.svg)
Evaluating the Energy Savings from Truck-drone Hybrid Last-mile Delivery in Arbitrary Cities
Drone Delivery Energy Savings Simulator
https://github.com/urbaninfolab/TruckDroneEnergySimulator
Parcel delivery with Autonomous Aerial Vehicles (AAV) or small Unmanned Aerial Systems (sUAS), commonly known as drones, is coming soon to your doorstep. Major corporations like Amazon, Walmart, and Alphabet have already begun commercial operations in limited areas. Since they are battery-powered without an internal combustion engine, and can fly over obstacles in a straight line, they are a promising mode of urban logistics. Especially, drones are known to be able to contribute to sustainable urban logistics by reducing travel distance and greenhouse gas emissions.
However, quantifying the environmental benefits of drone delivery is challenging in practice. Policymakers performing environmental impact assessments in their own cities simply cannot reuse existing data and apply it. This is because the urban form is unique to each city, and every last-mile delivery tour is topologically unique.
This research designs a novel computer simulation framework that can estimate the energy savings in any known geographic location worldwide, compared to conventional modes. I specifically focused on one-truck multi-drone systems, whose environmental implications have not been thoroughly examined in existing literature.
The proposed framework consists of three distinct modules.
The first module takes a set of text inputs: city name, depot address, and ‘city center’ parameter. We utilize OSMnx, a Python network analysis package by Prof. Geoff Boeing, to geocode the inputs and build a graph representation of the city’s actual road network. Along the graph, hypothetical customers are generated, and the package’s routing feature calculates the travel times between each pair of customer nodes, including the depot.