Pre-crash scenarios at road junctions: a clustering method for car crash data

Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth “On-the-Spot” database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations

A holistic multi-scale approach to using 3D scanning technology in accident reconstruction

Three-dimensional scanning and documentation methods are becoming increasingly employed by law enforcement personnel for crime scene and accident scene recording. Three-dimensional documentation of the victim’s body in such cases is also increasingly used as the field of forensic radiology and imaging is expanding rapidly. These scanning technologies enable a more complete and detailed documentation than standard autopsy. This was used to examine a fatal pedestrian-vehicle collision where the pedestrian was killed by a van whilst crossing the road. Two competing scenarios were considered for the vehicle speed calculation: the pedestrian being projected forward by the impact or the pedestrian being carried on the vehicle’s bonnet. In order to assist with this, the impact area of the accident vehicle was scanned using laser surface scanning, the victim was scanned using postmortem CT and micro-CT and the data sets were combined to virtually match features of the vehicle to injuries on the victim. Micro-CT revealed additional injuries not previously detected, lending support to the pedestrian-carry theory

Mortality due to trauma in cats attending veterinary practices in central and south-east England

Objectives: To identify important demographic and spatial factors associated with the risk of trauma and, more specifically, road traffic accident‐related mortality, relative to other diagnoses in cats. Methods: A sample of 2738 cats with mortality data derived from the VetCompass primary‐care veterinary database was selected for detailed study. Generalised linear models investigated risk factors for mortality due to trauma and due to road traffic accidents versus other causes

Geospatial dashboards for intelligent multimodal traffic management

This paper presents the current status and future outlook of Traffic Management as a Service (TMaaS). TMaaS is an innovative web platform that provides a cloud-based vendor-neutral multimodal traffic management solution for small and medium-sized cities. Urban mobility data from several stakeholders and public service providers is integrated and visualized in a clean, intuitive and customizable interface for traffic operators and citizens

Hot Routes: Developing a New Technique for the Spatial Analysis of Crime

The use of hotspot mapping techniques such as KDE to represent the geographical spread of linear events can be problematic. Network-constrained data (for example transport-related crime) require a different approach to visualize concentration. We propose a methodology called Hot Routes, which measures the risk distribution of crime along a linear network by calculating the rate of crimes per section of road. This method has been designed for everyday crime analysts, and requires only a Geographical Information System (GIS), and suitable data to calculate. A demonstration is provided using crime data collected from London bus routes

Fine-grained traffic state estimation and visualisation

Tools for visualising the current traffic state are used by local authorities for strategic monitoring of the traffic network and by everyday users for planning their journey. Popular visualisations include those provided by Google Maps and by Inrix. Both employ a traffic lights colour-coding system, where roads on a map are coloured green if traffic is flowing normally and red or black if there is congestion. New sensor technology, especially from wireless sources, is allowing resolution down to lane level. A case study is reported in which a traffic micro-simulation test bed is used to generate high-resolution estimates. An interactive visualisation of the fine-grained traffic state is presented. The visualisation is demonstrated using Google Earth and affords the user a detailed three-dimensional view of the traffic state down to lane level in real time

The impact of localized road accident information on road safety awareness

The World Health Organization (WHO) estimate that road traffic accidents represent the third leading cause of ‘death and disease’ worldwide. Many countries have, therefore, launched safety campaigns that are intended to reduce road traffic accidents by increasing public awareness. In almost every case, however, a reduction in the total number of fatalities has not been matched by a comparable fall in the total frequency of road traffic accidents. Low severity incidents remain a significant problem. One possible explanation is that these road safety campaigns have had less effect than design changes. Active safety devices such as anti-lock braking, and passive measures, such as side impact protection, serve to mitigate the consequences of those accidents that do occur. A number of psychological phenomena, such as attribution error, explain the mixed success of road safety campaigns. Most drivers believe that they are less likely to be involved in an accident than other motorists. Existing road safety campaigns do little to address this problem; they focus on national and regional statistics that often seem remote from the local experiences of road users. Our argument is that localized road accident information would have better impact on people’s safety awareness. This thesis, therefore, describes the design and development of a software tool to provide the general public with access to information on the location and circumstances of road accidents in a Scottish city. We also present the results of an evaluation to determine whether the information provided by this software has any impact on individual risk perception. A route planing experiment was also carried out. The results from the experiment gives more positive feedback that road users would consider accident information if such information was available for them

Detailed Analysis and Identification of Key Factors Resulting in Motor Accidents Across the UK

Motor accidents across the globe amount to a large number of deaths every year. The collisions result in not just the personal injury to people involved but also in the loss of money to the motor insurance companies, trauma to the people involved, and added pressure on the emergency services. With the help of data analytics techniques, this project aims to identify critical factors that might contribute to the accidents. Upon investigating the temporal features and geo-spatial features of the motor accident locations, we tried to establish a correlation between the accident intensity and its key factors. For this exploratory analysis, we also considered weather conditions and daily average traffic flow data. We then trained Supervised learning models on the data to find out the best performing multi-label classification model