Mobile phone use and driving : the message is just not getting through

Previous research has shown that mobile phone use while driving can increase crash risk fourfold while texting results in 23 times greater crash risk for heavy vehicle drivers. However, mobile phone use has changed in recent years with the functional capabilities of smart phones to now also include a range of other common behaviours while driving such as using Facebook, emailing, the use of ‘apps’, and GPS. Research continues to show performance decrements for many such behaviours while driving, however many Australians still openly admit to illegal mobile phone use while driving despite ongoing enforcement efforts and public awareness campaigns. Of most concern are young drivers. ‘Apps’ available to restrict mobile phone use while in motion do not prevent use while a driver is stopped at traffic lights, so are therefore not a wholly viable solution. Vehicle manufacturers continue to develop in-vehicle technology to minimise distraction, however communication with the ‘outside world’ while driving is also perhaps a strong selling point for vehicles. Hence, the safety message that drivers should focus on the driving task solely and not use communication devices is unlikely to ever be internalised by many drivers. This paper reviews the available literature on the topic and argues that a better understanding of perceptions of mobile phone use while driving and motives for use are required to inform public awareness campaign development for specific road user groups. Additionally, illegal phone use while driving may be reinforced by not being apprehended (punishment avoidance), therefore stronger deterrence-focussed messages may also be beneficial.

Traffic safety risks trends and patterns analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestion. Hence, reducing the frequency of crashes assist in addressing congestion issues (Meyer, 2008). Analysing traffic conditions and discovering risky traffic trends and patterns are essential basics in crash likelihood estimations studies and still require more attention and investigation. In this paper we will show, through data mining techniques, that there is a relationship between pre-crash traffic flow patterns and crash occurrence on motorways, compare them with normal traffic trends, and that this knowledge has the potentiality to improve the accuracy of existing crash likelihood estimation models, and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash occurrence. K-Means clustering algorithm applied to determine dominant pre-crash traffic patterns. In the first phase of this research, traffic regimes identified by analysing crashes and normal traffic situations using half an hour speed in upstream locations of crashes. Then, the second phase investigated the different combination of speed risk indicators to distinguish crashes from normal traffic situations more precisely. Five major trends have been found in the first phase of this paper for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Moreover, the second phase explains that spatiotemporal difference of speed is a better risk indicator among different combinations of speed related risk indicators. Based on these findings, crash likelihood estimation models can be fine-tuned to increase accuracy of estimations and minimize false alarms.

On the periodicity of traffic oscillations and capacity drop : the role of driver characteristics

This paper shows that traffic hysteresis, a manifestation of driver characteristics, has a profound impact on the development of traffic oscillations and the bottleneck discharge rate. Findings suggest that aggressive driver behavior (with small response times and jammed spacing) leads to spontaneous formations of stop-and-go disturbances. Furthermore, the aggressive behavior, coupled with a late response to adopt less aggressive behavior, generates large hysteresis that leads to oscillations’ transformation from localized to substantial disturbances and growth. The larger the magnitude of hysteresis is, the larger the growth is. Our finding also suggests that the bottleneck discharge rate can diminish by 8-23% when driver adopts a less aggressive reaction to a disturbance (characterized by a larger response time). This finding is particularly notable since lane-changes have been believed to be the major cause of a reduction in bottleneck discharge rate.

Observation of ions and particles near busy roads using a Neutral Cluster and Air Ion Spectrometer (NAIS)

Motor vehicles emit large quantities of ions in the form of both charged particles and molecular cluster ions. While, the health effects of inhalation of charged particles is largely unexplored, the concentrations near busy roads and the distance to which these particles and ions are carried have important implications for the exposure of the large percentage of the population that lives close to such roadways. We measured ion concentrations using a neutral cluster and air ion spectrometer (NAIS) near seven busy roads carrying on the average approximately 7000 vehicles hr-1 including about 15% heavy duty diesel vehicles. In this study, charged particle concentrations were measured as a function of downwind distance from the road for the first time. We show that, at a moderate wind speed of 2.0 m s-1, mean charged particle concentrations at the kerb were of the order of 2x104 cm-3 and, more importantly, decreased as d 0.6 where d is the distance from the road. While cluster ions were rapidly depleted by attachment to particles and were not carried to more than about 20 m from the road, elevated concentrations of charged particle were detected up to at least 400 m from the road. Most of the charge on the downwind side was carried on the larger particles, with no excess charge on particles smaller than about 10 nm. At 30 nm, particles carried more than double the charge they would normally carry in equilibrium. There are very few measurements of ions near road traffic and this is the first study of the spatial dispersion of charged particles from a road.

Determinants of drink-driving and association between drink-driving and road traffic fatalities in Ghana

Background Drink-driving has been implicated in many road traffic crashes in the world. Consequently, the developed countries have prioritized drink-driving research. Contrary, drink-driving research has not attained any meaningful consideration in many developing countries. It is therefore imperative to intensify drink-driving research so as to provide research driven solutions to the menace. Aims The objective is to establish determinants of drink-driving and its association with traffic crashes in Ghana. Methods A randomized roadside breathalyzer survey was conducted. A multivariable logistic regression was used to establish significant determinants of drink-driving and a bivariate logistic regression to establish the association between drink–driving and road traffic crashes in Ghana. Results In total, 2,736 motorists were randomly stopped for breath testing of whom 8.7% tested positive for alcohol. Among the total participants, 5.5% exceeded the legal BAC limit of 0.08%. Formal education is associated with a reduced likelihood of drink-driving compared with drivers without formal education. The propensity to drink-drive is 1.8 times higher among illiterate drivers compared with drivers with basic education. Young adult drivers also recorded elevated likelihoods for driving under alcohol impairment compared with adult drivers. The odds of drink-driving among truck drivers is OR=1.81, (95% CI=1.16 to 2.82) and two wheeler riders is OR=1.41, (95% CI=0.47 to 4.28) compared with car drivers. Contrary to general perception, commercial car drivers have a significant reduced likelihood of 41%, OR=0.59, (95% CI=0.38 to 0.92) compared with the private car driver. Bivariate analysis conducted showed a significant association between the proportion of drivers exceeding the legal BAC limit and road traffic fatalities, p<0.001. The model predicts a 1% increase in the proportion of drivers exceeding the legal BAC to be associated with a 4% increase in road traffic fatalities, 95% CI= 3% to 5% and vice versa. Discussion and conclusion A positive and significant association between roadside alcohol prevalence and road traffic fatality has been established. Scaling up roadside breath test, determining standard drink and disseminating to the populace and formulating policies targeting the youth such as increasing minimum legal drinking age and reduced legal BAC limit for the youth and novice drivers might improve drink-driving related crashes in Ghana.

The role of sleepiness, sleep disorders, and the work environment on heavy-vehicle crashes in 2 Australian states

Heavy-vehicle driving involves a challenging work environment and a high crash rate. We investigated the associations of sleepiness, sleep disorders, and work environment (including truck characteristics) with the risk of crashing between 2008 and 2011 in the Australian states of New South Wales and Western Australia. We conducted a case-control study of 530 heavy-vehicle drivers who had recently crashed and 517 heavy-vehicle drivers who had not. Drivers' crash histories, truck details, driving schedules, payment rates, sleep patterns, and measures of health were collected. Subjects wore a nasal flow monitor for 1 night to assess for obstructive sleep apnea. Driving schedules that included the period between midnight and 5:59 am were associated with increased likelihood of crashing (odds ratio = 3.42, 95% confidence interval: 2.04, 5.74), as were having an empty load (odds ratio = 2.61, 95% confidence interval: 1.72, 3.97) and being a less experienced driver (odds ratio = 3.25, 95% confidence interval: 2.37, 4.46). Not taking regular breaks and the lack of vehicle safety devices were also associated with increased crash risk. Despite the high prevalence of obstructive sleep apnea, it was not associated with the risk of a heavy-vehicle nonfatal, nonsevere crash. Scheduling of driving to avoid midnight-to-dawn driving and the use of more frequent rest breaks are likely to reduce the risk of heavy-vehicle nonfatal, nonsevere crashes by 2–3 times.

The use of acoustic indices to determine avian species richness in audio-recordings of the environment

Interpreting acoustic recordings of the natural environment is an increasingly important technique for ecologists wishing to monitor terrestrial ecosystems. Technological advances make it possible to accumulate many more recordings than can be listened to or interpreted, thereby necessitating automated assistance to identify elements in the soundscape. In this paper we examine the problem of estimating avian species richness by sampling from very long acoustic recordings. We work with data recorded under natural conditions and with all the attendant problems of undefined and unconstrained acoustic content (such as wind, rain, traffic, etc.) which can mask content of interest (in our case, bird calls). We describe 14 acoustic indices calculated at one minute resolution for the duration of a 24 hour recording. An acoustic index is a statistic that summarizes some aspect of the structure and distribution of acoustic energy and information in a recording. Some of the indices we calculate are standard (e.g. signal-to-noise ratio), some have been reported useful for the detection of bioacoustic activity (e.g. temporal and spectral entropies) and some are directed to avian sources (spectral persistence of whistles). We rank the one minute segments of a 24 hour recording in descending order according to an "acoustic richness" score which is derived from a single index or a weighted combination of two or more. We describe combinations of indices which lead to more efficient estimates of species richness than random sampling from the same recording, where efficiency is defined as total species identified for given listening effort. Using random sampling, we achieve a 53% increase in species recognized over traditional field surveys and an increase of 87% using combinations of indices to direct the sampling. We also demonstrate how combinations of the same indices can be used to detect long duration acoustic events (such as heavy rain and cicada chorus) and to construct long duration (24 h) spectrograms.

A methodology for estimating exposure-controlled crash risk using traffic Police crash data

Exposure control or case-control methodologies are common techniques for estimating crash risks, however they require either observational data on control cases or exogenous exposure data, such as vehicle-kilometres travelled. This study proposes an alternative methodology for estimating crash risk of road user groups, whilst controlling for exposure under a variety of roadway, traffic and environmental factors by using readily available police-reported crash data. In particular, the proposed method employs a combination of a log-linear model and quasi-induced exposure technique to identify significant interactions among a range of roadway, environmental and traffic conditions to estimate associated crash risks. The proposed methodology is illustrated using a set of police-reported crash data from January 2004 to June 2009 on roadways in Queensland, Australia. Exposure-controlled crash risks of motorcyclists—involved in multi-vehicle crashes at intersections—were estimated under various combinations of variables like posted speed limit, intersection control type, intersection configuration, and lighting condition. Results show that the crash risk of motorcycles at three-legged intersections is high if the posted speed limits along the approaches are greater than 60 km/h. The crash risk at three-legged intersections is also high when they are unsignalized. Dark lighting conditions appear to increase the crash risk of motorcycles at signalized intersections, but the problem of night time conspicuity of motorcyclists at intersections is lessened on approaches with lower speed limits. This study demonstrates that this combined methodology is a promising tool for gaining new insights into the crash risks of road user groups, and is transferrable to other road users.

Simulating the effect of acoustic treatment types for residential balconies with road traffic noise

Development of design guides to estimate the difference in speech interference level due to road traffic noise between a reference position and balcony position or façade position is explored. A previously established and validated theoretical model incorporating direct, specular and diffuse reflection paths is used to create a database of results across a large number of scenarios. Nine balcony types with variable acoustic treatments are assessed to provide acoustic design guidance on optimised selection of balcony acoustic treatments based on location and street type. In total, the results database contains 9720 scenarios on which multivariate linear regression is conducted in order to derive an appropriate design guide equation. The best fit regression derived is a multivariable linear equation including modified exponential equations on each of nine deciding variables, (1) diffraction path difference, (2) ratio of total specular energy to direct energy, (3) distance loss between reference position and receiver position, (4) distance from source to balcony façade, (5) height of balcony floor above street, (6) balcony depth, (7) height of opposite buildings, (8) diffusion coefficient of buildings, and; (9) balcony average absorption. Overall, the regression correlation coefficient, R2, is 0.89 with 95% confidence standard error of ±3.4 dB.

Traffic state estimation from partial Bluetooth and volume observations : case study in the Brisbane metropolitan area

The application of the Bluetooth (BT) technology to transportation has been enabling researchers to make accurate travel time observations, in freeway and arterial roads. The Bluetooth traffic data are generally incomplete, for they only relate to those vehicles that are equipped with Bluetooth devices, and that are detected by the Bluetooth sensors of the road network. The fraction of detected vehicles versus the total number of transiting vehicles is often referred to as Bluetooth Penetration Rate (BTPR). The aim of this study is to precisely define the spatio-temporal relationship between the quantities that become available through the partial, noisy BT observations; and the hidden variables that describe the actual dynamics of vehicular traffic. To do so, we propose to incorporate a multi- class traffic model into a Sequential Montecarlo Estimation algorithm. Our framework has been applied for the empirical travel time investigations into the Brisbane Metropolitan region.

Bayesian inference of traffic volumes based on Bluetooth data

The study of the relationship between macroscopic traffic parameters, such as flow, speed and travel time, is essential to the understanding of the behaviour of freeway and arterial roads. However, the temporal dynamics of these parameters are difficult to model, especially for arterial roads, where the process of traffic change is driven by a variety of variables. The introduction of the Bluetooth technology into the transportation area has proven exceptionally useful for monitoring vehicular traffic, as it allows reliable estimation of travel times and traffic demands. In this work, we propose an approach based on Bayesian networks for analyzing and predicting the complex dynamics of flow or volume, based on travel time observations from Bluetooth sensors. The spatio-temporal relationship between volume and travel time is captured through a first-order transition model, and a univariate Gaussian sensor model. The two models are trained and tested on travel time and volume data, from an arterial link, collected over a period of six days. To reduce the computational costs of the inference tasks, volume is converted into a discrete variable. The discretization process is carried out through a Self-Organizing Map. Preliminary results show that a simple Bayesian network can effectively estimate and predict the complex temporal dynamics of arterial volumes from the travel time data. Not only is the model well suited to produce posterior distributions over single past, current and future states; but it also allows computing the estimations of joint distributions, over sequences of states. Furthermore, the Bayesian network can achieve excellent prediction, even when the stream of travel time observation is partially incomplete.

How accurate is the identification of serious traffic injuries by Police? The concordance between Police and hospital reported traffic injuries

Police reported crash data are the primary source of crash information in most jurisdictions. However, the definition of serious injury within police-reported data is not consistent across jurisdictions and may not be accurate. With the Australian National Road Safety Strategy targeting the reduction of serious injuries, there is a greater need to assess the accuracy of the methods used to identify these injuries. A possible source of more accurate information relating to injury severity is hospital data. While other studies have compared police and hospital data to highlight the under-reporting in police-reported data, little attention has been given to the accuracy of the methods used by police to identify serious injuries. The current study aimed to assess how accurate the identification of serious injuries is in police-reported crash data, by comparing the profiles of transport-related injuries in the Queensland Road Crash Database with an aligned sample of data from the Queensland Hospital Admitted Patients Data Collection. Results showed that, while a similar number of traffic injuries were recorded in both data sets, the profile of these injuries was different based on gender, age, location, and road user. The results suggest that the ‘hospitalisation’ severity category used by police may not reflect true hospitalisations in all cases. Further, it highlights the wide variety of severity levels within hospitalised cases that are not captured by the current police-reported definitions. While a data linkage study is required to confirm these results, they highlight that a reliance on police-reported serious traffic injury data alone could result in inaccurate estimates of the impact and cost of crashes and lead to a misallocation of valuable resources.

Hospital outpatients' responses to taking medications with driving warnings

OBJECTIVE The study investigates the knowledge, intentions, and driving behavior of persons prescribed medications that display a warning about driving. It also examines their confidence that they can self-assess possible impairment, as is required by the Australian labeling system. METHOD We surveyed 358 outpatients in an Australian public hospital pharmacy, representing a well-advised group taking a range of medications including those displaying a warning label about driving. A brief telephone follow-up survey was conducted with a subgroup of the participants. RESULTS The sample had a median age of 53.2 years and was 53 percent male. Nearly three quarters (73.2%) had taken a potentially impairing class of medication and more than half (56.1%) had taken more than one such medication in the past 12 months. Knowledge of the potentially impairing effects of medication was relatively high for most items; however, participants underestimated the possibility of increased impairment from exceeding the prescribed dose and at commencing treatment. Participants' responses to the safety implications of taking drugs with the highest level of warning varied. Around two thirds (62.8%) indicated that they would consult a health practitioner for advice and around half would modify their driving in some way. However, one fifth (20.9%) would drive when the traffic was thought to be less heavy and over a third (37.7%) would modify their medication regime so that they could drive. The findings from the follow-up survey of a subsample taking target drugs at the time of the first interview were also of concern. Only just over half (51%) recalled seeing the warning label on their medications and, of this group, three quarters (78%) reported following the warning label advice. These findings indicated that there remains a large proportion of people who either did not notice or did not consider the warning when deciding whether to drive. There was a very high level of confidence in this group that they could determine whether they were personally affected by the medication, which may be a problem from a safety perspective. CONCLUSION This study involved persons who should have had a very high level of knowledge and awareness of medication warning labeling. Even in this group there was a lack of informed response to potential impairment. A review of the Australian warning system and wider dissemination of information on medication treatment effects would be useful. Clarifying the importance of potential risk in the general community context is recommended for consideration and further research.

Short-term traffic volume forecasting : a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm

To enhance the performance of the k-nearest neighbors approach in forecasting short-term traffic volume, this paper proposed and tested a two-step approach with the ability of forecasting multiple steps. In selecting k-nearest neighbors, a time constraint window is introduced, and then local minima of the distances between the state vectors are ranked to avoid overlappings among candidates. Moreover, to control extreme values’ undesirable impact, a novel algorithm with attractive analytical features is developed based on the principle component. The enhanced KNN method has been evaluated using the field data, and our comparison analysis shows that it outperformed the competing algorithms in most cases.