Enhancing the quality and social impacts of urban planning through community-engaged operations research

While inquiry in operations research (OR) modeling of urban planning processes is long-standing, on the whole, the OR discipline has not influenced urban planning practice, teaching and scholarship at a level of other domains such as public policy and information technology. Urban planning presents contemporary challenges that are complex, multi-stakeholder, data-intensive, and ill structured. Could an OR approach which focuses on the complex, emergent nature of cities, the institutional environment in which urban planning strategies are designed and implemented and which puts citizen engagement and a critical approach at the center enable urban planning to better meet these challenges? Based on a review of research and practice in OR and urban planning, we argue that a prospective and prescriptive approach to planning that is inductive in nature and embraces “methodological pluralism” and mixed methods can enable researchers and practitioners develop effective interventions that are equitable and which reflect the concerns of community members and community serving organizations. We discuss recent work in transportation, housing, and community development that illustrates the benefits of embracing an enhanced OR modeling approach both in the framing of the model and in its implementation, while bringing to the fore three cautionary themes. First, a mechanistic application of decision modeling principles rooted in stylized representations of institutions and systems using mathematics and computational methods may not adequately capture the central role that human actors play in developing neighborhoods and communities. Second, as innovations such as the mass adoption of automobiles decades ago led to auto-centric city design show, technological innovations can have unanticipated negative social impacts. Third, the current COVID pandemic shows that approaches based on science and technology alone are inadequate to improving community lives. Therefore, we emphasize the important role of critical approaches, community engagement and diversity, equity, and inclusion in planning approaches that incorporate decision modeling.

Toward Human-Centered Design of Automated Vehicles: A Naturalistic Brake Policy

While safety is the ultimate goal in designing Connected and Automated Vehicles (CAVs), current automotive safety standards fail to explicitly define rules and regulations that ensure the safety of CAVs or those interacting with such vehicles. This study investigates CAV safety in mixed traffic environments with both human-driven and automated vehicles, focusing particularly on rear-end collisions at intersections. The central hypothesis is that the primary reason behind these crashes is the potential mismatch between CAVs’ braking decisions and human drivers’ expectations. To test this hypothesis, various Artificial Intelligence (AI) techniques, along with specialized statistical methods are adopted to learn and model the braking behavior of human drivers at intersections and compare the results to that of CAVs. Findings suggest systematical differences in CAVs’ and humans’ braking trajectories, revealing a mismatch between their braking patterns. Accordingly, a Markovian decision modeling framework is adopted to design a novel CAV braking profile that ensures 1) compatibility with human expectation, and 2) safe and comfortable maneuvers by CAVs in mixed driving environments. The findings of this study are expected to facilitate the development of higher levels of vehicle automation by providing guidelines to prevent rear-end collisions caused by existing differences in CAVs’ and humans’ braking strategies.