UK智能高速公路主、次事故的非参数Hawkes过程模型
A non-parametric Hawkes process model of primary and secondary accidents on a UK smart motorway
摘要：
一个自激时空点过程被拟合到英国国家交通信息服务中心的事件数据中，以模拟2017-18年12个月期间M25高速公路上的主要和次要事故率。该过程使用背景分量来表示主要事故，并且使用自激分量来表示次要事故。背景由周期性的每日和每周分量、空间分量和长期趋势组成。自激分量是空间和时间的衰减、单向函数。这些成分通过核平滑和似然估计来确定。在时间上，背景是稳定的跨季节的日常双峰结构，反映通勤模式。在空间上，有两个强度峰值，其中一个在研究期间变得更加明显。自激占数据的6-7%，相关时间和长度分别约为100分钟和1公里。进行样本内和样本外验证以评估模型拟合。当我们将数据限制为导致网络速度大幅下降的事件时，结果仍然是一致的。
英文摘要：
A self-exciting spatio-temporal point process is fitted to incident data from the UK National Traffic Information Service to model the rates of primary and secondary accidents on the M25 motorway in a 12-month period during 2017-18. This process uses a background component to represent primary accidents, and a self-exciting component to represent secondary accidents. The background consists of periodic daily and weekly components, a spatial component and a long-term trend. The self-exciting components are decaying, unidirectional functions of space and time. These components are determined via kernel smoothing and likelihood estimation. Temporally, the background is stable across seasons with a daily double peak structure reflecting commuting patterns. Spatially, there are two peaks in intensity, one of which becomes more pronounced during the study period. Self-excitation accounts for 6-7% of the data with associated time and length scales around 100 minutes and 1 kilometre respectively. In-sample and out-of-sample validation are performed to assess the model fit. When we restrict the data to incidents that resulted in large speed drops on the network, the results remain coherent.
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。
