McCormack, E. (2014). Developing Roadway Performance Measures Using Commercial GPS Data from Trucks. Institute of Transportation Engineers. ITE Journal, 84(6), 36-40.
The movement of a truck or vehicle that meet one or more of the following requirements: (1) vehicle weight, weight rating, gross combination weight, or gross combination weight rating exceeding 10,001 pounds, (2) designed and used to transport over eight passengers and is doing so for compensation, (3) designed and used to transport more than 15 passengers (including the driver), and is not receiving compensation, and (4) is used to transport hazardous materials.
This research offers a novel formulation for including emissions into fleet assignment and vehicle routing and for the trade-offs faced by fleet operators between cost, emissions, and service quality. This approach enables evaluation of the impact of a variety of internal changes (e.g. time window schemes) and external policies (e.g. spatial restrictions), and enables comparisons of the relative impacts on fleet emissions. To apply the above approach to real fleets, three different case studies were developed. Each of these cases has significant differences in their fleet composition, customers’ requirements, and operational features that provide this research with the opportunity to explore different scenarios.
The research includes estimations of the impact on cost and CO2 and NOX emissions from fleet upgrades, the impact on cost, emissions, and customer wait time when demand density or location changes, and the impact on cost, emissions, and customer wait time from congestion and time window flexibility. Additionally, it shows that any infrastructure use restriction increases cost and emissions. A discussion of the implications for policymakers and fleet operators in a variety of physical and transportation environments is also presented.
In many regions throughout the world, freight models are used to aid infrastructure investment and policy decisions. Since freight is such an integral part of efficient supply chains, more realistic transportation models can be of greater assistance. Transportation models in general have been moving away from the traditional four-step model into activity-based and supply chain-based models. Personal transportation models take into consideration household demographics and why families travel. Freight research has yet to fully identify the relationships between truck movements and company characteristics, so most freight models use the methodology of personal transportation models, despite situational differences.
In an effort to classify freight companies into groupings with differentiated travel movements, a survey of licensed motor carriers was designed and conducted in Oregon. The survey consisted of 33 questions. Respondents were asked about their vehicle fleets, locations served, times traveled, types of deliveries, and commodities. An analysis of the data revealed clusters of company types that can be distinguished by determining characteristics such as their role in a supply chain, facilities operated, commodity type, and vehicle types. An assessment of how the relationships found can be integrated into state models is also presented.
As commercial vehicle activity grows, the environmental impacts of these movements have increasing negative effects, particularly in urban areas. The transportation sector is the largest producer of CO2 emissions in the United States, by end-use sector, accounting for 32% of CO2 emissions from fossil fuel combustion in 2008. Medium and heavy-duty trucks account for close to 22% of CO2 emissions within the transportation sector, making systems using these vehicles key contributors to air quality problems. An important well-known type of such systems is the “pickup and delivery” in which a fleet of vehicles pickups and/or delivers goods from customers.
Companies operating fleet of vehicles reduce their cost by efficiently designing the routes their vehicles follow and the schedules at which customers will be visited. This principle especially applies to pickup and delivery systems. Customers are spread out in urban regions or are located in different states which makes it critical to efficiently design the routes and schedules vehicles will follow. So far, a less costly operation has been the main focus of these companies, particularly pickup and delivery systems, and less attention has been paid to understand how cost and emissions relate and how to directly reduce the environmental impacts of their transportation activities. This is the research opportunity that motivates the present study.
While emissions from transportation activities are mostly understood broadly, this research looks carefully at relationships between cost, emissions and service quality at an individual-fleet level. This approach enables evaluation of the impact of a variety of internal changes and external policies based on different time window schemes, exposure to congestion, or impact of CO2 taxation. It this makes it possible to obtain particular and valuable insights from the changes in the relationship between cost, emissions and service quality for different fleet characteristics.
In an effort to apply the above approach to real fleets, two different case studies are approached and presented in this thesis. Each of these cases has significant differences in their fleet composition, customers’ requirements and operational features that provide this research with the opportunity to explore different scenarios.
Three research questions guide this research. They are explained in more detailed below. The present study does not seek to provide a conclusive answer for each of the research questions but does shed light on general insights and relationships for each of the different features presented in the road network, fleet composition, and customer features.
In summary, this research provides a better understanding of the relationships between fleet operating costs, emissions reductions and impacts on customer service. The insights are useful for companies trying to develop effective emission-reduction strategies. Additionally, public agencies can use these results to develop emissions reductions policies.