Inverse Vehicle Routing for Activity-based Urban Freight Forecast Modeling and City Logistics

초록

Goods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability. Consequently, the significance of this trade-off is exacerbated in urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. Although city logistics are typically ruled by the profit-maximizing nature of private firms, its impacts on urban areas lead to the need for activity-based urban freight forecasting models which are capable of addressing trip chaining behavior and operating level events like peak period congestion. This paper proposes a modeling framework to consider both spatial-temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the Household Activity Pattern Problem (HAPP). Conceptual comparisons with a state of the art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem (SSVRP) associated with drayage truck activities at the San Pedro Bay Ports in Southern California.