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INTRODUCTION: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. METHOD: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. RESULTS: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. CONCLUSION: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. PRACTICAL APPLICATIONS: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.
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Políticas , Caminata , HumanosRESUMEN
OBJECTIVE: Speeding behavior is recognized as one of the three main causes of risk on the roads and the most frequent ordinary violation among truck drivers. The study reported in this article focused on the driving anger personality trait and its role in predicting the observed speeding behavior in a sample of truck drivers. METHODS: A longitudinal study design was implemented, where 93 Serbian truck drivers filled in an adapted form of the Driving Anger Scale designed to assess the amount of driving anger evoked by specific traffic situations. The drivers' second-by-second speeding behavior data were captured using GPS tracking and recording devices installed in the vehicles over a period of six months. Hierarchical multiple linear regression was used to examine the predictive performance of driving anger. RESULTS: The results showed that more than three fourths of the exceeded speed ranged from 1 to 9 km/h and the highest percentage of speed events occurred in urban areas. The findings suggest that truck drivers experienced the highest level of anger in driving because of the discourteous and illegal behavior of other drivers. Younger truck drivers are more likely to drive fast compared to older drivers. Truck drivers who perform driving tasks more often and spend less time driving are more likely to participate in speed events. The results revealed that the subscales of anger, hostile gestures and traffic obstructions have a positive effect, while slow driving has a negative effect on the observed speeding behavior of truck drivers. CONCLUSIONS: This study confirms the predictive performance of driving anger facets. The results of the research can help us improve our understanding of the mechanisms of speeding behavior of truck drivers.
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Aceleración , Ira , Conducción de Automóvil/psicología , Vehículos a Motor , Adulto , Conducción de Automóvil/estadística & datos numéricos , Femenino , Sistemas de Información Geográfica , Humanos , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Serbia , Adulto JovenRESUMEN
Objective: Driving anger has been found to be a motivational determinant of risky and aggressive driving. The study reported in this article focused on research questions concerning the conceptualization of driving anger and relationships between driving anger and aggressive driving.Methods: In a cross-sectional survey study, 1,020 regular Serbian drivers completed a web-based online questionnaire. Exploratory Factor Analysis and Confirmatory Factor Analysis were used to assess the factor structure and psychometric properties of the Driving Anger Scale.Results: The results showed that the adapted DAS was a valid and reliable instrument divided into five subscales such as discourteous behavior, illegal behavior, slow driving, hostile gestures and traffic obstructions. The robustness of the five-factor model obtained from the EFA was confirmed by the CFA. The study provided evidence supporting the predictive validity of the adapted DAS and revealed that the dimensions of driving anger are differently associated with prosocial and aggressive driving behaviors.Conclusions: The study supports the adapted DAS validity and produces evidence that driving anger is associated with driving outcomes.
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Accidentes de Tránsito/estadística & datos numéricos , Conducción Agresiva/estadística & datos numéricos , Ira , Psicometría/estadística & datos numéricos , Adulto , Anciano , Estudios Transversales , Análisis Factorial , Femenino , Humanos , Masculino , Persona de Mediana Edad , Serbia , Adulto JovenRESUMEN
In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be benefitial to reserachers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analysed area.