arXiv subjects

Songhua Hu

Publications and source records attributed to Songhua Hu.

14 recordsLinked to original sources

arXiv2026

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↗

arXiv2025

The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics

The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution within these evolving sectors. To model and predict visitor flow patterns, we develop a BiTransGCN framework, integrating an attention-based Transformer architecture with a Graph Convolutional Network backbone. By integrating AI-enabled forecasting techniques with spatial analysis, this study improves our ability to track, interpret, and anticipate changes in industry clustering and mobility trends, thereby supporting strategic planning for a secure and resilient transportation network. It offers a data-driven foundation for economic planning, workforce development, and targeted investments in the transportation cybersecurity ecosystem.

cs.CY↗

arXiv2025

Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility

Monitoring health resource disparities during public health crises is critical, yet traditional methods, like surveys, lack the requisite speed and spatial granularity. This study introduces a novel framework that leverages: 1) crowdsourced Google Maps reviews (2018-2021) and 2) advanced NLP (DeBERTa) to create a high-resolution, spatial-temporal index of public perception of health resource accessibility in the United States. We then employ Partial Least Squares (PLS) regression to link this perception index to a range of socioeconomic and demographic drivers. Our results quantify significant spatial-temporal shifts in perceived access, confirming that disparities peaked during the COVID-19 crisis and only partially recovered post-peak. We identify political affiliation, racial composition, and educational attainment as primary determinants of these perceptions. This study validates a scalable method for real-time health equity monitoring and provides actionable evidence for interventions to build a more resilient healthcare infrastructure.

cs.CL↗

arXiv2024

Assessing Response Disparities in California Wildland-Urban-Interface (WUI) Cities Using the Compartmental Model

The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in real-time. This study uses social media data to assess public responses and explores how these responses are linked to city-level community characteristics. Specifically, we leveraged a transformer-based topic modeling technique called BERTopic to identify wildfire response-related topics and then utilized the Susceptible-Infectious-Recovered (SIR) model to compute two key metrics associated with wildfire responses - awareness and resilience indicators. Additionally, we used GIS-based spatial analysis to map wildfire locations along with four groups of city-level factors (racial/ethnic, socioeconomic, demographic, and wildfire-specific). Our findings reveal significant geographic and socio-spatial differences in public responses. Southern California cities with larger Hispanic populations demonstrate higher wildfire awareness and resilience. In contrast, urbanized regions in Central and Northern California exhibit lower awareness levels. Furthermore, resilience is negatively correlated with unemployment rates, particularly in southern regions where higher unemployment aligns with reduced resilience. These findings highlight the need for targeted and equitable wildfire management strategies to improve the adaptive capacity of WUI communities.

cs.SI↗

arXiv2024

Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design

As urban populations grow, the need for accessible urban design has become urgent. Traditional survey methods for assessing public perceptions of accessibility are often limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models can facilitate their use. This study uses Google Maps reviews across the United States and fine-tunes Llama 3 model with the Low-Rank Adaptation technique to analyze public sentiment on accessibility. At the POI level, most categories -- restaurants, retail, hotels, and healthcare -- show negative sentiments. Socio-spatial analysis reveals that areas with higher proportions of white residents and greater socioeconomic status report more positive sentiment, while areas with more elderly, highly-educated residents exhibit more negative sentiment. Interestingly, no clear link is found between the presence of disabilities and public sentiments. Overall, this study highlights the potential of crowdsourcing for identifying accessibility challenges and providing insights for urban planners.

cs.SI↗

arXiv2024

Academic collaboration on large language model studies increases overall but varies across disciplines

Interdisciplinary collaboration is crucial for addressing complex scientific challenges. Recent advancements in large language models (LLMs) have shown significant potential in benefiting researchers across various fields. To explore their potential for interdisciplinary collaboration, we collect and analyze data from OpenAlex, an open-source academic database. Our dataset comprises 59,293 LLM-related papers, along with 70,945 machine learning (ML) papers and 73,110 papers from non-LLM/ML fields as control groups. We first employ Shannon Entropy to assess the diversity of collaboration. Our results reveal that many fields have exhibited a more significant increasing trend following the release of ChatGPT as compared to the control groups. In particular, Computer Science and Social Science display a consistent increase in both institution and department entropy. Other fields such as Decision Science, Psychology, and Health Professions have shown minor to significant increases. Our difference-in-difference analysis also indicates that the release of ChatGPT leads to a statistically significant increase in collaboration in several fields, such as Computer Science and Social Science. In addition, we analyze the author networks and find that Computer Science, Medicine, and other Computer Science-related departments are the most prominent. Regarding authors' institutions, our analysis reveals that entities such as Stanford University, Harvard University, and University College London are key players, either dominating centrality or playing crucial roles in connecting research networks. Overall, this study provides valuable information on the current landscape and evolving dynamics of collaboration networks in LLM research. It also suggests potential areas for fostering more diverse collaborations and highlights the need for continued research on the impact of LLMs on scientific practices.

cs.SI↗

arXiv2024

Crowdsourced reviews reveal substantial disparities in public perceptions of parking

Due to increased reliance on private vehicles and growing travel demand, parking remains a longstanding urban challenge globally. Quantifying parking perceptions is paramount as it enables decision-makers to identify problematic areas and make informed decisions on parking management. This study introduces a cost-effective and widely accessible data source, crowdsourced online reviews, to investigate public perceptions of parking across the U.S. Specifically, we examine 4,987,483 parking-related reviews for 1,129,460 points of interest (POIs) across 911 core-based statistical areas (CBSAs) sourced from Google Maps. We employ the Bidirectional Encoder Representations from Transformers (BERT) model to classify the parking sentiment and conduct regression analyses to explore its relationships with socio-spatial factors. Findings reveal significant variations in parking sentiment across POI types and CBSAs, with Restaurant POIs showing the most negative. Regression results further indicate that denser urban areas with higher proportions of African Americans and Hispanics and lower socioeconomic status are more likely to exhibit negative parking sentiment. Interestingly, an opposite relationship between parking supply and sentiment is observed, indicating increasing supply does not necessarily improve parking experiences. Finally, our textual analysis identifies keywords associated with positive or negative sentiments and highlights disparities between urban and rural areas. Overall, this study demonstrates the potential of a novel data source and methodological framework in measuring parking sentiment, offering valuable insights that help identify hyperlocal parking issues and guide targeted parking management strategies.

cs.CY↗

arXiv2024

Crowdsourcing public attitudes toward local services through the lens of Google Maps reviews: An urban density-based perspective

Understanding how urban density impacts public perceptions of urban service is important for informing livable, accessible, and equitable urban planning. Conventional methods such as surveys are limited by their sampling scope, time efficiency, and expense. On the other hand, crowdsourcing through online platforms presents an opportunity for decision-makers to tap into a user-generated source of information that is widely available and cost-effective. To demonstrate such potential, we collect Google Maps reviews for 23,906 points of interest (POIs) in Atlanta, Georgia. Next, we use the Bidirectional Encoder Representations from Transformers (BERT) model to classify reviewers' attitudes toward urban density and the Robustly Optimized BERT approach (RoBERTa) to compute sentiment. Finally, a partial least squares regression is fitted to examine the relationships between average sentiment and socio-spatial factors. The findings reveal areas in Atlanta with predominantly negative sentiments toward urban density and highlight the variation in sentiment distribution across different POIs. Further, the regression analysis reveals that minority and low-income communities often express more negative sentiments, and higher land use density exacerbates such negativity. This study introduces a novel data source and methodological framework that can be easily adapted to different regions, offering useful insights into public sentiment toward the built environment and shedding light on how planning policies can be designed to handle related challenges.

cs.SI↗

arXiv2023

Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data

Climate changes lead to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data offer new opportunities to monitor and investigate changes in road traffic flow during extreme weather. This study utilizes diverse crowdsourced data from mobile devices and the community-driven navigation app, Waze, to examine the impact of three weather events (i.e., floods, winter storms, and fog) on road traffic. Three metrics, speed change, event duration, and area under the curve (AUC), are employed to assess link-level traffic change and recovery. In addition, a user's perceived severity is computed to evaluate link-level weather impact based on crowdsourced reports. This study evaluates a range of new data sources, and provides insights into the resilience of road traffic to extreme weather, which are crucial for disaster preparedness, response, and recovery in road transportation systems.

econ.GN↗

arXiv2023

A Big-Data Driven Framework to Estimating Vehicle Volume based on Mobile Device Location Data

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance plans and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions such as probe vehicle data, while still suffering from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, Mobile Device Location Data (MDLD) have been growing dramatically in terms of the spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume at a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, i.e., Annual Vehicle-Miles of Travel (AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is implemented on the all-street network in the state of Maryland using MDLD for the entire year of 2019. Results indicate that our proposed framework produces reliable vehicle volume estimates and also demonstrate its transferability and the generalization ability.

cs.CY↗

arXiv2020

COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: "best performance", "effort", and "consistency". We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.

physics.soc-ph↗

arXiv2020

Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S

One approach to delay the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. It is yet unclear how effective those policies are on suppressing the mobility trend due to the lack of ground truth and large-scale dataset describing human mobility during the pandemic. This study uses real-world location-based service data collected from anonymized mobile devices to uncover mobility changes during COVID-19 and under the 'Stay-at-home' state orders in the U.S. The study measures human mobility with two important metrics: daily average number of trips per person and daily average person-miles traveled. The data-driven analysis and modeling attribute less than 5% of the reduction in the number of trips and person-miles traveled to the effect of the policy. The models developed in the study exhibit high prediction accuracy and can be applied to inform epidemics modeling with empirically verified mobility trends and to support time-sensitive decision-making processes.

stat.AP↗

arXiv2015

The new explanation of cluster synchronization in the generalized Kuramoto system

The cluster synchronization is a very important characteristic for the higher harmonic coupling Kuramoto system. A novel transformation is provided, and it gives cluster synchronization by the periodic properties of the density function. The periodic properties of the density function also make the cluster sections' boundaries barrier-like, which helps to explain the sensitiveness of cluster synchronization on the initial conditions of the oscillators. Detailed numerical studies confirm the theoretical predictions from this new view of the symmetry transformation. The work is very beneficial to the further study on cluster synchronization in various systems.

nlin.CD↗

arXiv2015

The symmetry of the Kuramoto system and the essence of the cluster synchronization

The cluster synchronization (CS) is a very important characteristic for the higher harmonic cou- pling Kuramoto system. A novel method from the symmetry transformation is provided, and it gives CS a profoundly mathematical explanation and clear physical annotation. Detailed numerical studies for the order parameters in various conditions confirm the theoretical predictions from this new view of the symmetry transformation. The work is very beneficial to the further study on CS in various systems.

nlin.CD↗