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

• URL: http://arxiv.org/abs/2301.12887v1
• Date: Mon, 30 Jan 2023 13:47:01 GMT
• Title: Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
• Authors: Max Schrader, Navish Kumar, Nicolas Collignon, Esben S{\o}rig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Astefanoaei
• Abstract summary: Over half of urban van deliveries are replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes. 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.
• Score: 9.194377306280757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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