An optimal path-finding algorithm for African cities: a case study of Nairobi, Kenya, considering traffic congestion and air pollution
- 1epartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Abstract
Identifying the most direct and unencumbered path within urban environments is essential, especially in the rapidly growing cities of Africa. This study presents an integrated traffic-pollution-aware routing framework for Nairobi, Kenya, one of Africa’s most heavily congested cities. Air quality is not taken into account while determining routes in the majority of current traffic congestion reduction strategies. The suggested approach is to take the city’s map out of Google Maps and use image processing techniques to turn it into a weighted graph. Google Maps was chosen over OpenStreetMap because it offers the integrated environmental data support required for dynamic route optimization, real-time traffic updates, and improved spatial accuracy. Although the high spatial resolution of Google Maps imagery was utilized for road extraction, the suggested framework is completely compatible with open-source basemaps like OpenStreetMap, which may be used in subsequent deployments without changing the technique. Air-quality data were gathered from monitoring stations managed by the National Environment Management Authority and the Kenya Meteorological Department and were supplemented using a validated Urban Emission Zone (UEZ) model for sites with no direct sensor coverage. Traffic conditions were represented using calibrated traffic simulations informed by GPS-derived traffic characteristics and available Nairobi traffic benchmarks. Traffic congestion levels are categorized throughout the network using Fuzzy C-Means (FCM) clustering technique. Air pollution levels and traffic density are considered while calculating the graph’s weights. To find the most effective and least polluting path, the routing problem is then transformed into a linear programming optimization problem. The framework is evaluated using a case study of a major transportation corridor connecting Jomo Kenyatta International Airport and Nairobi’s Central Business District (CBD), demonstrating its potential applicability for sustainable urban mobility and environmentally informed route planning.
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BibTeX
@article{parkavi2026optimal,
title = {{An optimal path-finding algorithm for African cities: a case study of Nairobi, Kenya, considering traffic congestion and air pollution}},
author = {Parkavi S and Parthiban A},
journal = {Frontiers in Environmental Science},
year = {2026},
month = oct,
doi = {10.3389/fenvs.2026.1793670},
url = {https://doi.org/10.3389/fenvs.2026.1793670},
note = {CC BY},
}
RIS
TY - JOUR
TI - An optimal path-finding algorithm for African cities: a case study of Nairobi, Kenya, considering traffic congestion and air pollution
AU - Parkavi S
AU - Parthiban A
PY - 2026
DA - 2026/10/23
JO - Frontiers in Environmental Science
DO - 10.3389/fenvs.2026.1793670
UR - https://doi.org/10.3389/fenvs.2026.1793670
AB - Identifying the most direct and unencumbered path within urban environments is essential, especially in the rapidly growing cities of Africa. This study presents an integrated traffic-pollution-aware routing framework for Nairobi, Kenya, one of Africa’s most heavily congested cities. Air quality is not taken into account while determining routes in the majority of current traffic congestion reduction strategies. The suggested approach is to take the city’s map out of Google Maps and use image processing techniques to turn it into a weighted graph. Google Maps was chosen over OpenStreetMap because it offers the integrated environmental data support required for dynamic route optimization, real-time traffic updates, and improved spatial accuracy. Although the high spatial resolution of Google Maps imagery was utilized for road extraction, the suggested framework is completely compatible with open-source basemaps like OpenStreetMap, which may be used in subsequent deployments without changing the technique. Air-quality data were gathered from monitoring stations managed by the National Environment Management Authority and the Kenya Meteorological Department and were supplemented using a validated Urban Emission Zone (UEZ) model for sites with no direct sensor coverage. Traffic conditions were represented using calibrated traffic simulations informed by GPS-derived traffic characteristics and available Nairobi traffic benchmarks. Traffic congestion levels are categorized throughout the network using Fuzzy C-Means (FCM) clustering technique. Air pollution levels and traffic density are considered while calculating the graph’s weights. To find the most effective and least polluting path, the routing problem is then transformed into a linear programming optimization problem. The framework is evaluated using a case study of a major transportation corridor connecting Jomo Kenyatta International Airport and Nairobi’s Central Business District (CBD), demonstrating its potential applicability for sustainable urban mobility and environmentally informed route planning.
SN - 2296-665X
ER -