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Behnam Tahmasbi

Publications and source records attributed to Behnam Tahmasbi.

3 recordsLinked to original sources

Mobility Inequity and Risk Response After Hurricane Helene: Evidence from Real-Time Travel and Social Sentiment Data

Hurricanes severely disrupt infrastructure and restrict access to essential services. While the physical impacts on post-disaster mobility are well studied, less is known about how individual travel behaviors change during and after disasters, and how these responses are shaped by social and geographic disparities. This study examines mobility patterns following Hurricane Helene, a Category 4 storm that struck six southeastern U.S. states on September 26, 2024, causing over 230 fatalities. Using anonymized GPS mobility data, hurricane severity metrics, and county-level social media sentiment, we examine shifts in travel behavior and their implications for equity. We ask two questions: How do post-hurricane mobility patterns reflect community vulnerability and adaptive capacity? and How do sociodemographic conditions and public sentiment factors shape the direction and extent of mobility change? Results from robust linear and ordered logistic regressions indicate that evacuation orders increase mobility; however, severe storm conditions, particularly high wind speeds, can limit travel. Communities with lower incomes, located in rural areas, and with higher percentages of Black populations exhibit the steepest declines in mobility, suggesting resource constraints and infrastructural barriers, while wealthier, urban, and higher-education areas maintain greater flexibility. Results also show that positive social sentiment is associated with higher mobility and a greater likelihood of increased travel during the hurricane. Our findings highlight the need to address structural barriers and social conditions in post-disaster mobility and disaster response.

cs.SI

Integrating En Route and Home Proximity in EV Charging Accessibility: A Spatial Analysis in the Washington Metropolitan Area

This study evaluates the accessibility of public EV charging stations in the Washington metropolitan area using a comprehensive measure that accounts for both destination-based and en route charging opportunities. By incorporating the full spectrum of daily travel patterns into the accessibility evaluation, our methodology offers a more realistic measure of charging opportunities than destination-based methods that prioritize proximity to residential locations. Results from spatial autocorrelation analysis indicate that conventional accessibility assessments often overestimate the availability of infrastructure in central urban areas and underestimate it in peripheral commuting zones, potentially leading to misallocated resources. By highlighting significant clusters of high-access and low-access areas, our approach identifies spatial inequalities in infrastructure distribution and provides insights into areas requiring targeted interventions. This study underscores the importance of incorporating daily mobility patterns into urban planning to ensure equitable access to EV charging infrastructure and suggests a framework that other regions could adopt to enhance sustainable transportation networks and support equitable urban development.

physics.soc-ph

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Due to Covid-19

COVID-19 has deeply affected daily life and travel behaviors. Understanding these changes is crucial, prompting an investigation into socio-demographic and socio-economic factors. This study used large-scale mobile device location data in Washington, D.C., Maryland, and Virginia (DMV area) to unveil the impacts of these variables on commute trip changes. It reflected short and long-term impacts through linear regression and geographically weighted regression models. Findings indicated that counties with a higher percentage of people using walking and biking during the initial phase of COVID-19 experienced greater reductions in commute trips. For the long-term effect in November, the impact of active modes became insignificant, and individuals using public modes showed more significant trip reductions. Positive correlations were observed between median income levels and reduced commute trips. Sectors requiring ongoing outdoor operations during the pandemic showed substantial negative correlations. In the DMV area, counties with a higher proportion of Democratic voters experienced less trip reduction. Applying Geographically Weighted Regression models captured local spatial relationships, showing the emergence of local correlations as the pandemic evolved, suggesting a geographical impact pattern. Initially global, the pandemic's impact on commuting behaviors became more influenced by spatial factors over time, showing localized effects.

stat.AP