Reaction times of young alcohol-impaired drivers

Young individuals who drive under the influence of alcohol have a higher relative risk of crash involvement; as such, the literature has extensively investigated the factors affecting such involvement through both post-accident surveys and simulator experiments. The effects of differentiated breath alcohol concentrations (BrAC) on young driver behavior, however, have been largely unaddressed, mainly as a result of the difficulty in collecting the necessary data. We explore young driver behavior under the influence of alcohol using a driving simulator experiment where 49 participants were subjected to a common pre-defined dose of alcohol consumption. Comparing reaction times before and after consumption allows for interesting insights and suggestions regarding policy interventions. As expected, the results indicate that increased reaction times before consuming alcohol strongly affect post-consumption reaction times, while increased BrAC levels prolong reaction times; a 10% increase in BrAC levels results in a 2% increase in reaction time. Interestingly, individuals with faster alcohol absorption times perform better regardless of absolute BrAC level, while recent meals lead to higher reaction times and regular exercising to lower.     

Multilevel modelling for the regional effect of enforcement on road accidents

This paper investigates the effect of the intensification of Police enforcement on the number of road accidents at national and regional level in Greece, focusing on one of the most important road safety violations: drinking-and-driving. Multilevel negative binomial models are developed to describe the effect of the intensification of alcohol enforcement on the reduction of road accidents in different regions of Greece. Moreover, two approaches are explored as far as regional clustering is concerned: the first one concerns an ad hoc geographical clustering and the second one is based on the results of mathematical cluster analysis through demographic, transport and road safety characteristics. Results indicate that there are significant spatial dependences among road accidents and enforcement. Additionally, it is shown that these dependences are more efficiently interpreted when regions are determined on the basis of qualitative similarities than on the basis of geographical adjacency.     

Explaining the road accident risk: Weather effects

This research aims to highlight the link between weather conditions and road accident risk at an aggregate level and on a monthly basis, in order to improve road safety monitoring at a national level. It is based on some case studies carried out in Work Package 7 on “Data analysis and synthesis” of the EU-FP6 project “SafetyNet – Building the European Road Safety Observatory”, which illustrate the use of weather variables for analysing changes in the number of road injury accidents. Time series analysis models with explanatory variables that measure the weather quantitatively were used and applied to aggregate datasets of injury accidents for France, the Netherlands and the Athens region, over periods of more than 20 years. The main results reveal significant correlations on a monthly basis between weather variables and the aggregate number of injury accidents, but the magnitude and even the sign of these correlations vary according to the type of road (motorways, rural roads or urban roads). Moreover, in the case of the interurban network in France, it appears that the rainfall effect is mainly direct on motorways – exposure being unchanged, and partly indirect on main roads – as a result of changes in exposure. Additional results obtained on a daily basis for the Athens region indicate that capturing the within-the-month variability of the weather variables and including it in a monthly model highlights the effects of extreme weather. Such findings are consistent with previous results obtained for France using a similar approach, with the exception of the negative correlation between precipitation and the number of injury accidents found for the Athens region, which is further investigated. The outlook for the approach and its added value are discussed in the conclusion.     

Reexamining adaptation and the set point model of happiness: Reactions to changes in marital status.

According to adaptation theory, individuals react to events but quickly adapt back to baseline levels of subjective well-being. To test this idea, the authors used data from a 15-year longitudinal study of over 24,000 individuals to examine the effects of marital transitions on life satisfaction. On average, individuals reacted to events and then adapted back toward baseline levels. However, there were substantial individual differences in this tendency. Individuals who initially reacted strongly were still far from baseline years later, and many people exhibited trajectories that were in the opposite direction to that predicted by adaptation theory. Thus, marital transitions can be associated with long-lasting changes in satisfaction, but these changes can be overlooked when only average trends are examined. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A GIS-based methodology for identifying pedestrians’ crossing patterns

A pedestrian trip is a spatiotemporal process going through different states and related to different decisions made at certain times and locations on the urban network. The analysis of pedestrian trips in terms of crossing patterns is a complex task, which is often further limited by a lack of appropriate and detailed data. The objective of this research is the development and testing of appropriate indicators of pedestrian crossing behavior along urban trips, and a methodology for collecting and processing the data required for the analysis of this behavior. First, a comprehensive set of indicators for the assessment of pedestrian behavior in urban areas is proposed (i.e. average trip length, number, type and location of crossings). Then, a GIS tool is developed for the storage and integration of information on pedestrian trips, and the crossings made during the trips, with other geographical information (e.g. road network function and geometry, traffic control and pedestrian facilities). The proposed approach is then tested at network level on a sample of pedestrian trips collected by a field survey. The results suggest specific patterns of pedestrian crossing behavior, such as the tendency to cross at the beginning of the trip and the tendency to cross at mid-block locations when signalized junctions are not available. The results are further discussed in terms of urban planning and management implications. It is concluded that the proposed approach is very efficient for the analysis of pedestrian crossing behavior in urban areas.     

Indexing and progressive top-k similarity retrieval of trajectories

In this work, we study the performance of state-of-the-art access methods to efficiently store and retrieve trajectories in spatial networks. First, we study how efficiently such methods can manage trajectory data to support indexing for data demanding applications where trajectory retrieval must be fast. At the same time, trajectory insertions, deletions and modifications should also be executed efficiently. Secondly, we compare the performance of progressive processing of trajectory similarity top-k queries, which is a common query in spatial applications. Specifically, we examine FNR-trees (Frentzos 2003) and MON-trees (de Almeida and Gueting, 2005), which have been proposed for trajectory management, against a novel variation of our proposed Cluster-extended Adjacency Lists (CeAL) (Tiakas and Rafailidis 2015). In particular: (a) we extend the above access methods to efficiently handle trajectories of objects that move in large spatial networks, and (b) to enhance their performance, we create an entirely new implementation framework to generate trajectories and to test the trajectory management and retrieval for each approach. With respect to the generation of trajectories, we extend the generator by Brinkhoff (2000) to efficiently support very large spatial networks. Finally, we conduct extensive experimentation which demonstrates that the proposed method CeAL prevails in space and time complexity.

     

Multi-Way Distance Join Queries in Spatial Databases

Let a tuple of n objects obeying a query graph (QG) be called the n-tuple. The D_{_distance}-value of this n-tuple is the value of a linear function of distances of the n objects that make up this n-tuple, according to the edges of the QG. This paper addresses the problem of finding the K n-tuples between n spatial datasets that have the smallest D_{_distance}-values, the so-called K-multi-way distance join query (K-MWDJQ), where each set is indexed by an R-tree-based structure. This query can be viewed as an extension of K-closest-pairs query (K-CPQ) [8] for n inputs. In addition, a recursive non-incremental branch-and-bound algorithm following a depth-first search for processing synchronously all inputs without producing any intermediate result is proposed. Enhanced pruning techniques are also applied to n R-trees nodes in order to reduce the total response time and the number of distance computations of the query. Due to the exponential nature of the problem, we also propose a time-based approximate version of the recursive algorithm that combines approximation techniques to adjust the quality of the result and the global processing time. Finally, we give a detailed experimental study of the proposed algorithms using real spatial datasets, highlighting their performance and the quality of the approximate results.     

Exemplar queries: a new way of searching

Modern search engines employ advanced techniques that go beyond the structures that strictly satisfy the query conditions in an effort to better capture the user intentions. In this work, we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries exemplar queries. We provide a formal specification of their semantics and show that they are fundamentally different from notions like queries by example, approximate queries and related queries. We provide an implementation of these semantics for knowledge graphs and present an exact solution with a number of optimizations that improve performance without compromising the result quality. We study two different congruence relations, isomorphism and strong simulation, for identifying the answers to an exemplar query. We also provide an approximate solution that prunes the search space and achieves considerably better time performance with minimal or no impact on effectiveness. The effectiveness and efficiency of these solutions with synthetic and real datasets are experimentally evaluated, and the importance of exemplar queries in practice is illustrated.     

A case study on propagating and updating provenance information using the CIDOC CRM

Provenance information of digital objects maintained by digital libraries and archives is crucial for authenticity assessment, reproducibility and accountability. Such information is commonly stored on metadata placed in various Metadata Repositories (MRs) or Knowledge Bases (KBs). Nevertheless, in various settings it is prohibitive to store the provenance of each digital object due to the high storage space requirements that are needed for having complete provenance. In this paper, we introduce provenance-based inference rules as a means to complete the provenance information, to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). Roughly, we show how provenance information can be propagated by identifying a number of basic inference rules over a core conceptual model for representing provenance. The propagation of provenance concerns fundamental modelling concepts such as actors, activities, events, devices and information objects, and their associations. However, since a MR/KB is not static but changes over time due to several factors, the question that arises is how we can satisfy update requests while still supporting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required add/delete operations, consider two different semantics for deletion of information, and provide the corresponding update algorithms. Finally, we report extensive comparative results for different repository policies regarding the derivation of new knowledge, in datasets containing up to one million RDF triples. The results allow us to understand the tradeoffs related to the use of inference rules on storage space and performance of queries and updates.     

Method to reduce errors of droplet sizing based on the ratio of fluorescent and scattered light intensities (laser-induced fluorescence/Mie technique)

The droplet sizing accuracy of the laser technique, based on the ratio of laser-induced fluorescence (LIF) and scattered light (Mie) intensities from droplets, is examined. We develop an analytical model of the ratio of fluorescent to scattered light intensities of droplets, which shows that the LIF/Mie technique is susceptible to sizing errors that depend on the mean droplet size and the spread of the droplet size distribution. The sizing uncertainty due to the oscillations of the scattered light intensity as a function of droplet size is first quantified. Then, a new data processing method is proposed that can improve the sizing uncertainty of the technique for the sprays that were examined in this study by more than 5% by accounting for the size spread of the measured droplets, while improvements of 25% are possible when accounting for the mean droplet size. The sizing accuracy of the technique is evaluated in terms of the refractive index of liquid, scattering angle, and dye concentration in the liquid. It is found that the proposed approach leads to sizing uncertainty of less than 14% when combined with light collection at forward scattering angles close to 60° and the lowest fluorescent dye concentration in the liquid for all refractive indices.