Urban parcel delivery, demand model, behavioral modeling, Macroscopic Fundamental Diagram (MFD), UAV traffic management (UTM), mechanism design
Digital Object Identifier (DOI)
Fueled by burgeoning e-commerce, urban parcel delivery (UPD) has emerged as a high growth market that is undergoing rapid technological change, particularly in the business-to-consumer segment. New classes of vehicles such as drones, droids, and autonomous ground vehicles, combined with new delivery models featuring crowdsourcing, parcel lockers, and mobile lockers, will enable a significant shift away from the conventional model of a dedicated delivery person operating a van. To reach the full potential of these changes to reduce costs and increase convenience, it is necessary to develop a complementary set of demand management strategies that will enable the next-generation parcel delivery system to mitigate current traffic congestion problems and avoid creating new ones. The project aims to (1) quantify the current and anticipated future contributions of UPD to urban congestion and related problems, such as traffic accidents and (2) identify opportunities for incentivizing consumers and delivery services to modify their behaviors to reduce the congestion impacts of UPD. To accomplish these objectives, the focus is on (1) demand models of e-commerce behaviors, (2) measuring the impact of delivery service operations on urban congestion using macroscopic fundamental diagrams, and (3) urban operations of drone deliveries to assess their potential for removing parcel delivery demand on roads. The modeling system will be used to assess the congestion reduction benefits of a range of policies geared toward encouraging consumers and service providers to adopt behaviors that reduce the congestion caused by urban delivery. In addition, an analytical framework for assessing the safety impacts, including non-recurring congestion reductions, of innovative UPD technologies is proposed. The method for identifying UPD crashes and statistical models for estimating UPD crash risks at TAZ levels by given demographic, roadway, and traffic conditions.
Scholar Commons Citation
Walker, Joan; Hansen, Mark; Lin, Pei-Sung; Wang, Zhenyu; Li, Ang; Tereshchenko, Ivan; Forscher, Teddy; Li, Mingchen; and Yang, Runan, "Demand-Side Management of Auto Traffic for Urban Parcel Delivery" (2021). Research Reports. 8.