The crash and offence involvement of speeding offenders

Overview: The role of speeding in crashes and contributing factors to the behaviour The need to better understand speeding offenders Characteristics of low-range, mid-range and high-range offenders Links to other offending behaviour Implications for speed management policies and practices

Priorities for developing and evaluating data quality characteristics of road crash data in Australia

The National Road Safety Strategy 2011-2020 outlines plans to reduce the burden of road trauma via improvements and interventions relating to safe roads, safe speeds, safe vehicles, and safe people. It also highlights that a key aspect in achieving these goals is the availability of comprehensive data on the issue. The use of data is essential so that more in-depth epidemiologic studies of risk can be conducted as well as to allow effective evaluation of road safety interventions and programs. Before utilising data to evaluate the efficacy of prevention programs it is important for a systematic evaluation of the quality of underlying data sources to be undertaken to ensure any trends which are identified reflect true estimates rather than spurious data effects. However, there has been little scientific work specifically focused on establishing core data quality characteristics pertinent to the road safety field and limited work undertaken to develop methods for evaluating data sources according to these core characteristics. There are a variety of data sources in which traffic-related incidents and resulting injuries are recorded, which are collected for a variety of defined purposes. These include police reports, transport safety databases, emergency department data, hospital morbidity data and mortality data to name a few. However, as these data are collected for specific purposes, each of these data sources suffers from some limitations when seeking to gain a complete picture of the problem. Limitations of current data sources include: delays in data being available, lack of accurate and/or specific location information, and an underreporting of crashes involving particular road user groups such as cyclists. This paper proposes core data quality characteristics that could be used to systematically assess road crash data sources to provide a standardised approach for evaluating data quality in the road safety field. The potential for data linkage to qualitatively and quantitatively improve the quality and comprehensiveness of road crash data is also discussed.

On the significance of omitted variables in intersection crash modeling

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causes and contributing factors—rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools—representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.

Empirical analysis on relationship between traffic conditions and crash occurrences

This paper investigates relationship between traffic conditions and the crash occurrence likelihood (COL) using the I-880 data. To remedy the data limitations and the methodological shortcomings suffered by previous studies, a multiresolution data processing method is proposed and implemented, upon which binary logistic models were developed. The major findings of this paper are: 1) traffic conditions have significant impacts on COL at the study site; Specifically, COL in a congested (transitioning) traffic flow is about 6 (1.6) times of that in a free flow condition; 2)Speed variance alone is not sufficient to capture traffic dynamics’ impact on COL; a traffic chaos indicator that integrates speed, speed variance, and flow is proposed and shows a promising performance; 3) Models based on aggregated data shall be interpreted with caution. Generally, conclusions obtained from such models shall not be generalized to individual vehicles (drivers) without further evidences using high-resolution data and it is dubious to either claim or disclaim speed kills based on aggregated data.

The prevalence and crash involvement of unlicensed drivers in Queensland

- Road safety implications of unlicensed driving - Present results from two studies conducted in Queensland examining: - the crash involvement of unlicensed drivers and the risks associated with the behaviour - the prevalence of unlicensed driving using a roadside survey method - Countermeasure options

Theoretical perspectives on motorcycle training: reducing risk taking

Little past empirical support has been found for the efficacy of motorcycle rider training as a road safety countermeasure. However, it has been argued that rider training should focus more particularly on the psychosocial factors that influence risk taking behaviour in addition to the traditional practice of developing vehicle-handling skills. This paper examines how rider training to reduce risk taking could be guided by appropriate theories. Two fundamental perspectives are examined: firstly training can be considered in terms of behaviour change, and secondly in terms of adult learning. Whilst behaviour change theories assume some pre-existing level of dysfunctional behaviour, an adult learning perspective does not necessarily carry this assumption. This distinction in perspectives conceptually aligns with the notions of intervention and prevention (respectively), with possible implications for specific target groups for pre-licence and post-licence training. The application of the Theory of Reasoned Action (Ajzen & Fishbein, 1975, 1980) and Transformative Learning Theory (Mezirow, 1997) to a pre-licence rider training program in Queensland, Australia is discussed.

Examining Exposure of Motorcycles at Signalized Intersections

Crash statistics in Singapore from 2001 to 2005 have shown that motorcycles are involved in about 54% of intersection crashes. The overall involvement of motorcycles in crashes as the not-at-fault party is about 43% but at intersections, the corresponding percentage is increased to 57%. Quasi-induced exposure estimates show that the motorcycle exposure rate at signalized intersections is 41.7% even though motorcycles account for only 19% of the vehicle population. This study seeks to examine in greater details, the problem of motorcycle exposure at signalized intersections. In particular, the exposure arising from potential crashes with red light running vehicles from the conflicting stream at four signalized intersections is investigated. The results show that motorcycles are more exposed because they tend to accumulate near the stop-line during the red phase to facilitate an earlier discharge during the initial period of the green which is the more vulnerable period. At sites where there are more weaving opportunities because the lanes are wider or where there are exclusive right-turn lanes, the accumulation is higher and hence an increased exposure is observed. The analysis also shows that the presence of heavy vehicles tends to decrease motorcycle exposure as their weaving opportunities become restricted as well as there is a greater reluctance for them to weave past or queue alongside the heavy vehicles and their effects intensify for narrower lane width.

Modeling fault among motorcyclists involved in crashes

Singapore crash statistics from 2001 to 2006 show that the motorcyclist fatality and injury rates per registered vehicle are higher than those of other motor vehicles by 13 and 7 times respectively. The crash involvement rate of motorcyclists as victims of other road users is also about 43%. The objective of this study is to identify the factors that contribute to the fault of motorcyclists involved in crashes. This is done by using the binary logit model to differentiate between at-fault and not-at-fault cases and the analysis is further categorized by the location of the crashes, i.e., at intersections, on expressways and at non-intersections. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Time trend effect shows that not-at-fault crash involvement of motorcyclists has increased with time. The likelihood of night time crashes has also increased for not-at-fault crashes at intersections and expressways. The presence of surveillance cameras is effective in reducing not-at-fault crashes at intersections. Wet road surfaces increase at-fault crash involvement at non-intersections. At intersections, not-at-fault crash involvement is more likely on single lane roads or on median lane of multi-lane roads, while on expressways at-fault crash involvement is more likely on the median lane. Roads with higher speed limit have higher at-fault crash involvement and this is also true on expressways. Motorcycles with pillion passengers or with higher engine capacity have higher likelihood of being at-fault in crashes on expressways. Motorcyclists are more likely to be at-fault in collisions involving pedestrians and this effect is higher at night. In multi-vehicle crashes, motorcyclists are more likely to be victims than at fault. Young and older riders are more likely to be at-fault in crashes than middle-aged group of riders. The findings of this study will help to develop more targeted countermeasures to improve motorcycle safety and more cost-effective safety awareness program in motorcyclist training.

Empirical evaluation of alternative approaches in identifying crash hot spots

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard EB ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate-related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag 1 dependent specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways of T intersections increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red-light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

Effects of impulsive sensation seeking, aggression and risk-taking behavior on the vulnerability of motorcyclists

While extensive research efforts have been devoted to improve the motorcycle safety, the relationship between the rider behavior and the crash risk is still not well understood.The objective of this study is to evaluate how behavioral factors influence crash risk and to identify the most vulnerable group of motorcyclists. To explore the rider behavior, a questionnaire containing 61-items of impulsive sensation seeking, aggression, and risk-taking behavior was developed. By clustering the crash risk using the medoid portioning algorithm, the log-linear model relating the rider behavior to crash risk has been developed. Results show that crash-involved motorcyclists score higher in all three behavioral traits. Aggressive and high risk-taking motorcyclists are more likely to fall under the high vulnerable group while impulsive sensation seeking is not found to be significant. Defining personality types from aggression and risk-taking behavior, “Extrovert” and “Follower” personality type of motorcyclists are more prone to crashes. The findings of this study will be useful for road safety campaign planners to be more focused in the target group as well as those who employ motorcyclists for their delivery business

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

Motorcycle safety at signalized intersections

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. ‘Motorcycle safety at signalized intersections’ provides an in-depth understanding of hazards of motorcycles at signalized intersections and suggests some targeted countermeasures to alleviate the problem. Motorcycle safety has been examined by establishing innovative statistical models with detailed investigation on specific maneuver behavior of motorcycles at intersections. Results indicate that motorcyclists are over exposed at signalized intersections and the excess exposure is one of main contributors to their vulnerability at intersections. The presence of red light cameras at intersections not only lower motorcycle crashes due to reduced red-light running in the conflicting stream but also decrease their exposure and hence crashes due to a more restrained discharge pattern of motorcycles. Targeted countermeasures should include deployment of red-light cameras, improvement in motorcyclist visibility and increased awareness on motorcyclist vulnerability.

The increased popularity of mopeds and motor scooters : exploring usage patterns and safety outcomes

Increased use of powered two-wheelers (PTWs) often underlies increases in the number of reported crashes, promoting research into PTW safety. PTW riders are overrepresented in crash and injury statistics relative to exposure and, as such, are considered vulnerable road users. PTW use has increased substantially over the last decade in many developed countries. One such country is Australia, where moped and scooter use has increased at a faster rate than motorcycle use in recent years. Increased moped use is particularly evident in the State of Queensland which is one of four Australian jurisdictions where moped riding is permitted for car licence holders and a motorcycle licence is not required. A moped is commonly a small motor scooter and is limited to a maximum design speed of 50 km/h and a maximum engine cylinder capacity of 50 cubic centimetres. Scooters exceeding either of these specifications are classed as motorcycles in all Australian jurisdictions. While an extensive body of knowledge exists on motorcycle safety, some of which is relevant to moped and scooter safety, the latter PTW types have received comparatively little focused research attention. Much of the research on moped safety to date has been conducted in Europe where they have been popular since the mid 20th century, while some studies have also been conducted in the United States. This research is of limited relevance to Australia due to socio-cultural, economic, regulatory and environmental differences. Moreover, while some studies have compared motorcycles to mopeds in terms of safety, no research to date has specifically examined the differences and similarities between mopeds and larger scooters, or between larger scooters and motorcycles. To address the need for a better understanding of moped and scooter use and safety, the current program of research involved three complementary studies designed to achieve the following aims: (1) develop better knowledge and understanding of moped and scooter usage trends and patterns; and (2) determine the factors leading to differences in moped, scooter and motorcycle safety. Study 1 involved six-monthly observations of PTW types in inner city parking areas of Queensland’s capital city, Brisbane, to monitor and quantify the types of PTW in use over a two year period. Study 2 involved an analysis of Queensland PTW crash and registration data, primarily comparing the police-reported crash involvement of mopeds, scooters and motorcycles over a five year period (N = 7,347). Study 3 employed both qualitative and quantitative methods to examine moped and scooter usage in two components: (a) four focus group discussions with Brisbane-based Queensland moped and scooter riders (N = 23); and (b) a state-wide survey of Queensland moped and scooter riders (N = 192). Study 1 found that of the PTW types parked in inner city Brisbane over the study period (N = 2,642), more than one third (36.1%) were mopeds or larger scooters. The number of PTWs observed increased at each six-monthly phase, but there were no significant changes in the proportions of PTW types observed across study phases. There were no significant differences in the proportions or numbers of PTW type observed by season. Study 2 revealed some important differences between mopeds, scooters and motorcycles in terms of safety and usage through analysis of crash and registration data. All Queensland PTW registrations doubled between 2001 and 2009, but there was an almost fifteen-fold increase in moped registrations. Mopeds subsequently increased as a proportion of Queensland registered PTWs from 1.2 percent to 8.8 percent over this nine year period. Moped and scooter crashes increased at a faster rate than motorcycle crashes over the five year study period from July 2003 to June 2008, reflecting their relatively greater increased usage. Crash rates per 10,000 registrations for the study period were only slightly higher for mopeds (133.4) than for motorcycles and scooters combined (124.8), but estimated crash rates per million vehicle kilometres travelled were higher for mopeds (6.3) than motorcycles and scooters (1.7). While the number of crashes increased for each PTW type over the study period, the rate of crashes per 10,000 registrations declined by 40 percent for mopeds compared with 22 percent for motorcycles and scooters combined. Moped and scooter crashes were generally less severe than motorcycle crashes and this was related to the particular crash characteristics of the PTW types rather than to the PTW types themselves. Compared to motorcycle and moped crashes, scooter crashes were less likely to be single vehicle crashes, to involve a speeding or impaired rider, to involve poor road conditions, or to be attributed to rider error. Scooter and moped crashes were more likely than motorcycle crashes to occur on weekdays, in lower speed zones and at intersections. Scooter riders were older on average (39) than moped (32) and motorcycle (35) riders, while moped riders were more likely to be female (36%) than scooter (22%) or motorcycle riders (7%). The licence characteristics of scooter and motorcycle riders were similar, with moped riders more likely to be licensed outside of Queensland and less likely to hold a full or open licence. The PTW type could not be identified in 15 percent of all cases, indicating a need for more complete recording of vehicle details in the registration data. The focus groups in Study 3a and the survey in Study 3b suggested that moped and scooter riders are a heterogeneous population in terms of demographic characteristics, riding experience, and knowledge and attitudes regarding safety and risk. The self-reported crash involvement of Study 3b respondents suggests that most moped and scooter crashes result in no injury or minor injury and are not reported to police. Study 3 provided some explanation for differences observed in Study 2 between mopeds and scooters in terms of crash involvement. On the whole, scooter riders were older, more experienced, more likely to have undertaken rider training and to value rider training programs. Scooter riders were also more likely to use protective clothing and to seek out safety-related information. This research has some important practical implications regarding moped and scooter use and safety. While mopeds and scooters are generally similar in terms of usage, and their usage has increased, scooter riders appear to be safer than moped riders due to some combination of superior skills and safer riding behaviour. It is reasonable to expect that mopeds and scooters will remain popular in Queensland in future and that their usage may further increase, along with that of motorcycles. Future policy and planning should consider potential options for encouraging moped riders to acquire better riding skills and greater safety awareness. While rider training and licensing appears an obvious potential countermeasure, the effectiveness of rider training has not been established and other options should also be strongly considered. Such options might include rider education and safety promotion, while interventions could also target other road users and urban infrastructure. Future research is warranted in regard to moped and scooter safety, particularly where the use of those PTWs has increased substantially from low levels. Research could address areas such as rider training and licensing (including program evaluations), the need for more detailed and reliable data (particularly crash and exposure data), protective clothing use, risks associated with lane splitting and filtering, and tourist use of mopeds. Some of this research would likely be relevant to motorcycle use and safety, as well as that of mopeds and scooters.