The Data-Driven Optimization of Parcel Locker Locations in a Transit Co-Modal System with Ride-Pooling Last-Mile Delivery

Integrating passenger and parcel transportation via transit (also known as transit co-modality) has been regarded as a potential solution to sustainable transportation, in which well-planned locations for parcel lockers are crucial for transferring parcels from transit to last-mile delivery vehicles. This paper proposes a data-driven optimization framework on parcel locker locations in a transit co-modal system, where last-mile delivery is realized via a ride-pooling service that pools passengers and parcels using the same fleet of vehicles. A p-median model is proposed to solve the problem of optimal parcel locker locations and matching between passengers and parcel lockers. We use the taxi trip data and the candidate parcel locker location data from Shenzhen, China, as inputs to the proposed p-median model. Given the size of the dataset, an optimization framework based on random sampling is then developed to determine the optimal parcel locker locations according to each candidate’s frequency of being selected in the sample. The numerical results are given to show the effectiveness of the proposed optimization framework, explore its properties, and perform sensitivity analyses on the key model parameters. Notably, we identify five types of optimal parcel location based on their ranking changes according to the maximum number of planned parcel locker locations, which suggests that planners should carefully determine the optimal number of candidate locations for parcel locker deployment. Moreover, the results of sensitivity analyses reveal that the average passenger detour distance is positively related to the density of passenger demand and is negatively impacted by the number of selected locations. We also identify the minimum distance between any pair of selected locations as an important factor in location planning, as it may significantly affect the candidates’ rankings.