arXiv subjects

Shangkun Jiang

Publications and source records attributed to Shangkun Jiang.

5 recordsLinked to original sources

arXiv2026

Evacuation destination choices during Hurricane Ian: A direct demand modeling approach

Hurricanes are causing unprecedented damage to the natural environment, infrastructure, and communities. Understanding evacuation behavior is essential for improving emergency preparedness. Past studies have relied on surveys and interviews, which are prone to recall bias. Additionally, they urge incorporating social vulnerability in evacuation research, emphasizing its impact on evacuation capability and destination choice. This study addresses these gaps by analyzing evacuation behavior using mobile device location data from Hurricane Ian, one of Florida's deadliest hurricanes, and directly incorporating variables from the Social Vulnerability Index (SVI) into a zone-to-zone (census tract level) evacuation demand model. We find that vehicle availability, residence in group quarters, road density, and English proficiency have significant effects on evacuation demand, shaping both the ability to evacuate from origin tracts and the attractiveness of destination tracts. Travel impedance, measured as distance, also plays a significant role, with evacuees substantially less likely to travel longer distances.

stat.AP↗

arXiv2026

Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires

The growing frequency and intensity of wildfires pose serious threats to communities in wildland-urban interface regions. Understanding evacuation behavior is critical for effective emergency planning. This study analyzes evacuation during the 2025 Palisades and Eaton Fires using high-resolution Facebook data. We propose a comprehensive framework to derive wildfire evacuation-related metrics, including compliance rate, departure timing, delay, origin-destination flows, travel distance, and destination types. A new metric, Damage-Evacuation Disparity Index (DEDI), identifies areas with severe structural damage but low evacuation compliance. Results reveal spatiotemporal heterogeneity: residents closer to the fire evacuated earlier, whereas late or nighttime orders led to lower compliance and longer delays. Contrasting patterns between East and West Altadena further illustrate this disparity. DEDI-identified communities exhibited higher social vulnerability and fire risk. Most evacuations concluded in residential areas, while longer trips concentrated in hotels and public facilities. These findings showcase the Facebook data's potential for data-driven wildfire evacuation planning.

stat.AP↗

arXiv2024

Analysis of vessel traffic flow characteristics in inland restricted waterways using multi-source data

To effectively manage vessel traffic and alleviate congestion on busy inland waterways, a comprehensive understanding of vessel traffic flow characteristics is crucial. However, limited data availability has resulted in minimal research on the traffic flow characteristics of inland waterway vessels. This study addresses this gap by conducting vessel-following experiments and fixed-point video monitoring in inland waterways, collecting multi-source data to analyze vessel traffic flow characteristics. First, the analysis of vessel speed distribution identifies the economic speed for vessels operating in these environments. Next, the relationship between microscopic vessel speed and gap distance is examined, with the logarithmic model emerging as the most accurate among various tested models. Additionally, the study explores the relationships among macroscopic speed, density, and flow rate, proposing a novel piecewise fundamental diagram model to describe these relationships. Lastly, the inland vessel traffic states are categorized using K-means clustering algorithm and applied to vessel navigation services. These findings provide valuable insights for enhancing inland waterway transportation and advancing the development of an integrated waterway transportation system.

cs.CE↗

arXiv2024

Evaluation of waterway lock service quality in Yangtze Delta: from the perspectives of customer and supplier

In recent decades, the waterway locks in the Yangtze Delta, China, have become major traffic bottlenecks. To gain a comprehensive understanding of the crew's perspectives and primary concerns regarding lock services during vessel lockage, and to enhance customer satisfaction and improve vessel lockage efficiency, it is necessary to assess the waterway lock service quality (WLSQ). This paper presents an evaluation system for WLSQ from various stakeholders' viewpoints. Firstly, by employing questionnaire surveys and the structural equation model method, in conjunction with factor analysis, the WLSQ and its influencing factors in the Yangtze River Delta region are analyzed from a customer perspective. Secondly, the Analytic Hierarchy Process method is utilized, along with a dedicated questionnaire for service suppliers, to examine their concerns regarding the performance of vessel lock services. The findings indicate that there exists a cognitive bias towards factors influencing the WLSQ. Crew members express the greatest concern over vessel lockage delays, whereas vessel lockage safety is the primary concern for management department administrators. Furthermore, enhancing the supporting facilities of waterway locks can significantly increase crew members' satisfaction during vessel lockage. Improving staff skills, and safety conditions can also greatly enhance customers' tolerance for lockage delays. The results of this study will provide valuable insights for the lock management department, operators, and the government in formulating relevant policies to improve WLSQ and implementing ongoing service quality evaluations.

cs.CY↗

arXiv2024

Hurricane Evacuation Analysis with Large-scale Mobile Device Location Data during Hurricane Ian

Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the context of climate change, mobile device location data offers an unprecedented opportunity to study hurricane evacuation behaviors. With a terabyte-level GPS dataset, we introduce a holistic hurricane evacuation behavior algorithm with a case study of Ian: we infer evacuees' departure time and categorize them into different behavioral groups, including self, voluntary, mandatory, shadow and in-zone evacuees. Results show the landfall area (Fort Myers, Lee County) had lower out-of-zone but higher overall evacuation rate, while the predicted landfall area (Tampa, Hillsborough County) had the opposite, suggesting the effects of delayed evacuation order. Out-of-zone evacuation rates would increase from shore to inland. Spatiotemporal analysis identified three evacuation waves: during formation, before landfall, and after landfall. These insights are valuable for enhancing future disaster planning and management.

cs.CE↗