A visual-numeric approach to clustering and anomaly detection for trajectory data

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.

Estimating fatality rates in occupational light vehicle users using vehicle registration and crash data

Objective: To estimate occupational light vehicle (OLV) fatality numbers using vehicle registration and crash data and compare these with previous estimates based on workers' compensation data. Method: New South Wales (NSW) Roads and Traffic Authority (RTA) vehicle registration and crash data were obtained for 2004. NSW is the only Australian jurisdiction with mandatory work-use registration, which was used as a proxy for work-relatedness. OLV fatality rates based on registration data as the denominator were calculated and comparisons made with published 2003/04 fatalities based on workers' compensation data. Results: Thirty-four NSW RTA OLV-user fatalities were identified, a rate of 4.5 deaths per 100,000 organisationally registered OLV, whereas the Australian Safety and Compensation Council (ASCC), reported 28 OLV deaths Australia-wide. Conclusions: More OLV user fatalities were identified from vehicle registration-based data than those based on workers' compensation estimates and the data are likely to provide an improved estimate of fatalities specific to OLV use. Implications: OLV-use is an important cause of traumatic fatalities that would be better identified through the use of vehicle-registration data, which provides a stronger evidence base from which to develop policy responses.

Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles

Aim  A key life-history component for many animals is the need for movement between different geographical locations at particular times. Green turtle (Chelonia mydas) hatchlings disperse from their natal location to spend an early pelagic stage in the ocean, followed by a neritic stage where small juveniles settle in coastal areas. In this study, we combined genetic and Lagrangian drifter data to investigate the connectivity between natal and foraging locations. In particular we focus on the evidence for transatlantic transport. Location  Atlantic Ocean.

Methods  We used mitochondrial DNA (mtDNA) sequences (n = 1567) from foraging groups (n = 8) and nesting populations (n = 12) on both sides of the Atlantic. Genetic data were obtained for Cape Verde juvenile turtles, a foraging group not previously sampled for genetic study. Various statistical methods were used to explore spatial genetics and population genetic structure (e.g. exact tests of differentiation, Geneland and analysis of molecular variance). Many-to-many mixed stock analysis estimated the connectivity between nesting and foraging groups.

Results  Our key new finding is robust evidence for connectivity between a nesting population on the South American coast (25% of the Surinam nesting population are estimated to go to Cape Verde) and a foraging group off the coast of West Africa (38% of Cape Verde juveniles are estimated to originate from Surinam), thus extending the results of previous investigations by confirming that there is substantial transatlantic dispersal in both directions. Lagrangian drifter data demonstrated that transport by drift across the Atlantic within a few years is possible.

Main conclusions  Small juvenile green turtles seem capable of dispersing extensively, and can drop out of the pelagic phase on a transatlantic scale (the average distance between natal and foraging locations was 3048 km). Nevertheless, we also find support for the ‘closest-to-home’ hypothesis in that the degree of contribution from a nesting population to a foraging group is correlated with proximity. Larger-sized turtles appear to feed closer to their natal breeding grounds (the average distance was 1133 km), indicating that those that have been initially transported to far-flung foraging grounds may still be able to move nearer to home as they grow larger.

A data as a product model for future consumption of big stream data in clouds

Data is becoming the world’s new natural resourceand big data use grows quickly. The trend of computingtechnology is that everything is merged into the Internet and‘big data’ are integrated to comprise completeinformation for collective intelligence. With the increasingsize of big data, refining big data themselves to reduce data sizewhile keeping critical data (or useful information) is a newapproach direction. In this paper, we provide a novel dataconsumption model, which separates the consumption of datafrom the raw data, and thus enable cloud computing for bigdata applications. We define a new Data-as-a-Product (DaaP)concept; a data product is a small sized summary of theoriginal data and can directly answer users’ queries. Thus, weseparate the mining of big data into two classes of processingmodules: the refine modules to change raw big data into smallsizeddata products, and application-oriented mining modulesto discover desired knowledge further for applications fromwell-defined data products. Our practices of mining big streamdata, including medical sensor stream data, streams of textdata and trajectory data, demonstrated the efficiency andprecision of our DaaP model for answering users’ queries

Energy efficiency on location based applications in mobile cloud computing: a survey

The constrained battery power of mobile devices poses a serious impact on user experience. As an increasingly prevalent type of applications in mobile cloud environments, location-based applications (LBAs) present some inherent limitations concerning energy. For example, the Global Positioning System based positioning mechanism is well-known for its extremely power-hungry attribute. Due to the severity of the issue, considerable researches have focused on energy-efficient locating sensing mechanism in the last a few years. In this paper, we provide a comprehensive survey of recent work on low-power design of LBAs. An overview of LBAs and different locating sensing technologies used today are introduced. Methods for energy saving with existing locating technologies are investigated. Reductions of location updating queries and simplifications of trajectory data are also mentioned. Moreover, we discuss cloud-based schemes in detail which try to develop new energy-efficient locating technologies by leveraging the cloud capabilities of storage, computation and sharing. Finally, we conclude the survey and discuss the future research directions. © 2013 Springer-Verlag Wien.

3D motion matching algorithm using signature feature descriptor

This paper introduces a basic frame for rehabilitation motion practice system which detects 3D motion trajectory with the Microsoft Kinect (MSK) sensor system and proposes a cost-effective 3D motion matching algorithm. The rehabilitation motion practice system displays a reference 3D motion in the database system that the player (patient) tries to follow. The player’s motion is traced by the MSK sensor system and then compared with the reference motion to evaluate how well the player follows the reference motion. In this system, 3D motion matching algorithm is a key feature for accurate evaluation for player’s performance. Even though similarity measurement of 3D trajectories is one of the most important tasks in 3D motion analysis, existing methods are still limited. Recent researches focus on the full length 3D trajectory data set. However, it is not true that every point on the trajectory plays the same role and has the same meaning. In this situation, we developed a new cost-effective method that only uses the less number of features called ‘signature’ which is a flexible descriptor computed from the region of ‘elbow points’. Therefore, our proposed method runs faster than other methods which use the full length trajectory information. The similarity of trajectories is measured based on the signature using an alignment method such as dynamic time warping (DTW), continuous dynamic time warping (CDTW) or longest common sub-sequence (LCSS) method. In the experimental studies, we applied the MSK sensor system to detect, trace and match the 3D motion of human body. This application was assumed as a system for guiding a rehabilitation practice which can evaluate how well the motion practice was performed based on comparison of the patient’s practice motion traced by the MSK system with the pre-defined reference motion in a database. In order to evaluate the accuracy of our 3D motion matching algorithm, we compared our method with two other methods using Australian sign word dataset. As a result, our matching algorithm outperforms in matching 3D motion, and it can be exploited for a base framework for various 3D motion-based applications at low cost with high accuracy.

An efficient mapreduce-based parallel clustering algorithm for distributed traffic subarea division

Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K -Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K -Means and then employ a MapReduce paradigm to redesign the optimized K -Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K -Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

Predicting driving direction with weighted markov model

Driving direction prediction can be useful in different applications such as driver warning and route recommendation. In this paper, a framework is proposed to predict the driving direction based on weighted Markov model. First the city POI (Point of Interesting) map is generated from trajectory data using weighted PageRank algorithm. Then, a weighted Markov model is trained for the near term driving direction prediction based on the POI map and historical trajectories. The experimental results on real-world data set indicate that the proposed method can improve the original Markov prediction model by 10% at some circumstances and 5% overall.

Research on seismic signals for vehicle targets and recognition by data fusion

This paper researches seismic signals of typical vehicle targets in order to extract features and to recognize vehicle targets. As a data fusion method, the technique of artificial neural networks combined with genetic algorithm(ANNCGA) is applied for recognition of seismic signals that belong to different kinds of vehicle targets. The technique of ANNCGA and its architecture have been presented. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that seismic properties of target acquired are correct, ANNCGA data fusion method is effective to solve the problem of target recognition.

Path planning for sensor data collecting mobile robot

A mobile robot employed for data collection is faced with the problem of travelling from an initial location to a final location while maintaining as close a distance as possible to all the sensors at a given time in the journey. Here we employ optimal control ideas in forming the necessary control commands for such a robot resulting not only the necessary acceleration commands for the underlying robot, but also the resulting trajectory. This approach can also be easily extended for the case of producing the optimal trajectory for an ariel vehicle used for data collection from indiscriminately scattered ad-hoc sensors located on the ground. We demonstrate the implementation of our algorithm using a Pioneer 3-AT robot.

Students' conflicting attitudes towards games as a vehicle for learning mathematics : a methodological dilemma

Mathematics games are widely employed in school classrooms for such reasons as a reward for early finishers or to enhance students' attitude towards mathematics. During a four week period, a total of 222 Grade 5 and 6 (9 to 12 years old) children from Melbourne, Australia, were taught multiplication and division of decimal numbers using calculator games or rich mathematical activities. Likert scale surveys of the children's attitudes towards games as a vehicle for learning mathematics revealed unexpectedly high proportions of negative attitudes at the conclusion of the research. In contrast, student interview data revealed positive associations between games and mathematical learning. This article reports on the methodological dilemma of resultant conflicting attitudinal data related to game- playing. Concerns arising from the divergence in the results are raised in this article. Implications based on the experience of this study may inform educational researchers about future methodological choices involving attitudinal research.

Risky driving habits and motor vehicle driver injury

Risky driving is an important cause of motor vehicle injury, but there is a lack of good epidemiological data in this field, particularly data comparing risky driving in younger drivers to those of other age groups. We examined the relationship between risky driving habits, prior traffic convictions and motor vehicle injury using cross-sectional data amongst 21,893 individuals in New Zealand, including 8029 who were aged 16–24 years. Those who reported frequently racing a motor vehicle for excitement or driving at 20 km/h or more over the speed limit, and those who had received traffic convictions over the past 12 months, were between two and four times more likely to have been injured while driving over the same time period. Driving unlicensed was a risk factor for older but not younger drivers, and driving at 20 km/h or more above the speed limits was a stronger risk factor for younger (<25 years) than older drivers. These results confirm the need for interventions targeting risky driving and suggest that different strategies may be required for different high-risk groups.

Vehicle design for minimum societal harm

A design technique was developed to provide the best protection to all occupants in the real-world crashes that occur on Australian roads. A team of experts from around the world was marshalled to analyse crash data, develop new information on impact injury and a new computer optimising technique for simulation of side impact crashes.

Story as a vehicle for learning in online role play : a case study

In online role plays, students are asked to engage with a story that serves as a metaphor for real-life experience as they learn and develop skills. However, practitioners rarely examine the characteristics and management of this story as factors in the students' engagement in and learning from the activity. In this paper I present findings from a recent case study which examines these factors in an online role play that has been named as an exemplar and has been run for 19 years in Australian and international universities to teach Middle East politics and journalism. Online role plays are increasingly popular in tertiary education, in forms ranging from simple text-based role plays to virtual learning environment activities and e-simulations. The role play I studied required students to communicate in role via simulated email messages and draw on real-life resources and daily simulated online newspaper publications produced by the journalism students rather than rely on information or automated interactions built into an interface. This relatively simple format enabled me to observe clearly the impact of the technique's basic design elements. I studied both the story elements of plot, character and setting and the non-story elements of assessment, group work and online format. The data collection methods include analysis of student emails in the role play, a questionnaire, a focus group, interviews and the journal I kept as a participant-observer in the role play. In evaluating the qualities and impact of story elements I drew upon established aesthetic principles for drama and poststructuralist drama education.