Tessa Kate Anderson

Kernel density estimation and K-means clustering to profile road accident hotspots

Identifying road accident hotspots is a key role in determining effective strategies for the reduction of high density areas of accidents. This paper presents (1) a methodology using Geographical Information Systems (GIS) and Kernel Density Estimation to study the spatial patterns of injury related road accidents in London, UK and (2) a clustering methodology using environmental data and results from the first section in order to create a classification of road accident hotspots. The use of this methodology will be illustrated using the London area in the UK. Road accident data collected by the Metropolitan Police from 1999 to 2003 was used. A kernel density estimation map was created and subsequently disaggregated by cell density to create a basic spatial unit of an accident hotspot. Appended environmental data was then added to the hotspot cells and using K-means clustering, an outcome of similar hotspots was deciphered. Five groups and 15 clusters were created based on collision and attribute data. These clusters are discussed and evaluated according to their robustness and potential uses in road safety campaigning.

Using geodemographics to measure and explain social and environment differences in road traffic accident risk

Geodemographic systems currently provide classifications of small areas based primarily on their demographic and socioeconomic characteristics. This paper presents an innovative use of geodemographic methods in the public sector, in particular to determine peoples road injury risk. It uses Stats19 road accident data from the London Metropolitan Police, specifically postcode information relating to the driver and casualty of each road accident from 1999 to 2003. Experians Mosaic software was used to append each postcode to a geodemographic Mosaic type. The results showed distinct spatial and statistical prevalence of certain groups within society to be more at risk of being in a road accident. This highlights the need for a more sophisticated understanding of the different risks people face as they engage in the road environment.

Spatial Analysis Methods of Road Traffic Collisions

Examine the Prevalence and Geography of Road Collisions Spatial Analysis Methods of Road Traffic Collisions centers on the geographical nature of road crashes, and uses spatial methods to provide a greater understanding of the patterns and processes that cause them. Written by internationally known experts in the field of transport geography, the book outlines the key issues in identifying hazardous road locations (HRLs), considers current approaches used for reducing and preventing road traffic collisions, and outlines a strategy for improved road safety. The book covers spatial accuracy, validation, and other statistical issues, as well as link-attribute and event-based approaches, cluster identification, and risk exposure. The book provides a brief summary of the evolution of road safety in the twentieth century, explores current road safety problems, and establishes road safety as a public health issue. The authors discuss risk and socioeconomic factors, lifestyle and behavior, and the impact of urban development. They consider road engineering, signage, vehicle design, the education of road users, and the enforcement of traffic safety measures. They also factor in the overall impact of road traffic collisions on transportation systems, economic systems, health systems, and society as a whole. Combines theoretical methodology with empirical data Bridges research and practice in road safety Includes case studies from around the world Spatial Analysis Methods of Road Traffic Collisions takes a look at spatial methods and their role in analyzing road traffic collisions to improve road safety. A great addition to transportation safety practice and research, this book serves as a reference for spatial analysis researchers and postgraduate students in traffic and transportation engineering, transport, and urban transport planning.