Computational Methods for Road Freight Transportation Using Telematics Data

Abstract

This thesis focuses on developing computational frameworks to address challenges in road freight transportation using telematics data. It aims to improve operational efficiency, reduce congestion, and inform policy decisions through advanced data analysis and visualization techniques. Recognizing the explanatory potential of visualization, we first examine the relevant literature and present a decision support tool for selecting suitable visualization techniques. One of the major cost factors in road freight transportation is freight bottlenecks. We allocate a chapter to identify and rank these bottlenecks at the network level using a parallel connected components algorithm. This method employs a utility-based ranking framework that includes emissions and economic factors, offering an efficient, scalable, and data-driven view of freight congestion. The remaining sections of the thesis apply the developed freight visualization and bottleneck frameworks to investigate critical road freight challenges: emissions-aware routing and the evaluation of complete street infrastructure implementation. A label-setting algorithm generates various routes with their emission trade-offs, facilitating route benchmarking and retrospective analysis of real-world vehicle operations. By utilizing data fusion and clustering, an interrupted time series analysis was conducted to evaluate the long-term effects of Bus Rapid Transit (BRT), providing insights into how BRT can influence freight flow, emissions, and bottlenecks. The research highlights the importance of data-driven decision-making in transportation policy. The proposed methodologies can help decision-makers optimize freight operations, reduce emissions, and improve urban planning.