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Public involvement is defined as a two-way communication aimed at providing information to the public and incorporating the views, concerns, and issues of the public in transportation decision making. According to U.S. Census data, 60% of U.S. counties are considered rural. Rural communities face unique challenges such as scarce resources, technological and geographical issues, and demographic shifts, which can limit effective engagement capabilities. Engagement strategies that are effective for urbanized and metropolitan areas may not be as effective for these rural communities. This study employed a mixed methods research approach to identify readily deployable practices for meaningful rural community engagement in transportation planning. The research methodology involves a literature review, interviews with supervisors from offices of community transportation, interviews with 24 community leaders in four case communities in Tennessee, and two rounds of Delphi community survey. The research process brought together all key stakeholders to build a true consensus of best practices to engage rural communities in transportation planning. Data analysis showed rural communities feel detached and unaware of the role of Departments of Transportation (DOT) in and their plans for community transportation. Engaging rural communities using social media and conducting virtual meetings can reach wider sections of the community. Lack of consistent internet coverage in rural communities, however, means this type of outreach cannot replace in-person engagement. Securing the support of community leaders, building partnerships, and having a presence in the community will increase trust in DOTs and foster better engagement. A list of recommendations is provided that will enhance rural community engagement for longrange transportation planning in predominantly rural states.

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Economic elites, rather than solely state actors, drove Mexico City’s transformation from a compact capital city to an industrial metropolis between the 1870s and the 1960s. I use contracts, company reports, correspondence, government documents, newspapers, oral histories, and maps to present a comprehensive history of local and international entrepreneurial elites and their interests in transportation infrastructure, housing, and urban utilities. In showing the central role of real estate developers, landlords, tramway tycoons, contractors, and bankers in the political decisions that commanded resources towards the city, my manuscript explains why Mexico City’s development was prioritized over an agrarian economy—even after millions of peasants mobilized for land distribution during the Mexican Revolution (1910-1920). Scholars of Mexico and Latin America have emphasized state and planning priorities to account for urbanization, but my manuscript shows entrepreneurial elites fostered an urban economy that attracted rural-to-urban migrants, who organized for social rights in an unevenly developed city. This work contributes to interdisciplinary efforts to address the questions of how and why modernizing projects have reproduced historical inequalities in Mexico and Latin America. I demonstrate that Mexico City’s landed elites used their control over urban property to claim the right to decide how and where to build transportation and housing infrastructure, overpowering officials that aimed to centralize public authority as part of the nineteenth-century liberal project. Digital mapping analysis with Geographic Information Systems (GIS) shows that businessmen’s investments in transportation favored the expansion of a largely informal housing market, triggering the growth of a socially differentiated city. I demonstrate that, during and after the Mexican Revolution, urban residents’ mobilization for social reform turned housing into a social right. Paradoxically, it also sparked the political organization of capitalist elites who negotiated their access to decision-making institutions. I show that their roles as bankers and planners within the mid-twentieth century welfare state explain why it transferred public investments from the countryside to Mexico City and only catered to formal workers. Because these processes continue unfolding, “Circuits of Power” is an intervention in contemporary debates on housing justice, sustainable mobility, and inequality.

Trajectory prediction aims to forecast future trajectories of agents (such as vehicles, pedestrians) based on their historical values. It is a fundamental step for advancing transportation management and control, directly impacting the safety and efficiency of modern transportation systems. In this research domain, deep learning-based methods have been widely adopted, achieving impressive performance. However, these methods have several drawbacks. First, they require substantial amounts of data. Second, they are prone to randomness inherent in real-world data. Third, complex interactions among transportation agents impose high demands on deep learning models. This dissertation seeks to address these challenges through physics-informed deep learning (PIDL), a promising approach that integrates physics-based prior knowledge into data-driven models. The dissertation is organized into three parts, focusing on different aspects of applying PIDL for trajectory prediction. First, we formulate the problem of single-agent trajectory prediction using PIDL. Second, we enhance PIDL by incorporating uncertainty quantification, accounting for uncertainties in both data and model parameters, and predicting future trajectories with confidence intervals. Third, we extend the single-agent trajectory prediction problem to a multi-agent setting, employing graph neural networks to model complex spatial interactions and NeuralODE to capture long-term dependencies. Through evaluations on both numerical and real-world datasets, our proposed methods demonstrate improved performance compared to state-of-the-art approaches. Moreover, leveraging physics-based prior knowledge makes our methods particularly robust in scenarios where deep learning models struggle, such as data-scarce environments and long-term predictions.

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