Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
This work addresses the environmental and business challenges of last-mile delivery for logistics operators, but it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of urban pollution from delivery vehicles by modeling the performance of cargo bikes versus light goods vehicles across urban micro-regions, showing that urban context is a critical predictor of delivery service time.
Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating over half of urban van deliveries being replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. By modelling the relative delivery performance of different vehicle types across urban micro-regions, machine learning can help operators evaluate the business and environmental impact of adding cargo-bikes to their fleets. In this paper, we introduce two datasets, and present initial progress in modelling urban delivery service time (e.g. cruising for parking, unloading, walking). Using Uber's H3 index to divide the cities into hexagonal cells, and aggregating OpenStreetMap tags for each cell, we show that urban context is a critical predictor of delivery performance.