Empirical analysis of crowd-sourced freight deliveries
Presenter: Amanda Stathopoulos, Assistant Professor of Civil and Environmental Engineering, Northwestern University
This seminar presents results from empirical analysis of crowd-sourced freight deliveries in the US. Crowd-sourced deliveries build on the idea that citizens deliver goods, ideally along planned travel routes. Crowdshipping has a potential to match highly fragmented transport capacities with vastly diverse demand for urban freight deliveries, temporally, spatially and in real-time. This is typically achieved through platforms that connect carriers with consumers in need of deliveries. A third-party broker, who operates the platform, provides match-making, analysis and customer services between demand and supply. The main advantage of crowdshipping is the reduced need for fixed facilities, such as cars or warehouses, to run operations. The main obstacles are trust, liability issues, and ensuring a critical mass of couriers and customers. Despite the growth in operations, there is still a poor understanding of the performance, functionality and acceptability of these new delivery methods. The seminar presents results analyzing the performance in the early stages of operation of crowdshipping. Based on real operational data from 2 years across the US the performance is examined with an emphasis on the specificity of crowdshipping, namely related to delivery variability and the temporal matching dynamics. Based on additional survey experiments the behavior of the main agents in the system is modeled with an emphasis on revealing acceptance and priorities of both occasional drivers and senders. The research derives from a Partnership-for-Innovation (PFI) project funded by the NSF where a Chicago based research team (NU, UIC) is evaluating the capabilities of CROwd-sourced Urban Delivery (CROUD) in collaboration with a crowd-shipper technology firm.
About Amanda: Amanda’s research focuses on developing new methodologies to collect data and specify mathematical models to account for broad and realistic choice behaviour in the transport setting (for instance social determinants, environmental concern, user experience, simplified decision rules). These richer layers of user motivations is an area of primary relevance in improving understanding and prediction of travel behavior. For a range of current transportation challenges such as promoting transit ridership growth, moving towards alternative fuels, or getting companies to adopt better practices in delivering goods, there is increasing recognition of the need to build adequate tools to account for decision complexity on the user side to match with effective decision support.