论文标题
英国智能高速公路上的主要和次要事故的非参数霍克斯进程模型
A non-parametric Hawkes process model of primary and secondary accidents on a UK smart motorway
论文作者
论文摘要
在2017 - 18年期间，在12个月的时间内，自我激发的时空点过程适合来自英国国家交通信息服务的事件数据，以对M25高速公路上的主要和次要事故发生率进行建模。此过程使用背景组件来表示主要事故，以及一个自我激发的组件来表示次要事故。背景由定期和每周的组件，空间组件和长期趋势组成。自我激发的组件是衰减的空间和时间的单向功能。这些组件是通过内核平滑和可能性估计来确定的。在时间上，背景在整个季节都保持稳定，每天的双峰结构反映通勤模式。在空间上，强度有两个峰，其中一个在研究期间变得更加明显。自我激发分别占数据的6-7％，相关的时间和长度尺度分别为100分钟和1公里。进行样本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上大幅下降的事件时，结果仍然连贯。
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.
