Spatial and temporal visualisation techniques for crash analysis

Abstract

Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of SVCs and the time they occur, especially at different levels of scales or spatial extents. This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of SVCs in Western Australia between 1999 and 2008, at different levels of scale. Spider graphs were adapted to identify temporal patterns of vehicle crashes at two different levels of scales: daily and weekly with respect to their causes. The spatial structures of vehicle crashes were analysed using Kernel Density Estimation analysis at three different scales: West Australia, Metropolitan area, and Perth Local Government Area (LGA). These are illustrated using spatial zooming theory. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes. The results show significant differences in spatio-temporal patterns of SVCs for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies.

A Selection of High Pedestrian Accident Zones Using Traffic Accident Data and GIS: A Case Study of Seoul

“…이러한 사업을 진행할 때, 어떤 지점에 안전 대책을 우 선적으로 수립하여야 교통안전을 크게 개선할 수 있는지 파 악하는 것은 매우 중요하다 (Khan et al, 2008). 많은 연구들이 교통사고 핫스팟 선정에 KDE를 사용하였 으며 (Anderson, 2009;Flahaut et al, 2003;Plug et al, 2011;Prasannakumar et al, 2011;Truong and Somenahalli, 2011;Vemulapalli, 2015), 일부 연구들은 KDE를 시행하여 보행자 사고 핫스팟을 선정하였다 (Blazquez and Celis, 2013;Jang et al, 2013;Loo et al, 2011;Pulugurtha et al, 2007;Rankavat and Tiwari, 2013 일부 연구들은 Getis-ord Gi*를 시행하여 교통사고 핫스팟 을 선정하였다 (Khan et al, 2008;Kingham et al, 2011;Kuo et al, 2011;Manepalli et al, 2011;Vemulapalli, 2015). 특히, Getis-ord Gi*를 사용한 기존 연구들은 Getis-ord Gi*로 …”

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

“…Plug et al [22] link data in spatial and temporal domains by using small multiples of maps, superimposing a subset of temporal data on each of the spatial maps. Jern et al [17] utilize color to link spatial and temporal data.…”

“…A. Categorizing different temporal states, i.e., unactivated (view 1), transitional (view 2), activated (view 3). B. Evolution of the cluster assignment changes through the cluster evolution view, stable cluster configuration from timestep intervals [0-10] and [20][21][22][23][24][25][26][27][28][29][30], and, random unstable assignment over timestep interval [10][11][12][13][14][15][16][17][18][19][20]. C. Detailed analysis of a time interval selected in the cluster evolution view, activation patterns can be seen in the lower-right corner of the views for the timestep interval [10,11].…”

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

“…Xie noted that NKDE has one of the same fundamental drawbacks as planar KDE. Moreover, Plug said that KDE is better for visualization purposes than for identification of black spots [49,50].…”