Freight Transport and Land Use Interaction: an Analysis Approach Based on Floating Car Data

Comi, Antonio; Polimeni, Antonio · 2024 · Crossref

DOI: 10.1016/j.trpro.2024.03.045

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Summary

This paper addresses the interaction between urban freight transport and land use, specifically investigating how territorial characteristics influence the production of freight vehicle tours. The authors argue that while the location of production activities significantly impacts freight distribution costs and efficiency, existing literature often overlooks the spatial correlation among land-use attributes when forecasting freight demand. To bridge this gap, the study proposes a methodology that combines floating car data (FCD) with secondary territorial data to calibrate models for predicting tour generation, explicitly accounting for spatial autocorrelation. The methodology employs spatial analysis techniques, utilizing the Moran Index to detect spatial autocorrelation among variables before model calibration. The study area is the Veneto region in northern Italy, divided into 581 zones. The primary data source is a dataset of FCD from 1,579 light freight vehicles (laden weight < 3.5 tons) collected over 60 working days in 2018, resulting in approximately 23,000 tours. Secondary data from ISTAT includes the number of wholesale and retail activities and employees in these sectors. The authors calibrated two types of models to predict the rate of tours generated per zone: a traditional linear regression model and a spatial autoregressive model. The independent variables included the number of wholesale and retail activities and a dummy variable for city center location. The results demonstrate significant positive spatial autocorrelation for all analyzed attributes, including wholesale activities, retail activities, employees, and generated tours, as indicated by Moran’s I values greater than zero and statistically significant p-values. Consequently, the spatial autoregressive model outperformed the traditional linear regression model. The spatial model achieved an R-squared value of 0.542, compared to 0.425 for the linear model. The spatial lag parameter was positive and significant, confirming that the number of tours in a zone is influenced by neighboring zones. The number of wholesale and retail activities were identified as significant predictors of tour generation. The significance of this work lies in validating the necessity of incorporating spatial dependence into freight trip generation models. By leveraging large-scale FCD, the study offers a cost-effective and detailed alternative to traditional operator interviews. The findings imply that ignoring spatial correlation leads to less accurate forecasts of urban freight demand. The authors conclude that this approach can be replicated in other contexts as telematics penetration increases, suggesting future work should extend the analysis to different vehicle classes and specific economic sectors to refine trip attraction and composition modeling.

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• urban rural setting