Role of traffic in atmospheric accumulation of heavy metals and polycyclic aromatic hydrocarbons

Traffic related emissions have been recognised as one of the main sources of air pollutants. In the research study discussed in this paper, variability of atmospheric total suspended particulate matter (TSP), polycyclic aromatic hydrocarbons (PAH) and heavy metal (HM) concentrations with traffic and land use characteristics during weekdays and weekends were investigated. Data required for the study were collected from a range of sampling sites to ensure a wide mix of traffic and land use characteristics. The analysis undertaken confirmed that zinc has the highest concentration in the atmospheric phase during weekends as well as weekdays. Although the use of leaded gasoline was discontinued a decade ago, lead was the second most commonly detected heavy metal. This is attributed to the association of previously generated lead with roadside soil and re-suspension to the atmosphere. Soil related particles are the primary source of TSP and manganese to the atmosphere. The analysis further revealed that traffic sources are dominant in gas phase PAHs compared to the other sources during weekdays. Land use related sources become important contributors to atmospheric PAHs during weekends when traffic sources are at their minimal levels.

Study on road traffic noise prediction model taking into account the citywide network

For the further noise reduction in the future, the traffic management which controls traffic flow and physical distribution is important. To conduct the measure by the traffic management effectively, it is necessary to apply the model for predicting the traffic flow in the citywide road network. For this purpose, the existing model named AVENUE was used as a macro-traffic flow prediction model. The traffic flow model was integrated with the road vehicles' sound power model, and the new road traffic noise prediction model was established. By using this prediction model, the noise map of entire city can be made. In this study, first, the change of traffic flow on the road network after the establishment of new roads was estimated, and the change of the road traffic noise caused by the new roads was predicted. As a result, it has been found that this prediction model has the ability to estimate the change of noise map by the traffic management. In addition, the macro-traffic flow model and our conventional micro-traffic flow model were combined, and the coverage of the noise prediction model was expanded.

A behavioural car-following model that captures traffic oscillations

This paper presents a behavioral car-following model based on empirical trajectory data that is able to reproduce the spontaneous formation and ensuing propagation of stop-and-go waves in congested traffic. By analyzing individual drivers’ car-following behavior throughout oscillation cycles it is found that this behavior is consistent across drivers and can be captured by a simple model. The statistical analysis of the model’s parameters reveals that there is a strong correlation between driver behavior before and during the oscillation, and that this correlation should not be ignored if one is interested in microscopic output. If macroscopic outputs are of interest, simulation results indicate that an existing model with fewer parameters can be used instead. This is shown for traffic oscillations caused by rubbernecking as observed in the US 101 NGSIM dataset. The same experiment is used to establish the relationship between rubbernecking behavior and the period of oscillations.

On selecting an optimal wavelet for detecting singularities in traffic and vehicular data

Serving as a powerful tool for extracting localized variations in non-stationary signals, applications of wavelet transforms (WTs) in traffic engineering have been introduced; however, lacking in some important theoretical fundamentals. In particular, there is little guidance provided on selecting an appropriate WT across potential transport applications. This research described in this paper contributes uniquely to the literature by first describing a numerical experiment to demonstrate the shortcomings of commonly-used data processing techniques in traffic engineering (i.e., averaging, moving averaging, second-order difference, oblique cumulative curve, and short-time Fourier transform). It then mathematically describes WT’s ability to detect singularities in traffic data. Next, selecting a suitable WT for a particular research topic in traffic engineering is discussed in detail by objectively and quantitatively comparing candidate wavelets’ performances using a numerical experiment. Finally, based on several case studies using both loop detector data and vehicle trajectories, it is shown that selecting a suitable wavelet largely depends on the specific research topic, and that the Mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data.

The role of cultural beliefs in contributing to risky road use behaviour in Pakistan

Background Pakistan has the highest population rate of road fatalities in South Asia (25.3 fatalities per 100,000 people: Global Status Report on Road Safety, WHO 2009). Along with road environment and vehicle factors, human factors make a substantial contribution to traffic safety in Pakistan. Beliefs about road crash causation and prevention have been demonstrated to contribute to risky road use behaviour and resistance to preventive measures in a handful of other developing countries, but has not been explored in Pakistan. In particular, fatalism (whether based on religion, other cultural beliefs or experience) has been highlighted as a barrier to achieving changes in attitudes and behaviour. Aims The research reported here aimed (i) to explore perceptions of road crash causation among policy makers, police officers, professional drivers and car drivers in Pakistan; (ii) to identify how cultural and religious beliefs influence road use behaviour in Pakistan; and (iii) to understand how fatalistic beliefs may work as obstacles to road safety interventions. Methods In-depth interviews were conducted by the primary author (mostly in Urdu) in Lahore, Rawalpindi and Islamabad with 12 professional drivers (taxi, bus and truck), 4 car drivers, 6 police officers, 4 policy makers and 2 religious orators. All but two were Muslim, two were female, and they were drawn from a wide range of ages (24 to 60) and educational backgrounds. The interviews were taped and transcribed, then translated into English and analysed for themes related to the aims. Results Fatalism emerged as a pervasive belief utilised to justify risky road use behaviour and to resist messages about preventive measures. There was a strong religious underpinning to the statement of fatalistic beliefs (this reflects popular conceptions of Islam rather than scholarly interpretations), but also an overlap with superstitious beliefs which have longer-standing roots in Pakistani culture. These beliefs were not limited to people of poor educational background or position. A particular issue which was explored in more detail was the way in which these beliefs and their interpretation within Pakistani society contributed to poor police reporting of crashes. Discussion and conclusions The pervasive nature of fatalistic beliefs in Pakistan affects road user behaviour by supporting continued risk taking behaviour on the road, and by interfering with public health messages about behaviours which would reduce the risk of traffic crashes. The widespread influence of these beliefs on the ways that people respond to traffic crashes and the death of family members contribute to low crash reporting rates and to a system which is difficult to change. The promotion of an evidence-based approach to road user behaviour is recommended, along with improved professional education for police and policy makers.

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.

Navigational traffic conflict technique: a proactive approach to quantitative measurement of collision risks in port waters

Navigational safety analysis relying on collision statistics is often hampered because of low number of observations. A promising alternative approach that overcomes this problem is proposed in this paper. By analyzing critical vessel interactions this approach proactively measures collision risk in port waters. The proposed method is illustrated for quantitative measurement of collision risks in Singapore port fairways, and validated by examining correlations between the measured risks with those perceived by pilots. This method is an ethically appealing alternative to the collision-based analysis for fast, reliable and effective safety assessment, thus possesses great potential for managing collision risks in port waters.

Modelling port water collision risk using traffic conflicts

Navigational collisions are one of the major safety concerns for many seaports. Despite the extent of work recently done on collision risk analysis in port waters, little is known about the influencing factors of the risk. This paper develops a technique for modeling collision risks in port waterways in order to examine the associations between the risks and the geometric, traffic, and regulatory control characteristics of waterways. A binomial logistic model, which accounts for the correlations in the risks of a particular fairway at different time periods, is derived from traffic conflicts and calibrated for the Singapore port fairways. Estimation results show that the fairways attached to shoreline, traffic intersection and international fairway attribute higher risks, whereas those attached to confined water and local fairway possess lower risks. Higher risks are also found in the fairways featuring higher degree of bend, lower depth of water, higher numbers of cardinal and isolated danger marks, higher density of moving ships and lower operating speed. The risks are also found to be higher for night-time conditions.

“They’re lunatics on the road” : Exploring the normative influences of parents, friends, and police on young novices’ risky driving decisions

Purpose: Young novice drivers experience significantly greater risk of being injured or killed in car crashes than older more experienced drivers. This research utilised a qualitative approach guided by the framework of Akers’ social learning theory. It explored young novice drivers’ perspectives on risky driving including rewards and punishments expected from and administered by parents, friends, and police, imitation of parents’ and friends’ driving, and advantages and disadvantages of risky driving. Methods: Twenty-one young drivers (12 females, 9 males) aged 16–25 years (M = 17.71 years, SD = 2.15) with a Learner (n = 11) or Provisional (n = 10) driver licence participated in individual or small group interviews. Findings and conclusions: Content analysis supported four themes: (1) rewards and (2) punishments for risky driving, and the influence of (3) parents and (4) friends. The young novice drivers differed in their vulnerability to the negative influences of friends and parents, with some novices advising they were able to resist risky normative influences whilst others felt they could not. The authority of the police as enforcers of road rules was either accepted and respected or seen as being used to persecute young novices. These findings suggest that road safety interventions should consider the normative influence of parents and friends on the risky and safe behaviour of young novices. Police were also seen as influential upon behaviour. Future research should explore the complicated relationship between parents, friends, the police, young novices, and their risky driving behaviour.

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.

Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using Intra-class Correlation Coefficient (ICC) and Deviance Information Criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time, in good street lighting condition, involving pedestrian injuries are associated with a lower severity, while those in night time, at T/Y type intersections, on right-most lane, and installed with red light camera have larger odds of being severe. Moreover, heavy vehicles have a better resistance on severe crash, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

Proactive management of collision risks in port waters using navigational traffic conflicts

Navigational collisions are one of the major safety concerns in many seaports. To address this safety concern, a comprehensive and structured method of collision risk management is necessary. Traditionally management of port water collision risks has been relied on historical collision data. However, this collision-data-based approach is hampered by several shortcomings, such as randomness and rarity of collision occurrence leading to obtaining insufficient number of samples for a sound statistical analysis, insufficiency in explaining collision causation, and reactive approach to safety. A promising alternative approach that overcomes these shortcomings is the navigational traffic conflict technique that uses traffic conflicts as an alternative to the collision data. This paper proposes a collision risk management method by utilizing the principles of this technique. This risk management method allows safety analysts to diagnose safety deficiencies in a proactive manner, which, consequently, has great potential for managing collision risks in a fast, reliable and efficient manner.