David Logan

Analysis of Risk Factors Affecting the Severity of Intersection Crashes by Logistic Regression

Objective: The objective of this research was to study the risk factors that significantly influence the severity of intersection crashes for vehicle occupants, as well as for pedestrians and other vulnerable road users. Methods: Logistic regression was applied as the method in this study to analyze a data set of intersection crashes involving casualties in Victoria, Australia, for the period January 2000 to December 2009. Results: Seven risk factors obtained were found to be significantly associated with the severity of intersection crashes, including driver age and gender, speed zone, traffic control type, time of day, crash type, and seat belt usage. Conclusions: This study found that male drivers as well as older drivers (age 65 and above) had higher odds of being involved in fatal intersection crashes. Intersection crashes occurring between midnight and early morning (12:00 a.m. to 5:59 a.m.), in 100 km/h speed zones, or with no traffic control had a higher odds of a fatal outcome than their counterpart categories. Furthermore, intersection crashes involving pedestrians or a non–seat belt–wearing driver were more likely to lead to a fatal outcome. In general, identification of risk factors and the discussion of the odds ratio between levels on the impact of the intersection crash severity would be beneficial for road safety stakeholders to develop initiatives to reduce the severity of intersection crashes.

A kinetic energy model of two-vehicle crash injury severity

An important part of any model of vehicle crashes is the development of a procedure to estimate crash injury severity. After reviewing existing models of crash severity, this paper outlines the development of a modelling approach aimed at measuring the injury severity of people in two-vehicle road crashes. This model can be incorporated into a discrete event traffic simulation model, using simulation model outputs as its input. The model can then serve as an integral part of a simulation model estimating the crash potential of components of the traffic system. The model is developed using Newtonian Mechanics and Generalised Linear Regression. The factors contributing to the speed change (ΔV(s)) of a subject vehicle are identified using the law of conservation of momentum. A Log-Gamma regression model is fitted to measure speed change (ΔV(s)) of the subject vehicle based on the identified crash characteristics. The kinetic energy applied to the subject vehicle is calculated by the model, which in turn uses a Log-Gamma Regression Model to estimate the Injury Severity Score of the crash from the calculated kinetic energy, crash impact type, presence of airbag and/or seat belt and occupant age.

Investigation Into the Effect of an Intersection Crash Warning System on Driving Performance in a Simulator

Objective: The objective of this research was to examine the effects on driving performance of a prototype intersection collision warning system (ICWS) that is based on the dedicated short-range communication (DSRC) technology concept. Methods: An experimental study with 32 participants using a driving simulator was conducted to examine the effects of ICWS on the driving performance of different drivers sorted by gender (male, female) and experience (experienced, novice) compared across 3 warning conditions: no warning, audio warning, and visual warning. Each participant completed 3 test, drives with each drive comprising 3 events. Each event contained a combination of scenario type (cross-traffic from left/right scenario, right turn against scenario) and warning condition. Results: The results showed that an ICWS, in general, could reduce the number of intersection crashes by approximately 40 to 50 percent and shorten drivers’ reaction times to brake in response to emergent events occurring at intersections. Moreover, drivers’ reaction times to brake in response to the audio warning system were significantly shorter than in response to the visual warning system in the cross-traffic from right scenario. In addition, speeds were reduced in all 3 scenarios and deceleration rates increased in the cross-traffic from left and right turn against scenario under the influence of ICWS. Conclusions: In general, the results provide overall support for ICWSs based on the DSRC technology concept. The broader implications of the research are discussed.

Factors affecting the probability of bus drivers being at-fault in bus-involved accidents

Previous research has provided little insight into factors that influence the probability of bus drivers being at-fault in bus-involved accidents. In this study, an analysis was conducted on accident data compiled by a bus company that include an assessment on whether the bus driver was deemed by the company to hold primary responsibility for accident occurrence. Using a mixed logit modelling approach, roadway/environmental, vehicle and driver related variables that were identified to be influential were road type, speed limit, traffic/lighting conditions, bus priority, bus age/length and drivers age/gender/experience/historic at-fault accident record. Results were indicative of possible confined road-space issues that bus drivers face along routes with roadside traffic friction and point to the provision of exclusive right of way for buses as a possible way to address this. Results also suggest benefits in assigning routes comprising mainly divided roads as well as newer and shorter buses to less experienced drivers.

An empirical bayes safety evaluation of tram/streetcar signal and lane priority measures in Melbourne

ABSTRACT Objective: Streetcars/tram systems are growing worldwide, and many are given priority to increase speed and reliability performance in mixed traffic conditions. Research related to the road safety impact of tram priority is limited. This study explores the road safety impacts of tram priority measures including lane and intersection/signal priority measures. Method: A before–after crash study was conducted using the empirical Bayes (EB) method to provide more accurate crash impact estimates by accounting for wider crash trends and regression to the mean effects. Before–after crash data for 29 intersections with tram signal priority and 23 arterials with tram lane priority in Melbourne, Australia, were analyzed to evaluate the road safety impact of tram priority. Results: The EB before–after analysis results indicated a statistically significant adjusted crash reduction rate of 16.4% after implementation of tram priority measures. Signal priority measures were found to reduce crashes by 13.9% and lane priority by 19.4%. A disaggregate level simple before–after analysis indicated reductions in total and serious crashes as well as vehicle-, pedestrian-, and motorcycle-involved crashes. In addition, reductions in on-path crashes, pedestrian-involved crashes, and collisions among vehicles moving in the same and opposite directions and all other specific crash types were found after tram priority implementation. Conclusions: Results suggest that streetcar/tram priority measures result in safety benefits for all road users, including vehicles, pedestrians, and cyclists. Policy implications and areas for future research are discussed.

Road Safety Benefits from Bus Priority: An Empirical Study

The provision of bus priority is often a major consideration in on-street bus rapid transit design. However, the research is limited and suggests mixed outcomes. This paper explores the impact of bus priority treatments on road safety. An empirical analysis of accident data about bus rapid transit routes in Melbourne, Australia, was conducted. The analysis included an aggregate crash frequency analysis that accounted for statistical effects and a disaggregate analysis that used a safety audit and review of accident types. Overall, bus priority treatments reduced accidents, with a statistically significant reduction of 14.0%. The number of fatal and serious incidents dropped considerably (42 to 29 per annum). The disaggregate safety audit showed reductions in intolerable risks, but some concerns such as interaction of buses and traffic at bus lane setbacks (section of a curbside lane where traffic may enter to make a turn as the bus lane reaches an intersection) and greater pedestrian road-crossing distances caused by bus lanes. However, disaggregate analysis of accident type suggested that bus lanes act as a roadside buffer, reducing collisions with roadside objects and other vehicles. The shifting of stopping buses from the traffic lane into a bus lane was shown to reduce accidents. Bus lanes were thought to increase sight distances at unsignalized intersections and consequently reduce accidents with vehicles to the side. Some treatments were thought to increase traffic density, creating safety benefits through slowed traffic. These findings were statistically robust and suggest that a new perspective on planning for and justifying bus priority measures is warranted.

Bus accident analysis of routes with/without bus priority

This paper summarises findings on road safety performance and bus-involved accidents in Melbourne along roads where bus priority measures had been applied. Results from an empirical analysis of the accident types revealed significant reduction in the proportion of accidents involving buses hitting stationary objects and vehicles, which suggests the effect of bus priority in addressing manoeuvrability issues for buses. A mixed-effects negative binomial (MENB) regression and back-propagation neural network (BPNN) modelling of bus accidents considering wider influences on accident rates at a route section level also revealed significant safety benefits when bus priority is provided. Sensitivity analyses done on the BPNN model showed general agreement in the predicted accident frequency between both models. The slightly better performance recorded by the MENB model results suggests merits in adopting a mixed effects modelling approach for accident count prediction in practice given its capability to account for unobserved location and time-specific factors. A major implication of this research is that bus priority in Melbournes context acts to improve road safety and should be a major consideration for road management agencies when implementing bus priority and road schemes.

Safety impacts of platform tram stops on pedestrians in mixed traffic operation: A comparison group before-after crash study.

Tram stops in mixed traffic environments present a variety of safety, accessibility and transport efficiency challenges. In Melbourne, Australia the hundred year-old electric tram system is progressively being modernized to improve passenger accessibility. Platform stops, incorporating raised platforms for level entry into low floor trams, are being retro-fitted system-wide to replace older design stops. The aim of this study was to investigate the safety impacts of platform stops over older design stops (i.e. Melbourne safety zone tram stops) on pedestrians in the context of mixed traffic tram operation in Melbourne, using an advanced before-after crash analysis approach, the comparison group (CG) method. The CG method evaluates safety impacts by taking into account the general trends in safety and the unobserved factors at treatment and comparison sites that can alter the outcomes of a simple before-after analysis. The results showed that pedestrian-involved all injury crashes reduced by 43% after platform stop installation. This paper also explores a concern that the conventional CG method might underestimate safety impacts as a result of large differences in passenger stop use between treatment and comparison sites, suggesting differences in crash risk exposure. To adjust for this, a modified analysis explored crash rates (crash counts per 10,000 stop passengers) for each site. The adjusted results suggested greater reductions in pedestrian-involved crashes after platform stop installation: an 81% reduction in pedestrian-involved all injury crashes and 86% reduction in pedestrian-involved FSI crashes, both are significant at the 95% level. Overall, the results suggest that platform stops have considerable safety benefits for pedestrians. Implications for policy and areas for future research are explored.

Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia

Safety is a key concern in the design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyze crash frequency data obtained from Yarra Trams, the tram operator in Melbourne. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that group specific effects are randomly distributed across locations. The results identify many significant factors effecting tram-involved crash frequency including tram service frequency (2.71), tram stop spacing (-0.42), tram route section length (0.31), tram signal priority (-0.25), general traffic volume (0.18), tram lane priority (-0.15) and ratio of platform tram stops (-0.09). Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.

Geospatial Analysis of Cyclist Injury Trends: An Investigation in Melbourne, Australia

Objective: This study applied geospatial analysis to explore spatial trends in cycling-related injury in Melbourne, Australia, in order to identify an area where injury density was reducing against expectation. The crash characteristics and cycling environment of the identified area were examined to better understand factors related to cycling safety. Method: Two methods were used to examine spatial trends in cycling-related injury. Firstly, cycling injury density was calculated using a kernel density estimation method for the years 2000 to 2011. This was used to examine patterns in injury density across Melbourne over an extended time period. Secondly, absolute change in injury density was calculated between 2005 and 2011. From this, a geographical area presenting a reduced injury density was selected for a case study, and crash characteristics of the area were obtained for the observational period. This led to discussion on which changes to the cycling environment, if any, may be associated with the reduced injury rate. Results: Injury density in Melbourne had been progressively increasing between 2000 and 2011, with a nearly 3-fold increase in the peak injury density over that period. Decreases were observed in some locations between 2005 and 2011, and a geographical area to the southeast of Melbourne experienced a more significant decrease than others. This appeared to be associated with a combination of behavior and road infrastructure change, although a lack of data to verify change in cycling exposure prevented more definitive associations from being established. Conclusion: The apparent positive response of the injury rate to behavior and road infrastructure interventions is promising, yet the injury rate is unlikely to achieve the governments road safety target of 30% reduction in serious injuries by 2022. Moreover, the number of injuries sustained at the most common crash location appears to be increasing. Further research is necessary to discern which specific features of the urban road infrastructure have an effect on the risk of injury to a cyclist and which combination of features is consistent with a safe cycling environment.