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.

Loop closure detection on a suburban road network using a continuous appearance-based trajectory

This paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or performing 3D map construction. For loop closure detection CAT-SLAM updates in constant time regardless of map size. We evaluate the effectiveness of CAT-SLAM on a 16km outdoor road network and determine its loop closure performance relative to FAB-MAP. CAT-SLAM recognizes 3 times the number of loop closures for the case where no false positives occur, demonstrating its potential use for robust loop closure detection in large environments.

Performance testing of road bridge deck containing flat rail wagons

A road bridge containing disused flatbed rail wagons as the primary deck superstructure was performance tested in a low volume, high axle load traffic road in Queensland, Australia; some key results are presented in this paper. A fully laden truck of total weight 28.88 % of the serviceability design load prescribed in the Australian bridge code was used; its wheel positions were accurately captured using a high speed camera and synchronised with the real‐time deflections and strains measured at the critical members of the flat rail wagons. The strains remained well below the yield and narrated the existence of composite action between the reinforced concrete slab pavement and the wagon deck. A three dimensional grillage model was developed and calibrated using the test data, which established the structural adequacy of the rail wagons and the positive contribution of the reinforced concrete slab pavement to resist high axle traffic loads on a single lane bridge in the low volume roads network.

A Delphi study on the critical sustainability criteria and indicators for Australian road infrastructure projects

With increasing media exposure and evidence of environmental impacts, it is increasingly recognized that incorporating sustainability principles in construction works is both crucial and beneficial. However a recent survey reveals that among stakeholders of infrastructure projects such as roads, there is no common understanding on what constitutes sustainability in real-life projects. Sustainability has been interpreted widely and differently and as a result, sustainability outcomes are not tangible at the project level or often neglected. Under such conditions, policies and strategies on sustainability remain largely ideological and cannot be sufficiently reflected in the actual project delivery. The major difficulty of this sustainability pursuit lies in the lack of consensus among the experts on sustainability criteria and indicators. To move ahead, these criteria and indicators are to be agreed upon. This paper reviews the sustainable infrastructure development, its criteria and indicators, focusing on road infrastructure context. It goes on to introduce a Delphi study, an integral part of a QUT research, aimed at identifying critical sustainability criteria and indicators for Australian road infrastructure projects. It paves the way for further identification of solutions for each critical indicator at a subsequent stage. The criteria, indicators and solutions will be encapsulated into a decision making framework for the enhancement of sustainability deliverables. By doing so, the research will promote more integrated thinking of and consistent approaches to the sustainability agenda in road and highway infrastructure projects in Australia.

Phenomenography : “Follow the yellow brick road!”

Imagine that is you holding the glass, and the separate larger picture of the glass to the left, is how much wine is inside the glass you are holding. Now, first of all, no tricks, truthfully answer the following question...

What do we currently know about designing and evaluating road safety advertising?

Overview: What we currently know - content design and evaluation The direct role (persuasive effects) of advertising Review of some key findings within a conceptual framework of the persuasive process Definitional inconsistencies, methodological limitations, & gaps in existing knowledge Suggested issues/directions for future advertising research

Young, inexperienced and on the road : do novice drivers comply with road rules?

The graduated driver licensing (GDL) program in Queensland, Australia, was considerably enhanced in July 2007. This paper explores the compliance of young Learner and Provisional (intermediate) drivers with current GDL requirements and general road rules. Unsupervised driving, Learner logbook accuracy, and experiences of punishment avoidance were explored, along with speeding as a Provisional driver. Participants (609 females; M = 17.43 years) self-reported sociodemographic characteristics, driving behaviours and licensing experiences as Learners. A subset of participants (238 females, 105 males) completed another survey six months later exploring their Provisional behaviours and experiences. While the majority of the participants reported compliance with both the GDL requirements and general road rules such as stopping at red lights on their Learner licence; a considerable proportion reported speeding. Furthermore, they reported becoming less compliant during the Provisional phase, particularly with speed limits. Self-reported speeding was predicted by younger age at licensure, being in a relationship, driving unsupervised, submitting inaccurate Learner logbooks, and speeding as a Learner. Enforcement and education countermeasures should focus upon curtailing noncompliance, targeting speeding in particular. Novice drivers should be encouraged to comply with all road rules, including speed limits, and safe driving behaviours should be developed and reinforced during the Learner and early Provisional periods. Novice drivers have been found to model their parents’ driving, and parents are pivotal in regulating novice driving. It is vital young novice drivers and parents alike are encouraged to comply with all road rules, including GDL requirements.

Perceptions of visibility and conspicuity of biomotion clothing configurations for road workers at road work sites

Aims: This study determined whether the visibility benefits of positioning retroreflective strips in biological motion configurations were evident at real world road worker sites. Methods: 20 visually normal drivers (M=40.3 years) participated in this study that was conducted at two road work sites (one suburban and one freeway) on two separate nights. At each site, four road workers walked in place wearing one of four different clothing options: a) standard road worker night vest, b) standard night vest plus retroreflective strips on thighs, c) standard night vest plus retroreflective strips on ankles and knees, d) standard night vest plus retroreflective strips on eight moveable joints (full biomotion). Participants seated in stationary vehicles at three different distances (80m, 160m, 240m) rated the relative conspicuity of the four road workers using a series of a standardized visibility and ranking scales. Results: Adding retroreflective strips in the full biomotion configuration to the standard night vest significantly (p<0.001) enhanced perceptions of road worker visibility compared to the standard vest alone, or in combination with thigh retroreflective markings. These visibility benefits were evident at all distances and at both sites. Retroreflective markings at the ankles and knees also provided visibility benefits compared to the standard vest, however, the full biomotion configuration was significantly better than all of the other configurations. Conclusions: These data provide the first evidence that the benefits of biomotion retroreflective markings that have been previously demonstrated under laboratory and closed- and open-road conditions are also evident at real work sites.

An object oriented road asset information model

Preservation and enhancement of transportation infrastructure is critical to continuous economic development in Australia. Of particular importance are the road assets infrastructure, due to their high costs of setting up and their social and economic impact on the national economy. Continuous availability of road assets, however, is contingent upon their effective design, condition monitoring, maintenance, and renovation and upgrading. However, in order to achieve this data exchange, integration, and interoperability is required across municipal boundaries. On the other hand, there are no agreed reference frameworks that consistently describe road infrastructure assets. As a consequence, specifications and technical solutions being chosen to manage road assets do not provide adequate detail and quality of information to support asset lifecycle management processes and decisions taken are based on perception not reality. This paper presents a road asset information model, which works as reference framework to, link other kinds of information with asset information; integrate different data suppliers; and provide a foundation for service driven integrated information framework for community infrastructure and asset management.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

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.

Project 2.7 Leveraging R&D Investment for the Australian built environment : Phase 2 Case Study Report Part 2 - Road Construction Safety

This report discusses findings of a case study into "Road Construction Safety" undertaken as a part of the retrospective analysis component of Sustainable Built Environment National Research Centre (SBEnrc) Project 2.7 Leveraging R&D investment for the Australian Built Environment. The Queensland Department of Transport and Main Roads (QTMR) has taken a leadership role in developing a safer working environment for road construction workers. In the past decades, a range of initiatives have been introduced to contribute to improved performance in this area. Several initiatives have been undertaken by QTMR as part of their overarching commitment to safety. Three such initiatives form the basis for this case study investigation, in order to better illustrate the nature of R&D investment and its impact on day-to-day operations and the supply chain. These are the development and implementation of: (i) the Mechanical Traffic Aid: (ii) the Thermal Imaging Camera; and (iii) the Trailer-based CCTV (camera). This case study should be read in conjunction with Part 1 of this suite of reports.

Vulnerable road user groups : a review of younger drivers, motorcyclists and older drivers

Scientific efforts to understand and reduce the occurrence of road crashes continue to expand, particularly in the areas of vulnerable road user groups. Three groups that are receiving increasing attention within the literature are younger drivers, motorcyclists and older drivers. These three groups are at an elevated risk of being in a crash or seriously injured, and research continues to focus on the origins of this risk as well as the development of appropriate countermeasures to improve driving outcomes for these cohorts. However, it currently remains unclear what factors produce the largest contribution to crash risk or what countermeasures are likely to produce the greatest long term positive effects on road safety. This paper reviews research that has focused on the personal and environmental factors that increase crash risk for these groups as well as considers direction for future research in the respective areas. A major theme to emerge from this review is that while there is a plethora of individual and situational factors that influence the likelihood of crashes, these factors often combine in an additive manner to exacerbate the risk of both injury and fatality. Additionally, there are a number of risk factors that are pertinent for all three road user groups, particularly age and the level of driving experience. As a result, targeted interventions that address these factors are likely to maximise the flow-on benefits to a wider range of road users. Finally, there is a need for further research that aims to bridge the research-to-practice gap, in order to develop appropriate pathways to ensure that evidenced-based research is directly transferred to effective policies that improve safety outcomes.

Role of solids in heavy metals build-up on urban road surfaces

Solids are widely identified as a carrier of harmful pollutants in stormwater runoff exerting a significant risk to receiving waters. This paper outlines the findings of an in-depth investigation on heavy metal adsorption to solids surfaces. Pollutant build-up samples collected from sixteen road sites in residential, industrial and commercial land uses were separated into four particle size ranges and analysed for a range of physico-chemical parameters and nine heavy metals including Iron (Fe), Aluminum (Al), Lead (Pb), Zinc (Zn), Cadmium (Cd), Chromium (Cr), Manganese (Mn), Nickel (Ni) and Copper (Cu). High specific surface area (SSA) and total organic carbon (TOC) content in finer particle size ranges was noted, thus confirming strong correlations with heavy metals. Based on their physico-chemical characteristics, two different types of solids originating from traffic and soil sources were identified. Solids generated by traffic were associated with high loads of heavy metals such as Cd and Cr with strong correlation with SSA. This suggested the existence of surface dependent bonds such as cation exchange between heavy metals and solids. In contrast, Fe, Al and Mn which can be attributed to soil inputs showed strong correlation with TOC suggesting strong bonds such as chemsorption. Zn was found to be primarily attached to solids by bonding with the oxides of Fe, Al and Mn. The data analysis also confirmed the predominance of the finer fraction, with 70% of the solids being finer than 150 µm and containing 60% of the heavy metal pollutant load.

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.