A Tobit model for analyzing speed limit compliance in work zones

Poor compliance with speed limits is a serious safety concern in work zones. Most studies of work zone speeds have focused on descriptive analyses and statistical testing without systematically capturing the effects of vehicle and traffic characteristics. Consequently, little is known about how the characteristics of surrounding traffic and platoons influence speeds. This paper develops a Tobit regression technique for innovatively modeling the probability and the magnitude of non-compliance with speed limits at various locations in work zones. Speed data is transformed into two groups—continuous for non-compliant and left-censored for compliant drivers—to model in a Tobit model framework. The modeling technique is illustrated using speed data from three long-term highway work zones in Queensland, Australia. Consistent and plausible model estimates across the three work zones support the appropriateness and validity of the technique. The results show that the probability and magnitude of speeding was higher for leaders of platoons with larger front gaps, during late afternoon and early morning, when traffic volumes were higher, and when higher proportions of surrounding vehicles were non-compliant. Light vehicles and their followers were also more likely to speed than others. Speeding was more common and greater in magnitude upstream than in the activity area, with higher compliance rates close to the end of the activity area and close to stop/slow traffic controllers. The modeling technique and results have great potential to assist in deployment of appropriate countermeasures by better identifying the traffic characteristics associated with speeding and the locations of lower compliance.

Investigations on road noise level spatial variability within a specially designed acoustic balcony

An investigation into the spatial distribution of road traffic noise levels on a balcony is conducted. A balcony constructed to a special acoustic design due to its elevation above an 8 lane motorway is selected for detailed measurements. The as-constructed balcony design includes solid parapets, side walls, ceiling shields and highly absorptive material placed on the ceiling. Road traffic noise measurements are conducted spatially using a five channel acoustic analyzer, where four microphones are located at various positions within the balcony space and one microphone placed outside the parapet at a reference position. Spatial distributions in both vertical and horizontal planes are measured. A theoretical model and prediction configuration is presented that assesses the acoustic performance of the balcony under existing traffic flow conditions. The prediction model implements a combined direct path, specular reflection path and diffuse reflection path utilizing image source and radiosity techniques. Results obtained from the prediction model are presented and compared to the measurement results. The predictions are found to correlate well with measurements with some minor differences that are explained. It is determined that the prediction methodology is acceptable to assess a wider range of street and balcony configuration scenarios.

Incorporation of road safety into road management systems

Road collisions negatively affect the lives of hundreds of Canadians per year. Unfortunately, safety has been typically neglected from management systems. It is common to find that a great deal of effort has been devoted to develop and implement systems capable of achieving and sustaining good levels of condition. It is relatively recent that road safety has become an important objective. Managing a network of roads is not an easy task; it requires long, medium and short term plans to maintain, rehabilitate and upgrade aging assets, reduce and mitigate accident exposure, likelihood and severity. This thesis presents a basis for incorporating road safety into road management systems; two case studies were developed; one limited by available data and another from sufficient information. A long term analysis was used to allocate improvements for condition and safety of roads and bridges, at the network level. It was confirmed that a safety index could be used to obtain a first cut model; meanwhile potential for improvement which is a difference between observed and predicted number of accidents was capable of capturing the degree of safety of individual segments. It was found that the completeness of the system resulted in savings because of the economies obtained from trade-off optimization. It was observed that safety improvements were allocated at the beginning of the analysis in order to reduce the extent of issues, which translated into a systematic reduction of potential for improvement up to a point of near constant levels, which were hypothesized to relate to those unavoidable collisions from human error or vehicle failure.

Traffic safety at road-rail level crossings using a driving simulator and traffic simulation

A number of Intelligent Transportation Systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions namely, Video in-vehicle (ITS1), Audio in-vehicle (ITS2), and On-road flashing marker (ITS3) were tested. Then, the results from the driving simulator were used as inputs for a developed model using a traffic micro-simulation (Vissim 5.4) in order to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through a number of active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of driver behavior changing in terms of speed and compliance rate was greater at passive crossings than at active crossings. The difference in speed of drivers approaching ITS devices was very small which indicates that ITS helps drivers encounter the crossings in a safer way. Since the current traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varies based on different ITS safety devices, some modifications of the current traffic simulation were conducted. The results showed that exposure to ITS devices at active crossings did not influence the drivers’ behavior significantly according to the traffic performance indicators used, such as delay time, number of stops, speed, and stopped delay. On the other hand, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

The applicability of the Precaution Adoption Process Model for understanding self-regulatory driving behaviour among older drivers

1. Introduction The success of self-regulation, in terms of enhancing older drivers’ safety and maintaining their mobility, depends largely upon older drivers’ awareness of the declines in their driving abilities. Therefore, interventions targeted at increasing older drivers’ safety should aim to enhance their awareness of their physical, sensory and cognitive limitations. Moreover, previous research suggests that driving behaviour change may occur through stages and that interventions and feedback may be perceived differently at each stage. 2. Study aims To further understand the process of driving self-regulation among older adults by exploring their perceptions and experiences of self-regulation, using the PAPM as a framework. To investigate the possible impact of feedback on their driving on their decision making process. 3. Methodology Research tool: Qualitative focus groups (n=5 sessions) Recruitment: Posters, media, newspaper advertisement and emails Inclusion criteria: Aged 70 or more, English-speaking, current drivers Participants: Convenience sample of 27 men and women aged 74 to 90 in the Sunshine Coast and Brisbane city, Queensland, Australia. 4. Analysis Thematic analysis was conducted following the process outlined by Braun and Clarke (2006) to identify, analyse and report themes within the data. Four main themes were identified.

Source characterisation of urban road surface pollutants for enhanced water quality predictions

This study developed a comprehensive research methodology for identification and quantification of sources responsible for pollutant build-up and wash-off from urban road surfaces. The study identified soil and asphalt wear, and non-combusted diesel fuel as the most influential sources for metal and hydrocarbon pollution respectively. The study also developed mathematical models to relate contributions from identified sources to underlying site specific factors such as land use and traffic. Developed mathematical model will play a key role in urban planning practices, enabling the implementation of effective water pollution control strategies.

Are we there yet? Australian road safety targets and road traffic crash fatalities

Background Road safety targets are widely used and provide a basis for evaluating progress in road safety outcomes against a quantified goal. In Australia, a reduction in fatalities from road traffic crashes (RTCs) is a public policy objective: a national target of no more than 5.6 fatalities per 100,000 population by 2010 was set in 2001. The purpose of this paper is to examine the progress Australia and its states and territories have made in reducing RTC fatalities, and to estimate when the 2010 target may be reached by the jurisdictions. Methods Following a descriptive analysis, univariate time-series models estimate past trends in fatality rates over recent decades. Data for differing time periods are analysed and different trend specifications estimated. Preferred models were selected on the basis of statistical criteria and the period covered by the data. The results of preferred regressions are used to determine out-of-sample forecasts of when the national target may be attained by the jurisdictions. Though there are limitations with the time series approach used, inadequate data precluded the estimation of a full causal/structural model. Results Statistically significant reductions in fatality rates since 1971 were found for all jurisdictions with the national rate decreasing on average, 3% per year since 1992. However the gains have varied across time and space, with percent changes in fatality rates ranging from an 8% increase in New South Wales 1972-1981 to a 46% decrease in Queensland 1982-1991. Based on an estimate of past trends, it is possible that the target set for 2010 may not be reached nationally, until 2016. Unsurprisingly, the analysis indicated a range of outcomes for the respective state/territory jurisdictions though these results should be interpreted with caution due to different assumptions and length of data. Conclusions Results indicate that while Australia has been successful over recent decades in reducing RTC mortality, an important gap between aspirations and achievements remains. Moreover, unless there are fairly radical ("trend-breaking") changes in the factors that affect the incidence of RTC fatalities, deaths from RTCs are likely to remain above the national target in some areas of Australia, for years to come.

Process variability of pollutant build-up on urban road surfaces

Knowledge of the pollutant build-up process is a key requirement for developing stormwater pollution mitigation strategies. In this context, process variability is a concept which needs to be understood in-depth. Analysis of particulate build-up on three road surfaces in an urban catchment confirmed that particles <150µm and >150µm have characteristically different build-up patterns, and these patterns are consistent over different field conditions. Three theoretical build-up patterns were developed based on the size-fractionated particulate build-up patterns, and these patterns explain the variability in particle behavior and the variation in particle-bound pollutant load and composition over the antecedent dry period. Behavioral variability of particles <150µm was found to exert the most significant influence on the build-up process variability. As characterization of process variability is particularly important in stormwater quality modeling, it is recommended that the influence of behavioral variability of particles <150µm on pollutant build-up should be specifically addressed. This would eliminate model deficiencies in the replication of the build-up process and facilitate the accounting of the inherent process uncertainty, and thereby enhance the water quality predictions.

Injury narrative text classification using factorization model

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.

Road operation phase sustainability indicator as a response to the climate change phenomena

Road infrastructure is a major contributor of greenhouse gas (GHG) around the world. Once constructed, a road becomes a part of a road network and is subjected to recurrent maintenance/rehabilitation activities. Studies to date are mostly aimed at the development of sustainability indicators that deal with the material and construction phases of a road when it is constructed. The operation phase is infrequently studied and there is a need for sustainability indicators to be developed relating to this phase to better understand the GHG emissions as a proper response to the climate change phenomena. During the operation phase, maintenance/rehabilitation activities are undertaken based on certain agreed intervention criteria that do not include environmental implications relating to the climate change aspect properly. Availability of appropriate indicators may, therefore, assist in sustainable road asset maintenance management. This paper presents the findings of a literature based study and has proposed a way forward to develop a key “road operation phase” environmental indicator, which can contribute to road operation phase carbon footprint management based on a comprehensive road life cycle system boundary model. The proposed indicator can address multiple aspects of high impact road operation life environmental components such as: pavement rolling resistance, albedo, material, traffic congestion and lighting, based on availability of relevant scientific knowledge. Development of the indicator to appropriate level would offset the impacts of these components significantly and contribute to sustainable road operation management.

Pedestrian Crashes in Ethiopia: Identification of contributing factors through modelling of exposure and road environment variables

Pedestrian safety is a critical issue in Ethiopia. Reports show that 50 to 60% of traffic fatality victims in the country are pedestrians. The primary aim of this research was to examine the possible causes of and contributing factors to crashes with pedestrians in Ethiopia, and improve pedestrian safety by recommending possible countermeasures. The secondary aim was to develop appropriate pedestrian crash models for two-way two-lane rural roads and roundabouts in the capital city of Ethiopia. This research uses quantitative methods throughout the process of the investigation. The research has applied various statistical methods. The results of this research support the idea that geometric and operational features have significant influence on pedestrian safety and crashes. Accordingly, policies and strategies are needed to safeguard pedestrians in Ethiopia.

Revisiting the Task-Capability interface model for incorporating human factors into car-following models

Human factors such as distraction, fatigue, alcohol and drug use are generally ignored in car-following (CF) models. Such ignorance overestimates driver capability and leads to most CF models’ inability in realistically explaining human driving behaviors. This paper proposes a novel car-following modeling framework by introducing the difficulty of driving task measured as the dynamic interaction between driving task demand and driver capability. Task difficulty is formulated based on the famous Task Capability Interface (TCI) model, which explains the motivations behind driver’s decision making. The proposed method is applied to enhance two popular CF models: Gipps’ model and IDM, and named as TDGipps and TDIDM respectively. The behavioral soundness of TDGipps and TDIDM are discussed and their stabilities are analyzed. Moreover, the enhanced models are calibrated with the vehicle trajectory data, and validated to explain both regular and human factor influenced CF behavior (which is distraction caused by hand-held mobile phone conversation in this paper). Both the models show better performance than their predecessors, especially in presence of human factors.

Characteristics of road factors in multi and single vehicle motorcycle crashes in Queensland

Motorcyclists were involved in 6.4% of all police-reported crashes and 12.5% of all fatal crashes in Queensland during 2004-2011. Of these crashes, 43% were single-vehicle (SV) and 57% were multi-vehicle (MV). The overall reduction in motorcycle crashes in this period masked different trends: single-vehicle crashes increased while MV motorcycle crashes decreased. However, little research has been undertaken to understand the similarities and differences between SV and MV motorcycle crashes in Queensland and the factors underlying these diverging trends. The descriptive analyses and regression model developed here confirm international research findings regarding the greater role of road infrastructure factors in SV crashes. In particular, road geometric factors such as horizontal and vertical alignment and road surface factors such as sealed/unsealed and wet/dry were more important in SV than MV crashes.

Influence of road traffic environment and mobile phone distraction on the speed selection behaviour of young drivers

Travel speed is one of the most critical parameters for road safety; the evidence suggests that increased vehicle speed is associated with higher crash risk and injury severity. Both naturalistic and simulator studies have reported that drivers distracted by a mobile phone select a lower driving speed. Speed decrements have been argued to be a risk compensatory behaviour of distracted drivers. Nonetheless, the extent and circumstances of the speed change among distracted drivers are still not known very well. As such, the primary objective of this study was to investigate patterns of speed variation in relation to contextual factors and distraction. Using the CARRS-Q high-fidelity Advanced Driving Simulator, the speed selection behaviour of 32 drivers aged 18-26 years was examined in two phone conditions: baseline (no phone conversation) and handheld phone operation. The simulator driving route contained five different types of road traffic complexities, including one road section with a horizontal S curve, one horizontal S curve with adjacent traffic, one straight segment of suburban road without traffic, one straight segment of suburban road with traffic interactions, and one road segment in a city environment. Speed deviations from the posted speed limit were analysed using Ward’s Hierarchical Clustering method to identify the effects of road traffic environment and cognitive distraction. The speed deviations along curved road sections formed two different clusters for the two phone conditions, implying that distracted drivers adopt a different strategy for selecting driving speed in a complex driving situation. In particular, distracted drivers selected a lower speed while driving along a horizontal curve. The speed deviation along the city road segment and other straight road segments grouped into a different cluster, and the deviations were not significantly different across phone conditions, suggesting a negligible effect of distraction on speed selection along these road sections. Future research should focus on developing a risk compensation model to explain the relationship between road traffic complexity and distraction.

ASEAN and ADB implementing new approaches to building road safety capacity

Following the Association of Southeast Asian Nations (ASEAN) senior transport officials meeting in May 2011, the Secretariat requested the Asian Development Bank (ADB) to provide assistance to improve road safety in ASEAN. In response, ADB, funded by the Japan Fund for Poverty Reduction, has begun an innovative approach to capacity building that has already been adapted and replicated in other sub-regions. This paper will discuss the model central to the project. The Road Safety Capacity Building for ASEAN Project commenced in May 2013. Each country has appointed a National Focal Point (NFP) to identify and coordinate information. A team of International Experts were appointed to develop materials and present a comprehensive train the trainer program focused on five key areas. Thirty eight senior Government officers from across ASEAN attended a two week program at ADB headquarters in Manila and will arrange and deliver specific training and associated activities to other colleagues within their country. ADB has appointed a National Consultant to work in partnership with the trainees on a range of activities including development of “pipeline project proposals” for funding consideration investors and donors. As part of the project, a draft ASEAN Regional Road Safety Strategy document has been prepared and consultation will further refine its directions and contents. The project will reach its conclusion in 2015 and a follow up phase three project is being considered.