The efficient and timely delivery of urban freight is a key factor in determining the economic health and sustainability of urban centers and their surrounds. Urban freight consists of both large shipments needed to replenish retail inventories and small parcels that fulfill electronic and telephonic commerce initiated by individual consumers. This study will develop new urban freight transportation planning and pricing tools that recognize the intrinsic uncertainty of freight demands, costs and delivery times. A family of mathematical models in the form of stochastic dynamic games, and their robust counterparts, that exploit recent advances in revenue management and nonlinear pricing theory will be created to describe a variety of urban settings. Fixed point algorithms, descent in Hilbert space and agent based simulation will be employed to solve these models for hypothetical data and, thereby, develop rules-of-thumb to guide urban planners and freight company operators in configuring urban freight networks and pricing urban freight services.
This research will provide models and algorithms that can be integrated with evolving information technology and decision support systems for influencing urban and suburban traffic congestion. Urban freight planning that directly considers the risk to shippers of untimely deliveries, the risks to carriers of penalties for violating quality of service guaranties, and the risks to private vehicles of excessive congestion will be possible, for the first time. Moreover, the results of this research will allow planners to quantify the advantages of restricting truck-based deliveries to particular times of day or particular routes, including the possible assignment of a unique time and route to each specific truck. Additionally, time-of-day tolls and auction-based strategies that charge delivery vehicles for access to congested areas will be facilitated by tools developed in this study.
