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towards the integration of game theory and operations research to understand freight system dynamics

 

The importance of the transportation sector in terms of energy and the environment is undeniable. The statistics show that, in the United States of America (USA), transportation consumed 28.5% of the total energy and 67.9% of the petroleum; and produced 54% of the carbon monoxide, 36% of the nitrogen oxide, 22% of the volatile organic compounds, and 1.4% of the sulfure dioxide. Obviously, overcoming the global warming challenge and achieving a sustainable economy requires improving the efficiency of transportation.

At the same time, transportation is one of the key engines of economic development and globalization. Official estimates indicate that transportation account for 10.3% of the USA Gross Domestic Product. The statistics show that about one in seven workers in the USA are doing freight related activities, and to one in four if one adds those employed in logistics.

Regrettably, current trucking practices are very inefficient from the economic and environmental point of view. The surveys show that about 25% of the truck trips are empty, and that the utilization of the trucks is equally low as, on average, only 20% of the truck capacity is utilized. Improving the overall efficiency of the freight industry could have dramatic effects on its competitiveness, and on reducing the environmental impacts produced by truck activity.

This is major challenge because, the freight transportation system will have to: be a proactive participant in National security efforts, cover a larger geographic area, be more responsive to user needs and expectations, reduce the externalities of truck traffic; and do all of this while providing additional freight infrastructure capacity will become more difficult and expensive. The freight transportation system will have to do more with less. This puts significant pressure on Metropolitan Planning Organizations to enhance freight transportation planning processes. This is compounded by the lack of freight-transportation-specific modeling methodologies because most of freight modeling applications are nothing more than adaptations of methodologies originally designed for passenger transportation, that overlook the unique characteristics of freight transportation. As a result of this, there is a severe lack of knowledge about how to model freight systems using first principles, while taking into account features such as the: economic interactions between the agents involved in freight decision making, market equilibrium/dynamics, profit maximizing behavior, and trip chaining.

In this, Professor Holguín-Veras will discuss the fundamental interactions linking shippers, carriers, and receivers, and how these interactions determine the outcome of two of the most important decisions for transportation policy making purposes: mode choice, and the choice of delivery times. The presentation will discuss the role of the nature of the relationship linking these agents in shaping their joint response to policies such as congestion pricing. In the second part of the presentation, Professor Holguín-Veras will discuss the concept of spatial price equilibrium, its potential applications as the modeling foundation of urban freight demand models, as well as the formulations developed to model the competition process among the companies involved.

These formulations consider two different cases: (1) a market comprised of independent shippers, carriers, and receivers in which the carriers compete for the transportation of the cargoes in order to maximize profits; and (2) a market in which the shipper and the corresponding carrier are part of a “supplier” that competes with other suppliers, in a dynamic fashion by changing production levels and transportation decisions (e.g., routing, rates). The unique aspect of these formulations is that by explicitly considering delivery tours, they extend the field spatial price equilibrium as most formulations only consider commodity flows. This second formulation could be used to find fairly accurate solutions to the competitive facility location problem.