Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

Traffic safety risks trends and patterns analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestion. Hence, reducing the frequency of crashes assist in addressing congestion issues (Meyer, 2008). Analysing traffic conditions and discovering risky traffic trends and patterns are essential basics in crash likelihood estimations studies and still require more attention and investigation. In this paper we will show, through data mining techniques, that there is a relationship between pre-crash traffic flow patterns and crash occurrence on motorways, compare them with normal traffic trends, and that this knowledge has the potentiality to improve the accuracy of existing crash likelihood estimation models, and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash occurrence. K-Means clustering algorithm applied to determine dominant pre-crash traffic patterns. In the first phase of this research, traffic regimes identified by analysing crashes and normal traffic situations using half an hour speed in upstream locations of crashes. Then, the second phase investigated the different combination of speed risk indicators to distinguish crashes from normal traffic situations more precisely. Five major trends have been found in the first phase of this paper for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Moreover, the second phase explains that spatiotemporal difference of speed is a better risk indicator among different combinations of speed related risk indicators. Based on these findings, crash likelihood estimation models can be fine-tuned to increase accuracy of estimations and minimize false alarms.

Risk factors for delayed healing in venous leg ulcers : a review of the literature

Background Chronic leg ulcers, remaining unhealed after 4–6 weeks, affect 1-3% of the population, with treatment costly and health service resource intensive. Venous disease contributes to approximately 70% of all chronic leg ulcers and these ulcers are often associated with pain, reduced mobility and a decreased quality of life. Despite evidence-based care, 30% of these ulcers are unlikely to heal within a 24-week period and therefore the recognition and identification of risk factors for delayed healing of venous leg ulcers would be beneficial. Aim To review the available evidence on risk factors for delayed healing of venous leg ulcers. Methods: A review of the literature in regard to risk factors for delayed healing in venous leg ulcers was conducted from January 2000 to December 2013. Evidence was sourced through searches of relevant databases and websites for resources addressing risk factors for delayed healing in venous leg ulcers specifically. Results Twenty-seven studies, of mostly low-level evidence (Level III and IV), identified risk factors associated with delayed healing. Risk factors that were consistently identified included: larger ulcer area, longer ulcer duration, a previous history of ulceration, venous abnormalities and lack of high compression. Additional potential predictors with inconsistent or varying evidence to support their influence on delayed healing of venous leg ulcers included decreased mobility and/or ankle range of movement, poor nutrition and increased age. Discussion Findings from this review indicate that a number of physiological risk factors are asso- ciated with delayed healing in venous leg ulcers and that social and/or psychological risk factors should also be considered and examined further. Conclusion The findings from this review can assist health professionals to identify prognostic indicators or risk factors significantly associated with delayed healing in venous leg ulcers. This will facilitate realistic outcome planning and inform implementation of appropriate early strategies to promote healing.

GABAA and NMDA receptors contribute to synaptic integration in lateral amygdala neurons

In classical fear conditioning a neutral conditioned stimulus (CS), is paired with an aversive unconditioned stimulus (US). The CS thereby acquires the capacity to elicit a fear response. This type of associative learning is thought to require co-activation of principal neurons in the lateral nucleus of the amygdala (LA) by two sets of synaptic inputs...

Polysynaptic potentials within the lateral amygdala networks as indicators of reverberatory activity

Synaptic plasticity in the lateral amygdala (LA) may underlie auditory fear conditioning. Hebb postulated that sustained activity in reverberating cellular ensembles can facilitate temporal coincidence detection. Our anatomical data show that LA neurons have extensive local axon collaterals that are topographically organized and that could provide the anatomical basis for reverberatory activity...