Automating multimodal routing for last-mile logistics in slums
DOI:
https://doi.org/10.58922/transportes.v34.e3142Keywords:
Last-mile; Spatial analysis; Automation; Sustainability.Abstract
Urban growth and the expansion of e-commerce have increased the logistical challenges in slum areas (“favelas”), where lack of infrastructure and detailed mapping make last-mile deliveries a challenging task. Adopting a multimodal approach based on spatial analysis can reduce greenhouse gas (GHG) emissions and improve distribution in hard-to-reach areas. However, the execution of these processes still depends on manual methods, limiting their replicability. This study implements an automated multimodal routing process based on GIS tools, allowing geospatial and delivery data to be processed in a systematic way. The integration of LiDAR data with OpenStreetMap features enables the mapping of the road/alley network, as well as its characterization (slopes and width) and definition of the most suitable transportation modes (by foot, bicycle, drone, and motorcycle), prioritizing environmentally sustainable options. The results include optimizing the allocation of modes, reducing travel distances by motorcycle, and reducing CO2 emissions by 32%. The main contribution of this study is the creation of a replicable and scalable workflow, allowing last-mile logistics operations in slums to be more efficient and sustainable.
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Copyright (c) 2026 Lara Silva, Milena de Campos Soares, Orlando Fontes Lima Júnior, Janaina Antonino Pinto, Henrique Candido de Oliveira, Luciano Aparecido Barbosa
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