Multi-scale Modeling of Crowdshipping as a New Form of Urban Delivery

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E-commerce in the US has experienced explosive growth in the past decade, resulting in a precipitous increase in truck traffic. The surge of truck traffic has caused many negative consequences to the urban environment such as greater traffic congestion and emissions, faster wear-and-tear on road infrastructure, and more acute shortage of truck parking space. These consequences are increasingly at odds with the need to develop livable urban communities which requires significant reduction of truck traffic. Crowdshipping has emerged recently as an alternative to truck-based delivery. Using a crowd of ordinary individuals who walk, bike, or drive to perform delivery in urban areas, crowdshipping presents considerable promise to reconcile the strong need for livable community development with the rapid growth of urban freight demand. This award develops theoretic foundations for modeling and improving the efficiency of crowdshipping for urban delivery. Results from the research will lead to a better understanding of the traffic, economic, and environmental impact of crowdshipping and support urban freight policy making. Knowledge gained from this project will also provide generic insights for other areas in the increasingly crowdsourced and on-demand society, such as ride sourcing and mobile sensing. By designing, delivering, and evaluating a pipeline of engineering education activities, this award will further enhance the awareness and understanding of freight transportation and urban delivery innovations among diverse student groups. The research activities are organized around two thrusts. The first thrust establishes static and dynamic mechanisms to address individual solicitation and shipment assignment problems at the microscale. The properties of the mechanisms in incentivizing individual participation in crowdsourced delivery and the asymptotics of an approximation technique in shipment assignment will be investigated. The second thrust creates a queueing network-based framework to characterize and improve system performance of crowdshipping at the macroscale. Conditions for crowdshipping system equilibrium existence and equilibrium flows of crowdsourced individuals will be identified. Analytical capabilities will be developed to determine the optimal pool size of crowdsourced individuals to meet given shipping demand and improve delivery service fairness. The developed mechanisms, techniques, and models will be evaluated using case studies informed by real world data. If successful, this research will advance the knowledge and analysis capabilities of crowdshipping for urban delivery, and enrich the literature of transportation systems analysis and city logistics. Additionally, the PI will engage diverse student groups (high school and community college students) to raise awareness in understanding of freight transportation and urban delivery innovations among diverse student groups.