to [6], traffic congestion impacted the trucking industry and caused over 996 million hours of delay on the National Highway System, which is comparable to having 362,243 commercial truck drivers inactive for a whole year. This significant loss of productivity costs the trucking industry about USD 63 billion annually. Additionally, due to the differences in size and operation between trucks and cars, trucks have negative impacts on surrounding traffic and contribute to increases in crash severity, driver frustration, and vehicular emissions. Hence, new approaches are needed to improve the flow of traffic and accommodate heavy vehicles safely under incident-induced congestion. Generally, traffic congestion can be classified as recurrent or nonrecurrent congestion. Recurrent congestion is usually triggered by a high level of traffic demand that regularly exceeds the road capacity, whereas nonrecurrent congestion is a temporary interruption of the normal flow of traffic that triggers an unpredicted reduction in roadway capacity, which leads to more lengthy travel delays than usual. According to [7], nearly 60% of all delays are caused by nonrecurring congestion. One of the leading causes of nonrecurrent congestion is traffic incidents. Traffic incident management plays an essential role in effectively and efficiently mitigating the impacts of incident-induced congestion. The process of traffic incident management includes several main phases: detection and verification, response, clearance, and recovery. All stages of the incident management process are vital in reducing the magnitude of incident-induced congestion. Each phase features a set of techniques and requires organized decision-making strategies coordinated between the involved agencies. Highway agencies in the U.S. have implemented several truck rerouting strategies.