Factor analysis acts a pivotal role in enhancing maritime safety. Most
previous studies conduct factor analysis within the framework of
incident-related label prediction, where the developed models can be
categorized into short-term and long-term prediction models. The long-term
models offer a more strategic approach, enabling more proactive risk
management, compared to the short-term ones. Nevertheless, few studies have
devoted to rigorously identifying the key factors for the long-term prediction
and undertaking comprehensive factor analysis. Hence, this study aims to delve
into the key factors for predicting the incident risk levels in the subsequent
year given a specific datestamp. The majority of candidate factors potentially
contributing to the incident risk are collected from vessels' historical safety
performance data spanning up to five years. An improved embedded feature
selection, which integrates Random Forest classifier with a feature filtering
process is proposed to identify key risk-contributing factors from the
candidate pool. The results demonstrate superior performance of the proposed
method in incident prediction and factor interpretability. Comprehensive
analysis is conducted upon the key factors, which could help maritime
stakeholders formulate management strategies for incident prevenion.

通过因子分析预测海事安全事故风险，筛选关键因素并提供管理策略。

探索长期船舶事故风险预测的关键因素

Exploring Key Factors for Long-Term Vessel Incident Risk Prediction

Emergency response management (ERM) is a challenge faced by communities
across the globe. First responders must respond to various incidents, such as
fires, traffic accidents, and medical emergencies. They must respond quickly to
incidents to minimize the risk to human life. Consequently, considerable
attention has been devoted to studying emergency incidents and response in the
last several decades. In particular, data-driven models help reduce human and
financial loss and improve design codes, traffic regulations, and safety
measures. This tutorial paper explores four sub-problems within emergency
response: incident prediction, incident detection, resource allocation, and
resource dispatch. We aim to present mathematical formulations for these
problems and broad frameworks for each problem. We also share open-source
(synthetic) data from a large metropolitan area in the USA for future work on
data-driven emergency response.

本教程论文探讨应急响应中的四个子问题：事件预测、事件检测、资源分配和资源调度。同时提供在美国大都市地区的开放数据进行数据驱动的应急响应研究和工作。

应急响应人工智能

Artificial Intelligence for Emergency Response

Efficient and accurate incident prediction in spatio-temporal systems is
critical to minimize service downtime and optimize performance. This work aims
to utilize historic data to predict and diagnose incidents using
spatio-temporal forecasting. We consider the specific use case of road traffic
systems where incidents take the form of anomalous events, such as accidents or
broken-down vehicles. To tackle this, we develop a neural model, called RadNet,
which forecasts system parameters such as average vehicle speeds for a future
timestep. As such systems largely follow daily or weekly periodicity, we
compare RadNet's predictions against historical averages to label incidents.
Unlike prior work, RadNet infers spatial and temporal trends in both
permutations, finally combining the dense representations before forecasting.
This facilitates informed inference and more accurate incident detection.
Experiments with two publicly available and a new road traffic dataset
demonstrate that the proposed model gives up to 8% higher prediction F1 scores
compared to the state-of-the-art methods.

使用历史数据进行 Spatio-Temporal 预测并利用神经模型 RadNet 预测交通系统的表现参数以诊断意外情况，结果显示及其精确且较先进的 F1 得分较高