论文标题
伦敦大流行的流量恢复更安全 - 车祸的时空数据挖掘
Safer Traffic Recovery from the Pandemic in London -- Spatiotemporal Data Mining of Car Crashes
论文作者
论文摘要
为了通过智能地改善道路安全性来支持伦敦从大流行中恢复更安全的恢复，这项研究调查了涉及年龄涉及的汽车崩溃和影响因素的时空模式，回答了两个主要的研究问题：（1）“汽车的空间和时间模式是什么，汽车的崩溃以及他们在2019年和2020年的两个典型年龄，伦敦和有影响力的效果如何？ （2）“按年龄组按伤亡人数的伤亡时空模式是什么，以及人们的日常活动如何影响大流行的模式”？ Three approaches, i.e., spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes on weekly and daily basis respectively, detect the crashes' hot spots, and to gain better understanding the effect from citizens' daily activity on crashes' patterns pre- and para- the pandemic.从研究中发现，汽车撞车主要聚集在伦敦的中部，尤其是在较密集的途径周围的繁忙地区（POIS）；通过通过地理探测器进一步评估互动，POI作为公民日常活动和旅行行为的反射器可以更好地了解坠机模式；崩溃的伤亡模式因年龄段而异，对相应年龄组的POIS和崩溃模式之间的关系独特。总而言之，该论文对伦敦的车祸及其伤亡模式进行了深入的探索性分析，以促进Covid-19对大规模大规模恢复的部署政策。
In the aim to support London's safer recovery from the pandemic by improving road safety intelligently, this study investigated the spatiotemporal patterns of age-involved car crashes and affecting factors, upon answering two main research questions: (1)"What are the spatial and temporal patterns of car crashes as well as their changes in two typical years, 2019 and 2020, in London, and how the influential factors work?"; (2)"What are the spatiotemporal patterns of casualty by age groups, and how people's daily activities affect the patterns pre- and para- the pandemic"? Three approaches, i.e., spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes on weekly and daily basis respectively, detect the crashes' hot spots, and to gain better understanding the effect from citizens' daily activity on crashes' patterns pre- and para- the pandemic. It had been found from the study that car crashes mainly clustered in the central part of London, especially busier areas around denser hubs of point-of-interest (POIs); the POIs, as a reflector for citizens' daily activities and travel behaviours, can be of help to gain a better understanding of the crashes' patterns, upon further assessment on interactions through the geographical detector; the crashes' casualty patterns varied by age group, with distinctive relationships between POIs and crashes' pattern for corresponding age group categorised. In all, the paper provided an in-depth exploratory analysis of car crashes and their casualty patterns in London to facilitate deployment policies towards post-pandemic safer recovery upon COVID-19.
