Villanova University

• Behavioral adaptations follow a sequenced shift from retiming to rerouting to cancellation. • System performance degrades in parallel through slower speeds, higher VHT, and detours. • Flood, built-environment, and trip factors jointly structure mobility disruption. • Mobility losses are uneven across groups, modes, and activity types. • Integrated individual-system evidence remains limited across the literature. This systematic review synthesizes 61 peer-reviewed studies to clarify how urban flooding jointly reshapes travel behavior and transport system performance, and how these coupled changes evolve through feedback between traveler decisions and network disruption. The inquiry is motivated by the convergence of extreme rainfall and rapid urbanization, a combination that accelerates runoff, heightens exposure, and places sustained pressure on urban mobility. Three findings emerge. First, flood-induced mobility disruption unfolds as a sequenced and time-dependent process in which behavioral adaptation and system degradation progress together. Travelers adjust departure times, shift routes or modes as options narrow, and cancel trips, while system performance deteriorates in parallel through declining speeds, rising vehicle hours traveled, expanding detours, and suppressed demand. Second, the distribution of flood-related mobility loss is sharply unequal, with the greatest burdens borne by low-income, marginalized, transit-dependent, and physically constrained travelers whose limited flexibility heightens exposure during walking, waiting, and transferring, and with work and school travel absorbing many of the largest delays and cancellations. Third, the evidence base exhibits a methodological imbalance, as most studies examine either behavioral responses or system performance in isolation. This review identifies four future research directions: (i) tracing floods, behavior, and operations together through time, (ii) calibrating models to observed behavioral thresholds under flood conditions, (iii) shifting equity analysis to activity-space exposure, and (iv) expanding comparative and multimodal evidence across under-studied regions and non-motorized and informal modes. These insights establish a unified basis for understanding how urban flooding restructures mobility and for strengthening planning and modeling efforts under intensifying climate conditions.

The field of developmental science is increasingly interested in conducting research that is more representative, equitable, inclusive and generalizable. One way to achieve these aims is to conduct research that positions communities as key collaborators and central contributors to our scientific inquiries. These principles are evident in community science approaches like community-based participatory research (CBPR) and participatory action research (PAR). We argue that CBPR and PAR can be implemented by developmental scientists. Here, we describe two initiatives that utilized CBPR and PAR. The first initiative is Playful Learning Landscapes (PLL). PLL embeds research from the learning sciences into public spaces to offer playful learning opportunities for children and families, and community science practices are leveraged throughout the process. The second initiative is the Galápagos Education and Research Alliance (GERA). GERA harnesses the tools of community science by working directly with communities in the fragile environment of Galápagos to gather data about ecosystem health and build community-based plans for climate change adaptation. Together, these two initiatives offer rich examples of how CBPR and PAR principles engender active participation from the community throughout the design and evaluation of a research project. We discuss the opportunities and challenges of using community-centred approaches.

ABSTRACT On freelance platforms, where value is co‐created through the joint effort of freelancers and customers, contract design plays a central managerial role. A key strategic choice concerns price timing: whether prices are set before collaboration begins (early pricing) or after the co‐creation process is completed (late pricing). We develop a game‐theoretic model comparing these contractual regimes. Under rational expectations, early pricing functions as a commitment device that coordinates mutual high effort, leading to superior product quality and welfare relative to late pricing. We test these predictions in a controlled laboratory experiment. Consistent with theory, early pricing leads freelancers to charge higher prices and earn greater profits than under late pricing. However, contrary to theoretical predictions, early pricing does not increase effort by either party and does not improve product quality or overall welfare. Instead, surplus shifts from customers to freelancers. We interpret this divergence as evidence of a behavioral failure of the commitment mechanism: Freelancers use early prices to signal quality but do not follow through with costly effort. Our findings carry important managerial implications for gig economy platforms. Without complementary reputation or monitoring systems, fixed‐price contracts may enable opportunistic behavior rather than support cooperative value creation.

ABSTRACT During the COVID‐19 pandemic, states imposed restrictions intended to slow the spread of the virus. We investigate whether states' reliance on consumption tax revenue, relative to other tax revenue sources, is associated with the duration of COVID‐19 mobility restrictions. We find that states that are more dependent on consumption taxes experienced shorter durations of stay‐at‐home orders, restaurant closures, and bar closures. We conduct a series of analyses to mitigate concerns that state‐level political preferences and biases may be influencing our findings. Our findings suggest that anticipated shortfalls in consumption tax revenue may have shaped public health responses, consistent with tax system structures relating, unintentionally, to crisis management decisions.

Understanding urban form is crucial for sustainable urban and transportation planning, and for enhancing quality of life. This study presents a data-driven framework to systematically identify and compare urban forms across geographically and culturally distinct cities. Using open-source geospatial data from OpenStreetMap, we extracted multidimensional features related to network structure, multimodality, green spaces, and points of interest for the cities of Lausanne, Switzerland, and Philadelphia, USA. A grid-based approach was used to divide each city into Basic Spatial Units (BSU), and Gaussian Mixture Models (GMM) were applied to cluster BSUs based on their urban characteristics. The results reveal coherent and interpretable urban forms within each city, with some cluster types emerging across both cities despite their differences in scale, density, and cultural context. Comparative analysis showed that adapting the grid size to each city’s forms improves the detection of shared typologies. Simplified clustering based solely on network degree centrality further demonstrated that meaningful structural patterns can be captured even with minimal feature sets. Our findings suggest the presence of functionally convergent urban forms across continents and highlight the importance of spatial scale in cross-city comparisons. The framework offers a scalable and transferable approach for urban analysis and transportation planning providing valuable insights for planners and policymakers aiming to enhance various aspects of cities including walkability, accessibility, and well-being. Limitations related to data completeness and feature selection are discussed, and directions for future work — including the integration of additional data sources and human-centered validation — are proposed. • This study presents a scalable, open-source framework to classify urban forms. • Case studies in Philadelphia and Lausanne reveal shared urban typologies. • Neighborhood clusters reflect walkability, green space, and transport access. • The results show that urban context strongly shapes cluster interpretation.

This chapter focuses on astrophysical concepts and results. The first part of the chapter describes physical processes in the interstellar medium that relate to polarization science, including grain alignment, magnetohydrodynamics, and Faraday rotation. The chapter also discusses statistical techniques, including the Davis-Chandrasekhar-Fermi method and histograms of relative orientations. The chapter concludes with discussions of recent results in both polarization measurements of thermal emission from aligned dust grains in the interstellar medium and polarization measurements of the cosmic microwave background.