Chinese driver perceptions about speeding and traffic law enforcement

The World Health Organisation has highlighted the urgent need to address the escalating global public health crisis associated with road trauma. Low-income and middle-income countries bear the brunt of this, and rapid increases in private vehicle ownership in these nations present new challenges to authorities, citizens, and researchers alike. The role of human factors in the road safety equation is high. In China, human factors have been implicated in more than 90% of road crashes, with speeding identified as the primary cause (Wang, 2003). However, research investigating the factors that influence driving speeds in China is lacking (WHO, 2004). To help address this gap, we present qualitative findings from group interviews conducted with 35 Beijing car drivers in 2008. Some themes arising from data analysis showed strong similarities with findings from highly-motorised nations (e.g., UK, USA, and Australia) and include issues such as driver definitions of ‘speeding’ that appear to be aligned with legislative enforcement tolerances, factors relating to ease/difficulty of speed limit compliance, and the modifying influence of speed cameras. However, unique differences were evident, some of which, to our knowledge, are previously unreported in research literature. Themes included issues relating to an expressed lack of understanding about why speed limits are necessary and a perceived lack of transparency in traffic law enforcement and use of associated revenue. The perception of an unfair system seemed related to issues such as differential treatment of certain drivers and the large amount of individual discretion available to traffic police when administering sanctions. Additionally, a wide range of strategies to overtly avoid detection for speeding and/or the associated sanctions were reported. These strategies included the use of in-vehicle speed camera detectors, covering or removing vehicle licence number plates, and using personal networks of influential people to reduce or cancel a sanction. These findings have implications for traffic law, law enforcement, driver training, and public education in China. While not representative of all Beijing drivers, we believe that these research findings offer unique insights into driver behaviour in China.

Social influences on Chinese drivers : cultural context and traffic law enforcement

Reducing road crashes and associated trauma is a critical focus as the Decade of Action for Road Safety commences. China is one of many rapidly-motorizing nations to experience recent increases in private-vehicle ownership and an associated escalation in novice drivers. Unfortunately, however, China also experiences a high rate of death and injury from road crashes. Several key pieces of legislation have been introduced in recent decades in China to deal with these changes. While managing the legal aspects of road use is important, social influences on driver behaviour may offer additional avenues for promoting safe driving, particularly in a culture where such factors carry high importance. To date, there is limited research on the role of social influence factors on driver behaviour in China, yet we know that Chinese society is strongly based on social rules, customs, and relationships. There is reason to assume therefore, that road use and driving-related issues may also be strongly influenced by social relationships. One previous study that has investigated such issues highlighted the need to consider culturally-specific issues such as interpersonal networks and social hierarchy when examining driver behaviour in China (Xie & Parker, 2002). Those authors suggested that there are some concepts relating to Chinese driving culture that may not necessarily have been identified from research conducted in western contexts and that research conducted in China must be considered in light of such concepts. The current paper reports qualitative research conducted with Beijing drivers to investigate such social influence factors. Findings indicated that family members, friends, and driving instructors appear influential on driver behaviour and that some novice drivers seek additional assistance after obtaining their licence. The finding relating to the influence of driving instructors was not surprising, given the substantial number of new drivers in China. In Beijing, driving instruction is conducted off-road in purpose-specific driving facilities rather than on the road network. Once licensed, it is common for new drivers to have little or no experience driving in complex traffic situations. This learning situation is unlikely to provide all the skills necessary to successfully negotiate crowded city streets and assess the related risk associated with such driving. Therefore, it was not surprising to find that one reported strategy to assist new drivers was to employ the services of an ‘accompanying driver’ to provide ongoing driving instruction once licensed. In more highly motorised countries supervised practice is part of a graduated licensing system where it is compulsory for new drivers to be supervised by a more experienced driver for a requisite period of time before progressing to solo driving. However, as this system is not in place in China, it appears that some drivers seek out and pay for additional support once they commence on-road driving. Additionally, strategies to avoid detection and penalties for inappropriate road use were discussed, many of which involve the use of a third person. These findings indicate potential barriers to implementing effective traffic enforcement and highlight the importance of understanding culturally-specific social factors relating to driver behaviour.

Impact of urban traffic and climate change on water quality from road runoff

Urban traffic and climate change are two phenomena that have the potential to degrade urban water quality by influencing the build-up and wash-off of pollutants, respectively. However, limited knowledge has made it difficult to establish any link between pollutant buildup and wash-off under such dynamic conditions. In order to safeguard urban water quality, adaptive water quality mitigation measures are required. In this research, pollutant build-up and wash-off have been investigated from a dynamic point of view which incorporated the impacts of changed urban traffic as well as changes in the rainfall characteristics induced by climate change. The study has developed a dynamic object classification system and thereby, conceptualised the study of pollutant build-up and wash-off under future changes in urban traffic and rainfall characteristics. This study has also characterised the buildup and wash-off processes of traffic generated heavy metals, volatile, semi-volatile and non-volatile hydrocarbons under dynamic conditions which enables the development of adaptive mitigation measures for water quality. Additionally, predictive frameworks for the build-up and wash-off of some pollutants have also been developed.

An examination of the microscopic simulation models to identify traffic safety indicators

Evaluating the safety of different traffic facilities is a complex and crucial task. Microscopic simulation models have been widely used for traffic management but have been largely neglected in traffic safety studies. Micro simulation to study safety is more ethical and accessible than the traditional safety studies, which only assess historical crash data. However, current microscopic models are unable to mimic unsafe driver behavior, as they are based on presumptions of safe driver behavior. This highlights the need for a critical examination of the current microscopic models to determine which components and parameters have an effect on safety indicator reproduction. The question then arises whether these safety indicators are valid indicators of traffic safety. The safety indicators were therefore selected and tested for straight motorway segments in Brisbane, Australia. This test examined the capability of a micro-simulation model and presents a better understanding of micro-simulation models and how such models, in particular car following models can be enriched to present more accurate safety indicators.

A review of road traffic noise indicators and their correlation with the LA10(18hour)

This paper outlines a study to determine the correlation between the LA10(18hour) and other road traffic noise indicators. It is based on a database comprising of 404 measurement locations including 947 individual days of valid noise measurements across numerous circumstances taken between November 2001 and November 2007. This paper firstly discusses the need and constraints on the indicators and their nature of matching a suitable indicator to the various road traffic noise dynamical characteristics. The paper then presents a statistical analysis of the road traffic noise monitoring data, correlating various indicators with the LA10(18hour) statistical indicator and provides a comprehensive table of linear correlations. There is an extended analysis on relationships across the night time period. The paper concludes with a discussion on the findings.

Raising awareness of traffic pollution: the potential benefits and problems of using a warning smell

Exposure to traffic pollution is increasing worldwide as people move to cities, and as more vehicles join the roads, creating longer journeys and more traffic jams. Most traffic pollutants are odourless and invisible, which hides exposure from the public. If traffic pollution had a distinctive smell it would enable people to avoid exposure, and increase the political will for difficult policy changes. A smell may also instigate longer-term changes, such as switching to active transport for school pick-ups. A smell could be added using a fuel additive or a temporary device attached to vehicle exhausts.

Fuelling traffic : abolitionist claims of a causal nexus between legalised prostitution and trafficking

Over the last decade, researchers and legislators have struggled to get an accurate picture of the scale and nature of the problem of human trafficking. In the absence of reliable data, some anti-prostitution activists have asserted that a causal relationship exists between legalised prostitution and human trafficking. They claim that systems of legalised or decriminalised prostitution lead to increases in trafficking into the sex industry. This paper critically analyses attempts to substantiate this claim during the development of anti-trafficking policy in Australia and the United States. These attempts are explored within the context of persistent challenges in measuring the scale and nature of human trafficking. The efforts of abolitionist campaigners to use statistical evidence and logical argumentation are analysed, with a specific focus on the characterisation of demand for sexual services and systems of legalised prostitution as ‘pull’ factors fuelling an increase in sex trafficking. The extent to which policymakers sought to introduce evidence-based policy is also explored.

Considering weather in simulation traffic

The impact of weather on traffic and its behavior is not well studied in literature primarily due to lack of integrated traffic and weather data. Weather can significant effect the traffic and traffic management measures developed for fine weather might not be optimal for adverse weather. Simulation is an efficient tool for analyzing traffic management measures even before their actual implementation. Therefore, in order to develop and test traffic management measures for adverse weather condition we need to first analyze the effect of weather on fundamental traffic parameters and thereafter, calibrate the simulation model parameters in order to simulate the traffic under adverse weather conditions. In this paper we first, analyses the impact of weather on motorway traffic flow and drivers’ behaviour with traffic data from Swiss motorways and weather data from MeteoSuisse. Thereafter, we develop methodology to calibrate a microscopic simulation model with the aim to utilize the simulation model for simulating traffic under adverse weather conditions. Here, study is performed using AIMSUN, a microscopic traffic simulator.

Evolution of traffic management measures on road traffic noise

Road traffic noise affects the quality of life in the areas adjoining the road. The effect of traffic noise on people is wide ranging and may include sleep disturbance and negative impact on work efficiency. To address the problem of traffic noise, it is necessary to estimate the noise level. For this, a number of noise estimation models have been developed which can estimate noise at the receptor points, based on simple configuration of buildings. However, for a real world situation we have multiple buildings forming built-up area. In such a situation, it is almost impossible to consider multiple diffractions and reflections in sound propagation from the source to the receptor point. An engineering solution to such a real world problem is needed to estimate noise levels in built-up area.

Prediction of the wash-off of traffic related semi- and non-volatile organic compounds from urban roads under climate change influenced rainfall characteristics

Traffic generated semi and non volatile organic compounds (SVOCs and NVOCs) pose a serious threat to human and ecosystem health when washed off into receiving water bodies by stormwater. Climate change influenced rainfall characteristics makes the estimation of these pollutants in stormwater quite complex. The research study discussed in the paper developed a prediction framework for such pollutants under the dynamic influence of climate change on rainfall characteristics. It was established through principal component analysis (PCA) that the intensity and durations of low to moderate rain events induced by climate change mainly affect the wash-off of SVOCs and NVOCs from urban roads. The study outcomes were able to overcome the limitations of stringent laboratory preparation of calibration matrices by extracting uncorrelated underlying factors in the data matrices through systematic application of PCA and factor analysis (FA). Based on the initial findings from PCA and FA, the framework incorporated orthogonal rotatable central composite experimental design to set up calibration matrices and partial least square regression to identify significant variables in predicting the target SVOCs and NVOCs in four particulate fractions ranging from >300-1 μm and one dissolved fraction of <1 μm. For the particulate fractions range >300-1 μm, similar distributions of predicted and observed concentrations of the target compounds from minimum to 75th percentile were achieved. The inter-event coefficient of variations for particulate fractions of >300-1 μm were 5% to 25%. The limited solubility of the target compounds in stormwater restricted the predictive capacity of the proposed method for the dissolved fraction of <1 μm.

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.

Benefits and issues of urban network travel time prediction

This paper presents the benefits and issues related to travel time prediction on urban network. Travel time information quantifies congestion and is perhaps the most important network performance measure. Travel time prediction has been an active area of research for the last five decades. The activities related to ITS have increased the attention of researchers for better and accurate real-time prediction of travel time. Majority of the literature on travel time prediction is applicable to freeways where, under non-incident conditions, traffic flow is not affected by external factors such as traffic control signals and opposing traffic flows. On urban environment the problem is more complicated due to conflicting areas (intersections), mid-link sources and sinks etc. and needs to be addressed.

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.