Authors : Fun-Akpo Pere Kormane; Thankgod Ode; Captain Gospel Otto; Kemejika Ichechi Amadi-Oparaeli

Volume/Issue : Volume 11 - 2026, Issue 10 - October

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Google Scholar : https://tinyurl.com/dkp6n5th

DOI : https://doi.org/10.38124/ijisrt/26Oct253

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Urban route generation depends on traffic statistics that are persistent in time, uneven in space and only partly predictable. This study applies descriptive, temporal, concentration, clustering, regression and sensitivity statistics to two complementary datasets: 7,908 ten-minute junction-volume periods and hourly travel times from Padua, Italy, and aggregate morning and evening peak observations from seven Port Harcourt, Nigeria, locations linked to a directed OpenStreetMap network of 48,504 nodes and 111,839 edges.

Keywords : Traffic Flow Statistics; Autocorrelation; Coefficient of Variation; Herfindahl Index; K-Means Clustering; Gradient Boosting; Route Generation; Port Harcourt.

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