Exploring the impact of traffic congestion on CO2 emissions in freight distribution networks

Kellner, Florian · 2016 · OpenAlex-citations

DOI: 10.1007/s12159-016-0148-5

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Summary

This study quantifies the impact of regular road traffic congestion on CO2 emissions within a real-world freight distribution network. Motivated by the significant contribution of transport-related greenhouse gases to climate change and the forecasted increase in traffic congestion, the research addresses the need for accurate, area-wide assessments of emission sensitivities. The authors argue that neglecting congestion leads to underestimations of emissions and that conventional data sources, such as interviews or limited sensor data, lack the consistency and representativeness required for large-scale logistics analysis. Consequently, the paper explores the use of online navigation services as a reliable data source for modeling traffic conditions across entire distribution networks. The methodology employs a detailed network model of a major German fast-moving consumer goods (FMCG) manufacturer, analyzing transport operations over one calendar year. The model covers four transportation flows: production flows, direct shipments, consolidated shipments via transshipment points, and last-mile delivery trips. Crucially, the study utilizes trip and traffic data retrieved from online navigation services (e.g., Bing Maps, Google Maps) rather than standard collection methods. This data allows for the simulation of two scenarios: a "free-flow" condition and a "normal" condition that accounts for regular, predictable congestion and driver re-routing behaviors. To assess the impact of network structure, the authors experimentally varied the number of distribution centers from one to four using a p-median model. CO2 emissions were calculated using the COPERT fuel consumption model, which incorporates extensive parameters including vehicle type, load factors, travel speed, and distance. The findings demonstrate that online navigation services provide a valuable, unbiased, and representative source of traffic data for logistics research. The study confirms that regular traffic congestion significantly increases CO2 emissions across the distribution network due to reduced travel speeds and altered itineraries. By isolating the congestion effect, the research provides specific insights into how different transport activities and geographical areas are exposed to these emission increases. Furthermore, the analysis reveals that modifying the network structure, specifically by increasing the number of distribution centers, alters the extent to which traffic congestion affects total CO2 volumes. This allows for the a priori evaluation of how alternative network configurations influence the environmental footprint of road freight transport. The significance of this work lies in its comprehensive approach to determining the CO2 footprint of distribution networks, offering a robust tool for logistics designers and operators. By combining a detailed network model with high-quality traffic data from navigation services, the study enables accurate comparisons of congestion effects across different transport activities. This supports better strategic and tactical decision-making, such as facility location planning and transport scheduling, by quantifying the environmental costs of congestion. The research also establishes a framework for future studies to analyze additional greenhouse gas sensitivities, such as fleet mix and load factors, thereby contributing to more effective strategies for reducing emissions in freight transportation.

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• traffic density