Understanding and Measuring the Performance of Mixed Mode Urban Freight Delivery Systems Using Large Scale Mobility Data

<p>This dissertation addresses the critical gap in understanding and evaluating the efficiency and sustainability of mixed-mode urban freight delivery systems. Existing performance metrics often fall short in providing a holistic view, being mostly mode-specific and unidimensional. To overcome this challenge, we propose a comprehensive metric - the Urban Freight Mobility Energy Productivity (UF-MEP), providing a multi-dimensional performance perspective. Our study uses a data-driven approach, integrating various inputs across different delivery modes. </p> <p>The first part of the research introduces a deep-learning based energy model to assess the risk associated with worst-case energy consumption in drone operations for last-mile deliveries. Utilizing a Temporal Convolutional Network (TCN), we generate a risk distribution which, together with the worst-case energy use, provides a comprehensive risk assessment framework. </p> <p>The second part focuses on inferencing truck activity from GPS data, presenting a unique method for determining truck operations, activity hotspots, and thereby enabling an understanding of the overall operational patterns. The algorithm developed in this segment was applied on a large-scale real-world dataset, providing pragmatic insights into the effect of variable data frequency and data loss. </p> <p>In the third part, we propose a novel multi-dimensional performance metric, the UF-MEP. Applying this metric to large-scale truck networks in the greater Philadelphia region, we demonstrated its applicability and relevance. UF-MEP provided a unified measure that considers cost, time, energy, and mode compatibility factors, offering insights into the quality and energy efficiency of urban freight delivery systems. </p> <p>While this work lays a solid foundation, future research could focus on extending the factors considered within the UF-MEP framework and integrating with regional freight demand models. This could provide a more nuanced tool for policymakers and urban planners, allowing for an evaluation of diverse investment strategies, policy decisions, and the influence of new technologies on urban freight transportation accessibility. </p>