English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Light goods vehicles (LGV) used extensively in the last mile of delivery areone of the leading polluters in cities. Cargo-bike logistics has been putforward as a high impact candidate for replacing LGVs, with experts estimatingover half of urban van deliveries being replaceable by cargo bikes, due totheir faster speeds, shorter parking times and more efficient routes acrosscities. By modelling the relative delivery performance of different vehicletypes across urban micro-regions, machine learning can help operators evaluatethe business and environmental impact of adding cargo-bikes to their fleets. Inthis paper, we introduce two datasets, and present initial progress inmodelling urban delivery service time (e.g. cruising for parking, unloading,walking). Using Uber's H3 index to divide the cities into hexagonal cells, andaggregating OpenStreetMap tags for each cell, we show that urban context is acritical predictor of delivery performance.

Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics