CrashFormer：一种预测碰撞风险的多模态架构
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
AI总结：
CrashFormer提出一种多模态架构，融合事故历史、天气、地图图像和人口数据，每六小时预测5.161平方公里区域的事故风险，在美国10城市实验中F1分数平均提升1.8%。
摘要：
减少交通事故是一个关键的全球公共安全问题。事故预测是改善交通安全的关键，能够在碰撞发生前采取主动措施，并为安全政策、法规和针对性干预提供信息。尽管过去几十年中已有大量关于事故预测的研究，但许多研究在泛化性、可重复性或实际应用的可行性方面存在局限，这通常是由于输入数据或问题设定所致。为解决现有不足，我们提出了CrashFormer，一种多模态架构，利用全面但相对容易获取的输入，如事故历史、天气信息、地图图像和人口统计信息。该模型以合理可接受的频率（即每六小时）预测5.161平方公里地理位置的未来事故风险。CrashFormer由五个组件组成：一个序列编码器，用于利用历史事故和天气数据；一个图像编码器，用于使用地图图像数据；一个原始数据编码器，用于利用人口统计信息；一个特征融合模块，用于聚合编码特征；以及一个分类器，接受聚合数据并据此进行预测。在美国10个主要城市进行的大量真实世界实验结果表明，当使用“稀疏”输入数据时，CrashFormer在F1分数上平均比最先进的序列和非序列模型高出1.8%。
英文摘要：
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash occurs, and informing safety policies, regulations, and targeted interventions. Despite numerous studies on accident prediction over the past decades, many have limitations in terms of generalizability, reproducibility, or feasibility for practical use due to input data or problem formulation. To address existing shortcomings, we propose CrashFormer, a multi-modal architecture that utilizes comprehensive (but relatively easy to obtain) inputs such as the history of accidents, weather information, map images, and demographic information. The model predicts the future risk of accidents on a reasonably acceptable cadence (i.e., every six hours) for a geographical location of 5.161 square kilometers. CrashFormer is composed of five components: a sequential encoder to utilize historical accidents and weather data, an image encoder to use map imagery data, a raw data encoder to utilize demographic information, a feature fusion module for aggregating the encoded features, and a classifier that accepts the aggregated data and makes predictions accordingly. Results from extensive real-world experiments in 10 major US cities show that CrashFormer outperforms state-of-the-art sequential and non-sequential models by 1.8% in F1-score on average when using ``sparse'' input data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
