Demand concentration and temporal imbalance in last-mile urban freight distribution
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weekday capacity planning, location-based demand segmentation, Gini coefficient concentration analysis, organizational shipment heterogeneity, dynamic route allocation, sustainable urban logistics
Abstract
Purpose. This paper identifies temporal, spatial, and organizational patterns in urban freight demand and develops a statistically grounded pre-optimization framework for sustainable last-mile logistics planning in a medium-sized city. Methodology. The empirical basis comprises 4,967 anonymized delivery records collected over 125 active service days in Ternopil. Demand intensity was normalized by active day and examined through Pareto analysis, Gini and Herfindahl-Hirschman concentration indices, and a 5,000-replication location bootstrap. Weekday and organizational differences were tested using Kruskal-Wallis tests with Holm-adjusted post-hoc comparisons, while a weekday-adjusted HC3 regression assessed the within-period trend and Pearson and Spearman correlations examined the consistency between load mass and volume. Results. Average daily order intensity increased by 25.9% and average daily load mass by 36.4% between January and May. Weekday imbalance was substantial, with Saturday significantly lower than every Monday-Friday group after correction. Destination demand was highly concentrated: the Gini coefficient reached 0.729 for order frequency and 0.832 for load mass, with a dual core of just 30 locations (7.1%) generating 65.0% of total mass. Load mass and recorded volume were strongly and positively associated (Spearman’s rho = 0.817), while organizational divisions differed significantly in shipment-size profiles. Theoretical contribution. The framework integrates temporal imbalance, dual-capacity demand, and two-dimensional location concentration within a single decision architecture for urban freight distribution. Practical implications. The findings support weekday-specific capacity thresholds, fixed service templates for the demand core, sector-based consolidation for the middle segment, and dynamic routing for the dispersed long tail, with cost and emission effects formulated for subsequent operational validation rather than assumed.
SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action
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Copyright (c) 2026 Oleh Tson, Yuriy Vovk, Nataliya Rozhko, Oleh Lyashuk, Uliana Plekan
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