Understanding China : navigating the road ahead

This paper proposes an alternative way of understanding China’s emergence, drawing on the idea of the creative industries. It looks at China’s embrace of the idea of cultural and creative industries and argues that this paradigm demonstrates that structures of domination (i.e. the commanding heights of political economy exemplified in political economy of the media) are being replaced by geographical development initiatives, mostly led by local governments and councils. In this decentralisation of power we find the seedlings of a more open and democratic society.

Application of text mining in analysing road crashes for road asset management

Traffic safety is a major concern world-wide. It is in both the sociological and economic interests of society that attempts should be made to identify the major and multiple contributory factors to those road crashes. This paper presents a text mining based method to better understand the contextual relationships inherent in road crashes. By examining and analyzing the crash report data in Queensland from year 2004 and year 2005, this paper identifies and reports the major and multiple contributory factors to those crashes. The outcome of this study will support road asset management in reducing road crashes.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Reading and writing PE-files with PERWAPI

PERWAPI is a component for reading and writing .NET PE-files. The name is a compound acronym for Program Executable – Reader/Writer – Application Programming Interface. The code was written by one of us (Diane Corney) with some contributions from some of the early users of the tool. PERWAPI is a managed component, written entirely in safe C#. The design of the writer part of the component is loosely based on Diane Corney’s previous PEAPI component. It is open source software, and is released under a “FreeBSD-like” license. The source may be downloaded from “http://plas.fit.qut.edu.au/perwapi/” As of the date of this document the code has facilities for reading and writing PEfiles compatible with the latest (beta-2) release of the ”Whidbey” version of .NET, that is, the Visual Studio 2005 framework. An invocation option allows earlier versions of the framework to be targeted.

Developing an integrated decision model for the enhancement of sustainability deliverables for Australian road infrastructure projects

Road and highway infrastructure provides the backbone for a nation's economic growth. The versatile dispersion of population in Australia, from sparsely settled communities in remote areas to regenerated inner city suburbs with high density living in metropolitans, calls for continuing development and improvement on roads infrastructure under the current federal government policies and state governments' strategic plans. As road infrastructure projects involve large resources and mechanism, achieving sustainability not only in economic scales but also through environmental and social responsibility becomes a crucial issue. Current efforts are often impeded by different interpretation on sustainability agenda by stakeholders involved in these types of projects. As a result, sustainability deliverables at the project level is not often as transparent and measurable, compared to promises in project briefs and designs. This paper reviews the past studies on sustainable infrastructure construction, focusing on roads and highway projects. Through literature study and consultation with the industry, key sustainability indicators specific to road infrastructure projects have been identified. Based on these findings, this paper introduces an on-going research project aimed at identifying and integrating the different perceptions and priority needs of the stakeholders, and issues that impact on the gap between sustainability foci and its actual realization at project end level. The exploration helps generate an integrated decision-making model for sustainable road infrastructure projects. The research will promote to the industry more systematic and integrated approaches to decision-making on the implementation of sustainability strategies to achieve deliverable goals throughout the development and delivery process of road infrastructure projects in Australia.

Reading and writing PE-files with PERWAPI

PERWAPI is a component for reading and writing .NET PE-files. The name is a compound acronym for Program Executable – Reader/Writer – Application Programming Interface. The code was written by one of us (Diane Corney) with some contributions from some of the early users of the tool. PERWAPI is a managed component, written entirely in safe C#. The design of the writer part of the component is loosely based on Diane Corney’s previous PEAPI component. It is open source software, and is released under a “FreeBSD-like” license. The source may be downloaded from “http://perwapi.codeplex.com”. As of the date of this document the code has facilities for reading and writing PEfiles compatible with the V2 or later frameworks.

Mobility and safety are conflicting goals for transport policy makers when making decisions about graduated driver licensing

Policy decisions are frequently influenced by more than research results alone. This review examines one road safety countermeasure, graduated driver licensing, in three jurisdictions and identifies how the conflict between mobility and safety goals can influence policy decisions relating to this countermeasure. Evaluations from around the world of graduated driver licensing have demonstrated clear reductions in crashes for young drivers. However, the introduction of this countermeasure may be affected, both positively and negatively, by the conflict some policy makers experience between ensuring individuals remain both mobile and safe as drivers. This review highlights how this conflict in policy decision making can serve to either facilitate or hinder the introduction of graduated driver licensing systems. However, policy makers whose focus on mobility is too strong when compared with safety may be mistaken, with evidence suggesting that after a graduated driver licensing system is introduced young drivers adapt their behaviour to the new system and remain mobile. As a result, policy makers should consciously acknowledge the conflict between mobility and safety and consider an appropriate balance in order to introduce these systems. Improvements to the licensing system can then be made in an incremental manner as the balance between these two priorities change. Policy makers can achieve an appropriate balance by using empirical evidence as a basis for their decisions.

The call of the road : factors predicting students' car travelling intentions and behaviour

The most common daily trip for employed persons and students is the commute to and from work and/or place of study. Though there are clear environmental, health and safety benefits from using public transport instead of private vehicles for these trips, a high proportion of commuters still choose private vehicles to get to work or study. This study reports an investigation of psychological factors influencing students’ travel choices from the perspective of the Theory of Planned Behaviour (TPB). Students from 3 different university campuses (n= 186) completed a cross-sectional survey on their car commuting behaviour. Particular focus was given to whether car commuting habits could add to understanding of commuting behaviour over and above behavioural intentions. Results indicated that, as expected, behavioural intention to travel by car was the strongest TPB predictor of car commuting behaviour. Further, general car commuting habits explained additional variance over and above TPB constructs, though the contribution was modest. No relationship between habit and intentions was found. Overall results suggest that, although student car commuting behaviour is habitual in nature, it is predominantly guided by reasoned action. Implications of these findings are that in order to alter the use of private vehicles, the factors influencing commuters’ intentions to travel by car must be addressed. Specifically, interventions should target the perceived high levels of both the acceptability of commuting by car and the perceived control over the choice to commute by car.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Road network extraction with new vectorization and pruning from high-resolution RS images

With the increasing resolution of remote sensing images, road network can be displayed as continuous and homogeneity regions with a certain width rather than traditional thin lines. Therefore, road network extraction from large scale images refers to reliable road surface detection instead of road line extraction. In this paper, a novel automatic road network detection approach based on the combination of homogram segmentation and mathematical morphology is proposed, which includes three main steps: (i) the image is classified based on homogram segmentation to roughly identify the road network regions; (ii) the morphological opening and closing is employed to fill tiny holes and filter out small road branches; and (iii) the extracted road surface is further thinned by a thinning approach, pruned by a proposed method and finally simplified with Douglas-Peucker algorithm. Lastly, the results from some QuickBird images and aerial photos demonstrate the correctness and efficiency of the proposed process.