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.

翻译：轻型货车（LGV）广泛用于最后一公里配送，是城市主要污染源之一。货运自行车物流被视为替代轻型货车的高效方案，专家估计超过半数的城市厢式货车配送可被货运自行车取代，因其具有更快的速度、更短的停车时间及更高效的城市配送路线。通过建模不同车辆类型在城市微区域的相对配送性能，机器学习可帮助运营者评估将货运自行车纳入车队对业务与环境的影响。本文提出两个数据集，并展示城市配送服务时间（如巡航寻位、卸货、步行）建模的初步进展。利用优步H3索引将城市划分为六边形单元格，并聚合每个单元格的OpenStreetMap标签，研究表明城市环境是配送性能的关键预测因子。