发表机构
University of Wisconsin--Madison; University of Wyoming（威斯康星大学麦迪逊分校; 怀俄明大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在预测碰撞热点的出现与演变，提出HERALD统一深度学习框架，能检测热点诞生、预测下周位置及跟踪生命周期。该框架在威斯康星州六个县表现出色，比基线更准确、定位更精确，还能提前预警，推动热点管理从回顾过去转向预测未来。
AI 中文摘要
道路碰撞仍然是对公共安全最严重的威胁之一，预防碰撞是全球交通系统的关键任务。大部分危害集中在热点地区，但热点与其说是一个地方，不如说是一个事件；它在十字路口或主干道悄然出现，持续数周，然后消退，之后在其他地方再次出现。以往碰撞地图指导下的执法必然滞后于这个周期，在关注昨天热点的同时，明天的热点却无人监管。打破这种滞后需要同时具备三种能力：在热点诞生时检测它们，预测下周它们将出现在何处，以及跟踪每个热点的整个生命周期。我们引入了HERALD（热点出现、风险预测和生命周期动态），这是一个统一的深度学习框架，从一个单一的全州模型中提供所有这三种能力。HERALD将每个县最近的碰撞历史提炼成每周风险地图，并用CNN-Transformer预测下一周的情况，其专家混合机制使一个模型能够服务于密集的城市核心区和稀疏的农村走廊。每个预测都基于该县的长期碰撞地理情况，通过近期碰撞的自激发效应进行强化，并伴有新热点即将出现的明确警告。随着时间的推移，每个热点都有一个清晰的生命周期故事，从诞生到成长、稳定，再到衰退和消亡。在威斯康星州的六个不同县，HERALD的预测比五个经过相同训练的基线更准确，能最精确地定位热点，并在新风险形成之前发出预警。一个单一的可调整设置可以在部署需要时以准确性换取更高的敏感性。结果是将热点管理从绘制过去的地图转变为预测未来。
英文摘要
Road crashes remain among the gravest threats to public safety, and preventing them is a defining task of transportation systems worldwide. Much of that harm concentrates at hotspots, yet a hotspot is less a place than an episode; it emerges quietly at an intersection or along an arterial, intensifies for weeks, then subsides, only to reappear elsewhere. Enforcement guided by maps of past crashes inevitably trails this cycle, patrolling yesterday's hotspots while tomorrow's form unwatched. Breaking that lag requires three capabilities at once: detecting hotspots as they are born, forecasting where they will sit next week, and following each one through its life. We introduce HERALD (Hotspot Emergence, Risk Anticipation, and Life-cycle Dynamics), a unified deep learning framework that provides all three from a single statewide model. HERALD distills each county's recent crash history into weekly risk maps and forecasts the next with a CNN--Transformer, whose mixture-of-experts lets one model serve dense urban cores and sparse rural corridors alike. Each forecast is anchored in the county's long-run crash geography, sharpened by the self-exciting effect of recent crashes, and paired with explicit warnings of where new hotspots are about to appear. Followed over time, every hotspot acquires a legible life story, from birth through growth and stability to decline and death. Across six heterogeneous Wisconsin counties, HERALD forecasts more accurately than five identically trained baselines, locates hotspots most precisely, and flags emerging risks before they take hold. A single adjustable setting trades accuracy for extra sensitivity where deployment demands it. The result shifts hotspot management from mapping the past to anticipating the future.
