Investigating the effect of contextual factors on driving: An experimental study

Alongside human factors, contextual factors are believed to have an ongoing and complex impact on driving outcomes. However, how and to what extent the components of context influence driving outcomes (e.g. rule violations, crash, stress, fatigue) are far beyond full understanding. The purpose of this study is to explore the effects of weather condition, lighting condition and traffic density on driving outcomes. Thirty-six volunteers were enrolled to participate into a driving simulator-based experiment. Each participant was required to complete twelve trials of simulated driving under different sets of scenarios. Driving outcome was measured by five dependent variables: frequency of speeding, frequency of lane deviations, number of correct sign recognition, completion time and workload. The results showed the frequency of speeding was significantly affected by weather condition, lighting condition and traffic density. Lighting condition had a significant effect on number of correct sign recognition. Weather condition, lighting condition and traffic density had significant effects on task completion time. Weather condition and lighting condition had significant effects on driver’s workload. The implications of the results could help traffic safety professionals better understand the risk factors that may lead to human errors during driving. Practically, countermeasures could be inspired and developed to mitigate the adverse impacts brought by driving context to minimum.     

Effects of chronic cocaine administration on spatial learning and hippocampal spine density in two genetically different strains of rats

Lewis and Fischer-344 rats have been proposed as an addiction model because of their differences in addiction behaviour. It has been suggested that drug addiction is related to learning and memory processes and depends on individual genetic background. We have evaluated learning performance using the eight-arm radial maze (RAM) in Lewis and Fischer-344 adult rats undergoing a chronic treatment with cocaine. In order to study whether morphological alterations were involved in the possible changes in learning after chronic cocaine treatment, we counted the spine density in hippocampal CA1 neurons from animals after the RAM protocol. Our results showed that Fischer-344 rats significantly took more time to carry out test acquisition and made a greater number of errors than Lewis animals. Nevertheless, cocaine treatment did not induce changes in learning and memory processes in both strains of rats. These facts indicate that there are genetic differences in spatial learning and memory that are not modified by the chronic treatment with cocaine. Moreover, hippocampal spine density is cocaine-modulated in both strains of rats. In conclusion, cocaine induces similar changes in hippocampal neurons morphology that are not related to genetic differences in spatial learning in the RAM protocol used here.     

Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested.