A Network-Based, Data-Driven Methodology for Identifying and Ranking Freight Bottlenecks

In complex road networks, the persistent challenge of inefficiency prominently arises from traffic bottlenecks. Freight bottleneck is a unique type of road traffic bottleneck that exclusively involves the analysis of truck freight mobility. Precise detection and comprehensive analysis of freight bottlenecks are pivotal in shaping optimal goods movement strategies, facilitating transportation policy-making, and improving supply chain efficiency. With the rapid growth of connected vehicles and telematics devices worldwide, there is a need for large-scale data processing and computing methods to harness the potential of big data to provide efficient and robust large-scale detection and analysis of freight bottlenecks. This research presents a methodology based on telematics big data to detect and analyze freight bottlenecks. The presented study includes a novel network-level definition of freight bottlenecks, a parallel implementation of a connected components algorithm for detecting such freight bottlenecks, and case studies on the effectiveness and performance of the proposed methodology. For the implementation of the freight bottleneck detection algorithm, the authors use 2-layer labeling and a graph contraction method, resulting in a reduction of 30% in the volume of processed bytes. This study represents a data-driven, large-scale, and network-level perspective on freight bottlenecks. The parallel implementation proves scalable and efficient database application, resulting in a large-scale and data-driven bottleneck analysis.