  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.
