Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling

Driving along any rural road within Western Australia involves some level of uncertainty about encountering an animal whether it is wildlife, farm stock or domestic. This level of uncertainty can vary depending on factors such as the surrounding land use, water source, geometry of the road, speed limits and signage. This paper aims to model the risk of animal–vehicle crashes (AVCs) on a segmented highway. A hierarchical Bayesian model involving multivariate Poisson lognormal regression is used in establishing the relationship between AVCs and the contributing factors. Findings of this study show that farming on both sides of a road, a mixture of farming and forest roadside vegetation and roadside vegetation have significant positive effect on AVCs, while speed limits and horizontal curves indicate a negative effect. AVCs consist of both spatial- and segment-specific contributions, even though the spatial random error does not dominate model variability. Segment 15 is identified as the highest risk segment and its nearby segments also exhibit high risk

Injuries to older users of buses in the UK

This is an Open Access article licensed under a Creative Commons Attribution 4.0 Unported Licence.The increasing age of the United Kingdom (UK) population coupled with enhanced life expectancy impacts on transport-user demographics and will affect transport planning in the years ahead. Whilst passenger car use is the ultimate means of personal independence, at some point the physiological and psychological impact of age-related conditions will inevitably shift people out of their vehicles and onto public transport systems. Overall, public transport is seen to be vital for social inclusion (Lucas et al 2008) and it is considered a safe means of mobility. However, it is important that the public and, in particular, the elderly perceive it to be so. Injuries (across a spectrum of severities) do occur during public transport use from time to time. In fact, over 5,000 people are injured on UK buses each year alone with over 300 bus-users killed/seriously injured (Department for Transport, 2012). This study was designed to examine the nature of injuries and their causes to older bus-users with the aim being to establish where design countermeasures may be indicated. The study uses descriptive statistics to analyse linked (accident and injury) data involving a sample of older bus-users. Most incidents in the linked dataset were non-collisions (62 per cent) resulting in 1,381 recorded injuries in those aged 60+ years, of which 46 per cent were 'slight' and 54 per cent 'serious'

Multiday Driving Patterns and Motor Carrier Accident Risk: A Disagregate Analysis

A method has been developed to estimate the relative accident risk posed by different patterns of driving over a multi-day period. The procedure explicitly considers whether a driver is on-duty of off-duty for each half hour of each day during the period of analysis.From a data set of over 1000 drivers, 9 distinct driving patterns are identified. Membership in the patterns is determined exclusively by the pattern of duty hours for seven consecutive days; for some drivers and accident occurred on the eighth day while others had no accident. Therefore each pattern can be associated with a relative accident risk. Additional statistical modeling allowed consideration, in addition to driving pattern, of driver age, experience with the firm, hours off-duty prior to the last trip and hours driving on the last trip (either until the accident or successful completion of the trip).The findings of the modeling are that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during afternoon and evening hours (e.g. noon to midnight) has the highest accident risk while driving during night and morning hours (e.g. midnight to noon) has lower risk. Consecutive hours driven also has a significant effect on accident risk: the first hour of driving and the ninth and tenth hour of driving have the highest risk. Hours 2 through 8 follow a flattened "u" shape. Driver age and hours off-duty immediately prior to a trip do not appear to affect accident risk significantly.These findings quantitatively assess the relative accident risk of multi-day driving patterns using data from actual truck operations. Further research is recommended in the areas of refining model structures, adding explanatory variables (such as highway type) and testing more complex models

Assessing The Safety Benefits Of Automated Freeways

This research is aimed at the identification of all types of accidents that have occurred on freeways which may affect the vehicle movement in the assumed automated left lane. The emphasis was on six-lane or more urban freeways. The accident data was obtained from the California TASAS ( Traffic Accidents Surveillance and Analysis System) data base and were used to identify all the accidents that originated outside the potential automated area and which affected movement of traffic in the leftmost lane

Multiday Driving Patterns and Motor Carrier Accident Risk: A Disagregate Analysis

A method has been developed to estimate the relative accident risk posed by different patterns of driving over a multi-day period. The procedure explicitly considers whether a driver is on-duty of off-duty for each half hour of each day during the period of analysis.From a data set of over 1000 drivers, 9 distinct driving patterns are identified. Membership in the patterns is determined exclusively by the pattern of duty hours for seven consecutive days; for some drivers and accident occurred on the eighth day while others had no accident. Therefore each pattern can be associated with a relative accident risk. Additional statistical modeling allowed consideration, in addition to driving pattern, of driver age, experience with the firm, hours off-duty prior to the last trip and hours driving on the last trip (either until the accident or successful completion of the trip).The findings of the modeling are that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during afternoon and evening hours (e.g. noon to midnight) has the highest accident risk while driving during night and morning hours (e.g. midnight to noon) has lower risk. Consecutive hours driven also has a significant effect on accident risk: the first hour of driving and the ninth and tenth hour of driving have the highest risk. Hours 2 through 8 follow a flattened "u" shape. Driver age and hours off-duty immediately prior to a trip do not appear to affect accident risk significantly.These findings quantitatively assess the relative accident risk of multi-day driving patterns using data from actual truck operations. Further research is recommended in the areas of refining model structures, adding explanatory variables (such as highway type) and testing more complex models

Assessing The Safety Benefits Of Automated Freeways

This research is aimed at the identification of all types of accidents that have occurred on freeways which may affect the vehicle movement in the assumed automated left lane. The emphasis was on six-lane or more urban freeways. The accident data was obtained from the California TASAS ( Traffic Accidents Surveillance and Analysis System) data base and were used to identify all the accidents that originated outside the potential automated area and which affected movement of traffic in the leftmost lane.Express highways--Automation, Automobiles--Automatic control, Roads--Safety measures, Intelligent Vehicle Highway Systems