The effectiveness of using a simple ARIA based geographical classification to identify road crash patterns in rural and urban areas of Queensland

Research has noted a ‘pronounced pattern of increase with increasing remoteness' of death rates in road crashes. However, crash characteristics by remoteness are not commonly or consistently reported, with definitions of rural and urban often relying on proxy representations such as prevailing speed limit. The current paper seeks to evaluate the efficacy of the Accessibility / Remoteness Index of Australia (ARIA+) to identifying trends in road crashes. ARIA+ does not rely on road-specific measures and uses distances to populated centres to attribute a score to an area, which can in turn be grouped into 5 classifications of increasing remoteness. The current paper uses applications of these classifications at the broad level of Australian Bureau of Statistics' Statistical Local Areas, thus avoiding precise crash locating or dedicated mapping software. Analyses used Queensland road crash database details for all 31,346 crashes resulting in a fatality or hospitalisation occurring between 1st July, 2001 and 30th June 2006 inclusive. Results showed that this simplified application of ARIA+ aligned with previous definitions such as speed limit, while also providing further delineation. Differences in crash contributing factors were noted with increasing remoteness such as a greater representation of alcohol and ‘excessive speed for circumstances.' Other factors such as the predominance of younger drivers in crashes differed little by remoteness classification. The results are discussed in terms of the utility of remoteness as a graduated rather than binary (rural/urban) construct and the potential for combining ARIA crash data with census and hospital datasets.

Urban growth monitoring techniques for sustainable development

This paper discusses the role of advance techniques for monitoring urban growth and change for sustainable development of urban environment. It also presents results of a case study involving satellite data for land use/land cover classification of Lucknow city using IRS-1C multi-spectral features. Two classification algorithms have been used in the study. Experiments were conducted to see the level of improvement in digital classification of urban environment using Artificial Neural Network (ANN) technique.

Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

Sustainable urban infrastructure management : integration of urban computer modelling

Purpose - This paper seeks to examine the complex relationships between urban planning, infrastructure management, sustainable urban development, and to illustrate why there is an urgent need for local governments to develop a robust planning support system which integrates with advance urban computer modelling tools to facilitate better infrastructure management and improve knowledge sharing between the community, urban planners, engineers and decision makers. Design/methodology/approach - The methods used in this paper includes literature review and practical project case observations. Originality/value - This paper provides an insight of how the Brisbane's planning support system established by Brisbane City Council has significantly improved the effectiveness of urban planning, infrastructure management and community engagement through better knowledge management processes. Practical implications - This paper presents a practical framework for setting up a functional planning support system within local government. The integration of the Brisbane Urban Growth model, Virtual Brisbane and the Brisbane Economic Activity Monitoring (BEAM) database have proven initially successful to provide a dynamic platform to assist elected officials, planners and engineers to understand the limitations of the local environment, its urban systems and the planning implications on a city. With the Brisbane's planning support system, planners and decision makers are able to provide better planning outcomes, policy and infrastructure that adequately address the local needs and achieve sustainable spatial forms.

Empirical evaluation of an autonomous vehicle in an urban environment

Operation in urban environments creates unique challenges for research in autonomous ground vehicles. Due to the presence of tall trees and buildings in close proximity to traversable areas, GPS outage is likely to be frequent and physical hazards pose real threats to autonomous systems. In this paper, we describe a novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition. We report empirical results analyzing the performance of the vehicle while navigating a 560-meter test loop multiple times in an actual urban setting with severe GPS outage. We show that our system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization. Finally, we discuss ongoing efforts in fusing vision data with other sensing modalities.

Intelligent computing and sensing for active safety on construction sites

On obstacle-cluttered construction sites where heavy equipment is in use, safety issues are of major concern. The main objective of this paper is to develop a framework with algorithms for obstacle avoidance and path planning based on real-time three-dimensional job site models to improve safety during equipment operation. These algorithms have the potential to prevent collisions between heavy equipment vehicles and other on-site objects. In this study, algorithms were developed for image data acquisition, real-time 3D spatial modeling, obstacle avoidance, and shortest path finding and were all integrated to construct a comprehensive collision-free path. Preliminary research results show that the proposed approach is feasible and has the potential to be used as an active safety feature for heavy equipment.

Program your city : designing an urban integrated open data API

Cities accumulate and distribute vast sets of digital information. Many decision-making and planning processes in councils, local governments and organisations are based on both real-time and historical data. Until recently, only a small, carefully selected subset of this information has been released to the public – usually for specific purposes (e.g. train timetables, release of planning application through websites to name just a few). This situation is however changing rapidly. Regulatory frameworks, such as the Freedom of Information Legislation in the US, the UK, the European Union and many other countries guarantee public access to data held by the state. One of the results of this legislation and changing attitudes towards open data has been the widespread release of public information as part of recent Government 2.0 initiatives. This includes the creation of public data catalogues such as data.gov.au (U.S.), data.gov.uk (U.K.), data.gov.au (Australia) at federal government levels, and datasf.org (San Francisco) and data.london.gov.uk (London) at municipal levels. The release of this data has opened up the possibility of a wide range of future applications and services which are now the subject of intensified research efforts. Previous research endeavours have explored the creation of specialised tools to aid decision-making by urban citizens, councils and other stakeholders (Calabrese, Kloeckl & Ratti, 2008; Paulos, Honicky & Hooker, 2009). While these initiatives represent an important step towards open data, they too often result in mere collections of data repositories. Proprietary database formats and the lack of an open application programming interface (API) limit the full potential achievable by allowing these data sets to be cross-queried. Our research, presented in this paper, looks beyond the pure release of data. It is concerned with three essential questions: First, how can data from different sources be integrated into a consistent framework and made accessible? Second, how can ordinary citizens be supported in easily composing data from different sources in order to address their specific problems? Third, what are interfaces that make it easy for citizens to interact with data in an urban environment? How can data be accessed and collected?

An integrated approach to supporting land-use decisions in site redevelopment for urban renewal in Hong Kong

Urban renewal is a significant issue in developed urban areas, with a particular problem for urban planners being redevelopment of land to meet demand whilst ensuring compatibility with existing land use. This paper presents a geographic information systems (GIS)-based decision support tool (called LUDS) to quantitatively assess land-use suitability for site redevelopment in urban renewal areas. This consists of a model for the suitability analysis and an affiliated land-information database for residential, commercial, industrial, G/I/C (government/institution/community) and open space land uses. Development has occurred with support from interviews with industry experts, focus group meetings and an experimental trial, combined with several advanced techniques and tools, including GIS data processing and spatial analysis, multi-criterion analysis, as well as the AHP method for constructing the model and database. As demonstrated in the trial, LUDS assists planners in making land-use decisions and supports the planning process in assessing urban land-use suitability for site redevelopment. Moreover, it facilitates public consultation (participatory planning) by providing stakeholders with an explicit understanding of planners' views.

Social cars : sensing, gathering, sharing and conveying social cues to road users

Intelligent Transport Systems (ITS) resembles the infrastructure for ubiquitous computing in the car. It encompasses a) all kinds of sensing technologies within vehicles as well as road infrastructure, b) wireless communication protocols for the sensed information to be exchanged between vehicles (V2V) and between vehicles and infrastructure (V2I), and c) appropriate intelligent algorithms and computational technologies that process these real-time streams of information. As such, ITS can be considered a game changer. It provides the fundamental basis of new, innovative concepts and applications, similar to the Internet itself. The information sensed or gathered within or around the vehicle has led to a variety of context-aware in-vehicular technologies within the car. A simple example is the Anti-lock Breaking System (ABS), which releases the breaks when sensors detect that the wheels are locked. We refer to this type of context awareness as vehicle/technology awareness. V2V and V2I communication, often summarized as V2X, enables the exchange and sharing of sensed information amongst cars. As a result, the vehicle/technology awareness horizon of each individual car is expanded beyond its observable surrounding, paving the way to technologically enhance such already advanced systems. In this chapter, we draw attention to those application areas of sensing and V2X technologies, where the human (driver), the human’s behavior and hence the psychological perspective plays a more pivotal role. The focal points of our project are illustrated in Figure 1: In all areas, the vehicle first (1) gathers or senses information about the driver. Rather than to limit the use of such information towards vehicle/technology awareness, we see great potential for applications in which this sensed information is then (2) fed back to the driver for an increased self-awareness. In addition, by using V2V technologies, it can also be (3) passed to surrounding drivers for an increased social awareness, or (4), pushed even further, into the cloud, where it is collected and visualized for an increased, collective urban awareness within the urban community at large, which includes all city dwellers.

Street Computing : Urban Informatics and City Interfaces

This book develops tools and techniques that will help urban residents gain access to urban computing. Metaphorically speaking, it is taking computing to the street by giving the general public – rather than just researchers and professionals – the power to leverage available city infrastructure and create solutions tailored to their individual needs. It brings together five chapters that are based on presentations given at the Street Computing Workshop held on 24 November 2009 in Melbourne in conjunction with the Australian Computer-Human Interaction Conference (OZCHI 2009). This book focuses on applying urban informatics, urban and community sensing and open application programming interfaces (APIs) to the public space through the delivery of online services, on demand and in real time. It then offers a case study of how the city of Singapore has harnessed the potential of an online infrastructure so that residents and visitors can access services electronically. This book was published as a special issue of the Journal of Urban Technology, 19(2), 2012.

Travel time prediction on signalised urban arterials by applying SARIMA modelling on Bluetooth data

Travel time prediction has long been the topic of transportation research. But most relevant prediction models in the literature are limited to motorways. Travel time prediction on arterial networks is challenging due to involving traffic signals and significant variability of individual vehicle travel time. The limited availability of traffic data from arterial networks makes travel time prediction even more challenging. Recently, there has been significant interest of exploiting Bluetooth data for travel time estimation. This research analysed the real travel time data collected by the Brisbane City Council using the Bluetooth technology on arterials. Databases, including experienced average daily travel time are created and classified for approximately 8 months. Thereafter, based on data characteristics, Seasonal Auto Regressive Integrated Moving Average (SARIMA) modelling is applied on the database for short-term travel time prediction. The SARMIA model not only takes the previous continuous lags into account, but also uses the values from the same time of previous days for travel time prediction. This is carried out by defining a seasonality coefficient which improves the accuracy of travel time prediction in linear models. The accuracy, robustness and transferability of the model are evaluated through comparing the real and predicted values on three sites within Brisbane network. The results contain the detailed validation for different prediction horizons (5 min to 90 minutes). The model performance is evaluated mainly on congested periods and compared to the naive technique of considering the historical average.

The rise of low-cost sensing for managing air pollution in cities

Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

Transdisciplinary approaches to urban computing

The research field of urban computing – defined as “the integration of computing, sensing, and actuation technologies into everyday urban settings and lifestyles” – considers the design and use of ubiquitous computing technology in public and shared urban environments. Its impact on cities, buildings, and spaces evokes innumerable kinds of change. Embedded into our everyday lived environments, urban computing technologies have the potential to alter the meaning of physical space, and affect the activities performed in those spaces. This paper starts a multi-themed discussion of various aspects that make up the, at times, messy and certainly transdisciplinary field of urban computing and urban informatics.

Assessment of volatile organic compounds in urban schools

There is an increased concern about airborne particles not only because of their environmental effects, but also due to their potential adverse health effects on humans, especially children. Despite the growing evidence of airborne particles having an impact on children’s health, there have been limited studies investigating the long term health effects as well as the chemical composition of ambient air which further helps in determining their toxicity. Therefore, a systematic study on the chemical composition of air in school environment has been carried out in Brisbane, which is known as “Ultrafine Particles from Traffic Emissions on Children’s Health” (UPTECH). This study is also a part of the larger project focusing on analysis of the chemical composition of ambient air, as well as source apportionment and the quantification of ambient concentrations of organic pollutants in the vicinity of schools. However, this particular paper presents some of the results on concentration of different Volatile Organic Compounds in both indoor and outdoor location from different schools. The database consisted of 750 samples (500 outdoor and 250 indoor) collected for VOCs at 25 different schools. The sampling and analysis were conducted following the standard methods. A total of 90 individual VOCs were identified from the schools studied. Compounds such as toluene, acetic acid, nonanal, benzaldehyde, 2- ethyl 1- hexanol, limonene were the most common in indoors whereas isopentane, toluene, hexane, heptane were dominant in outdoors. The indoor/ outdoor ratio of average sum of VOCs were found to be more than one in most of the schools indicating that there might be additional indoor sources along with the outdoor air in those schools. However, further expansion of the study in relation to source apportionment, correlating with traffic and meteorological data is in progress.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.