Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China

Abstract

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions.

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

“…Li et al [24] conducted a GIS-based Bayesian analysis to discover the spatiotemporal pattern of motor vehicle crashes occurred in Harris County, Texas, and they found stability in high-risk segments using the data of four years. Fan [25] explored the traffic collision characteristics of Jianghan District of Wuhan, and detected how the collision hotspots evolve over time from a spatiotemporal perspective. The relationship between features of imported second-hand cars and likelihood of traffic crashes was evaluated by Reference [26], in which the spatiotemporal visualization of data was performed using Geographic Information System (GIS), while the statistical analysis was conducted by regression models.…”

A Non-Parametric Hawkes Process Model of Primary and Secondary Accidents on a UK Smart Motorway

“…They furthered this by incorporating spatial components into an autoregressive model, finding it improved upon models disregarding the spatial correlation in the data. Additional analysis is provided in Fan et al (2018), where the authors analysed the evolution of event hot spots through time on an urban network in China. They extended and applied kernel density estimation on networks, Moran's I and Local Indicators of Mobility Associations (LIMA) to offer exploratory analysis of the data set from multiple perspectives.…”

A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets

“…To deal with a variable that is a rate or proportion, the local Moran's I based on Empirical Bayes (EB) standardization is normally used [43]. The LISA statistics and its extensions have been widely used for diverse spatial applications including public health, transportation, ecology, population, and crime [44]- [48].…”