Crash Risk Assessment with Cooperative Systems

Crash risk is the statistical probability of a crash. Its assessment can be performed through ex post statistical analysis or in real-time with on-vehicle systems. These systems can be cooperative. Cooperative Vehicle-Infrastructure Systems (CVIS) are a developing research avenue in the automotive industry worldwide. This paper provides a survey of existing CVIS systems and methods to assess crash risk with them. It describes the advantages of cooperative systems versus non-cooperative systems. A sample of cooperative crash risk assessment systems is analysed to extract vulnerabilities according to three criteria: market penetration, over-reliance on GPS and broadcasting issues. It shows that cooperative risk assessment systems are still in their infancy and requires further development to provide their full benefits to road users.

A model to predict hypovigilance during a monotonous task

The driving task requires sustained attention during prolonged periods, and can be performed in highly predictable or repetitive environments. Such conditions could create drowsiness or hypovigilance and impair the ability to react to critical events. Identifying vigilance decrement in monotonous conditions has been a major subject of research, but no research to date has attempted to predict this vigilance decrement. This pilot study aims to show that vigilance decrements due to monotonous tasks can be predicted through mathematical modelling. A short vigilance task sensitive to short periods of lapses of vigilance called Sustained Attention to Response Task is used to assess participants’ performance. This task models the driver’s ability to cope with unpredicted events by performing the expected action. A Hidden Markov Model (HMM) is proposed to predict participants’ hypovigilance. Driver’s vigilance evolution is modelled as a hidden state and is correlated to an observable variable: the participant’s reactions time. This experiment shows that the monotony of the task can lead to an important vigilance decline in less than five minutes. This impairment can be predicted four minutes in advance with an 86% accuracy using HMMs. This experiment showed that mathematical models such as HMM can efficiently predict hypovigilance through surrogate measures. The presented model could result in the development of an in-vehicle device that detects driver hypovigilance in advance and warn the driver accordingly, thus offering the potential to enhance road safety and prevent road crashes.

Random Indexing K-tree

Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering.

Word Segmentation for Chinese Wikipedia Using N-Gram Mutual Information

In this paper, we propose an unsupervised segmentation approach, named "n-gram mutual information", or NGMI, which is used to segment Chinese documents into n-character words or phrases, using language statistics drawn from the Chinese Wikipedia corpus. The approach alleviates the tremendous effort that is required in preparing and maintaining the manually segmented Chinese text for training purposes, and manually maintaining ever expanding lexicons. Previously, mutual information was used to achieve automated segmentation into 2-character words. The NGMI approach extends the approach to handle longer n-character words. Experiments with heterogeneous documents from the Chinese Wikipedia collection show good results.

Duty of police and ambulance services : what happens when the plaintiff refuses treatment?

In a recent case the New South Wales Court of Appeal considered the duty of care owed by ambulance and police officers, issues concerning breach and causation and the practical effect of the exclusion of the plaintiff's evidence.

The impact of police speed enforcement practices on self-reported speeding: An exploration of the effects of visibility and mobility

Research has highlighted the relationship between vehicle speed and increased crash risk and severity. Evidence suggests that police speed enforcement, in particular speed camera operations, can be an effective tool for reducing traffic crashes. A quantitative survey of Queensland drivers (n = 852) was conducted to investigate the impact of police speed enforcement methods on self-reported speeding behaviour. Results indicate that visible enforcement was associated with significantly greater self-reported compliance than covert operations irrespective of the mobility of the approach, and the effects on behaviour were longer lasting. The mobility of operations appeared to be moderated the visibility of the approach. Specifically, increased mobility was associated with increase reported compliant behaviour, but only for covert operations, and increased longevity of reported compliant behaviour, but only for overt operations. The perceived effectiveness of various speed enforcement approaches are also analysed across a range of driving scenarios. Results are discussed in light of the small effect sizes. Recommendations for policy and future research are presented.

"There's no way in hell I would pull up" : Deterrent and other effects of vehicle impoundment laws for hooning

Traffic law enforcement is based on deterrence principles, whereby drivers control their behaviour in order to avoid an undesirable sanction. For “hooning”-related driving behaviours in Queensland, the driver’s vehicle can be impounded for 48 hours, 3 months, or permanently depending on the number of previous hooning offences. It is assumed that the threat of losing something of value, their vehicle, will discourage drivers from hooning. While official data shows that the rate of repeat offending is low, an in-depth understanding of the deterrent effects of these laws should involve qualitative research with targeted drivers. A sample of 22 drivers who reported engaging in hooning behaviours participated in focus group discussions about the vehicle impoundment laws as applied to hooning offences in Queensland. The findings suggested that deterrence theory alone cannot fully explain hooning behaviour, as participants reported hooning frequently, and intended to continue doing so, despite reporting that it is likely that they will be caught, and perceiving the vehicle impoundment laws to be extremely severe. The punishment avoidance aspect of deterrence theory appears important, as well as factors over and above legal issues, particularly social influences. A concerning finding was drivers’ willingness to flee from police in order to avoid losing their vehicle permanently for a third offence, despite acknowledging risks to their own safety and that of others. This paper discusses the study findings in terms of the implications for future research directions, enforcement practices and policy development for hooning and other traffic offences for which vehicle impoundment is applied.

Applying data mining to assess crash risk on curves

The wide range of contributing factors and circumstances surrounding crashes on road curves suggest that no single intervention can prevent these crashes. This paper presents a novel methodology, based on data mining techniques, to identify contributing factors and the relationship between them. It identifies contributing factors that influence the risk of a crash. Incident records, described using free text, from a large insurance company were analysed with rough set theory. Rough set theory was used to discover dependencies among data, and reasons using the vague, uncertain and imprecise information that characterised the insurance dataset. The results show that male drivers, who are between 50 and 59 years old, driving during evening peak hours are involved with a collision, had a lowest crash risk. Drivers between 25 and 29 years old, driving from around midnight to 6 am and in a new car has the highest risk. The analysis of the most significant contributing factors on curves suggests that drivers with driving experience of 25 to 42 years, who are driving a new vehicle have the highest crash cost risk, characterised by the vehicle running off the road and hitting a tree. This research complements existing statistically based tools approach to analyse road crashes. Our data mining approach is supported with proven theory and will allow road safety practitioners to effectively understand the dependencies between contributing factors and the crash type with the view to designing tailored countermeasures.

Collaborative Filtering Recommender Systems based on Popular Tags

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Investigating the use of association rules in improving recommender systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus when there is not sufficient knowledge on a user it is difficult for a recommender system to make quality recommendations. This problem is often referred to as the cold-start problem. Here we investigate whether association rules can be used as a source of information to expand a user profile and thus avoid this problem, leading to improved recommendations to users. Our pilot study shows that indeed it is possible to use association rules to improve the performance of a recommender system. This we believe can lead to further work in utilising appropriate association rules to lessen the impact of the cold-start problem.

How useful is expert opinion for predicting the distribution of a species within and beyond the region of expertise? A case study using brush-tailed rock-wallabiesPetrogale penicillata

1. Species' distribution modelling relies on adequate data sets to build reliable statistical models with high predictive ability. However, the money spent collecting empirical data might be better spent on management. A less expensive source of species' distribution information is expert opinion. This study evaluates expert knowledge and its source. In particular, we determine whether models built on expert knowledge apply over multiple regions or only within the region where the knowledge was derived. 2. The case study focuses on the distribution of the brush-tailed rock-wallaby Petrogale penicillata in eastern Australia. We brought together from two biogeographically different regions substantial and well-designed field data and knowledge from nine experts. We used a novel elicitation tool within a geographical information system to systematically collect expert opinions. The tool utilized an indirect approach to elicitation, asking experts simpler questions about observable rather than abstract quantities, with measures in place to identify uncertainty and offer feedback. Bayesian analysis was used to combine field data and expert knowledge in each region to determine: (i) how expert opinion affected models based on field data and (ii) how similar expert-informed models were within regions and across regions. 3. The elicitation tool effectively captured the experts' opinions and their uncertainties. Experts were comfortable with the map-based elicitation approach used, especially with graphical feedback. Experts tended to predict lower values of species occurrence compared with field data. 4. Across experts, consensus on effect sizes occurred for several habitat variables. Expert opinion generally influenced predictions from field data. However, south-east Queensland and north-east New South Wales experts had different opinions on the influence of elevation and geology, with these differences attributable to geological differences between these regions. 5. Synthesis and applications. When formulated as priors in Bayesian analysis, expert opinion is useful for modifying or strengthening patterns exhibited by empirical data sets that are limited in size or scope. Nevertheless, the ability of an expert to extrapolate beyond their region of knowledge may be poor. Hence there is significant merit in obtaining information from local experts when compiling species' distribution models across several regions.

Creating cultural change in education : A proposal for a continuum for evaluating the effectiveness of sustainable schools implementation strategies in Australia

Networks are increasingly recognised as advantageous when creating and embedding cultural change within organisations. This paper explores andproblematises ideas around networks for education for sustainability (EfS), Australian Sustainable Schools Initiative (AuSSI), a national, whole-school approach to EfS. In three Australian states - New South Wales, Victoria and Queensland – AuSSI has been implemented in different ways. In examining the use of products, facilitators and networks to embed initiatives such as AuSSI in Australian schools, we propose a “continuum of cultural change strategies” as a framework for thinking about each of these approaches to creating organisational and cultural change for sustainability. We anticipate that such a framework may assist where choices need to be made in relation to the kinds of capacity building processes that might best achieve “deep and wide” change within schools hoping to engender significant cultural change.

Analyzing web multimedia query reformulation behavior

Current multimedia Web search engines still use keywords as the primary means to search. Due to the richness in multimedia contents, general users constantly experience some difficulties in formulating textual queries that are representative enough for their needs. As a result, query reformulation becomes part of an inevitable process in most multimedia searches. Previous Web query formulation studies did not investigate the modification sequences and thus can only report limited findings on the reformulation behavior. In this study, we propose an automatic approach to examine multimedia query reformulation using large-scale transaction logs. The key findings show that search term replacement is the most dominant type of modifications in visual searches but less important in audio searches. Image search users prefer the specified search strategy more than video and audio users. There is also a clear tendency to replace terms with synonyms or associated terms in visual queries. The analysis of the search strategies in different types of multimedia searching provides some insights into user’s searching behavior, which can contribute to the design of future query formulation assistance for keyword-based Web multimedia retrieval systems.