Logistics Sprawl and Urban Congestion Dynamics Toward Sustainability: A Logistic Regression and Random-Forest-Based Model

Manal El Yadari (), Fouad Jawab, Imane Moufad and Jabir Arif
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Manal El Yadari: Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
Fouad Jawab: Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
Imane Moufad: Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
Jabir Arif: Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco

Sustainability, 2025, vol. 17, issue 13, 1-34

Abstract: Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables.

Keywords: sustainability; urban freight transport; logistics sprawl; congestion; logistic regression; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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