Athanasios Theofilatos

A review of the effect of traffic and weather characteristics on road safety

Taking into consideration the increasing availability of real-time traffic data and stimulated by the importance of proactive safety management, this paper attempts to provide a review of the effect of traffic and weather characteristics on road safety, identify the gaps and discuss the needs for further research. Despite the existence of generally mixed evidence on the effect of traffic parameters, a few patterns can be observed. For instance, traffic flow seems to have a non-linear relationship with accident rates, even though some studies suggest linear relationship with accidents. On the other hand, increased speed limits have found to have a straightforward positive relationship with accident occurrence. Regarding weather effects, the effect of precipitation is quite consistent and leads generally to increased accident frequency but does not seem to have a consistent effect on severity. The impact of other weather parameters on safety, such as visibility, wind speed and temperature is not found straightforward so far. The increasing use of real-time data not only makes easier to identify the safety impact of traffic and weather characteristics, but most importantly makes possible the identification of their combined effect. The more systematic use of these real-time data may address several of the research gaps identified in this research.

Factors Affecting Accident Severity Inside and Outside Urban Areas in Greece

Objectives: This research aims to identify and analyze the factors affecting accident severity through a macroscopic analysis, with a focus on the comparison between inside and outside urban areas. Disaggregate road accident data for Greece for the year 2008 were used. Methods: Two models were developed, one for inside and one for outside urban areas. Because the dependent variable had 2 categories, killed/severely injured (KSI) and slightly injured (SI), the binary logistic regression analysis was selected. Furthermore, this research aims to estimate the probability of fatality/severe injury versus slight injury as well as to calculate the odds ratios (relative probabilities) for various road accident configurations. The Hosmer and Lemeshow statistic and other diagnostic tests were conducted in order to assess the goodness-of-fit of the model. Results: From the application of the models, it appears that inside urban areas 3 types of collisions (sideswipe, rear-end, with fixed object/parked car), as well as involvement of motorcycles, bicycles, buses, 2 age groups (18–30 and older than 60 years old), time of accident, and location of the accident, seem to affect accident severity. Outside urban areas, 4 types of collisions (head-on, rear-end, side, sideswipe), weather conditions, time of accident, one age group (older than 60 years old), and involvement of motorcycles and buses were found to be significant. Conclusions: Factors affecting road accident severity only inside urban areas include young driver age, bicycles, intersections, and collision with fixed objects, whereas factors affecting severity only outside urban areas are weather conditions and head-on and side collisions, demonstrating the particular road users and traffic situations that should be focused on for road safety interventions for the 2 different types of networks (inside and outside urban areas). The methodology and the results of this research may provide a promising tool to prioritize programs and measures to improve road safety in Greece and worldwide.

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials

INTRODUCTION The effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece. METHOD Random Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively. RESULTS Regarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity. CONCLUSIONS The study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms. PRACTICAL APPLICATION The identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials.

Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data

ABSTRACT Objective: Understanding the various factors that affect accident risk is of particular concern to decision makers and researchers. The incorporation of real-time traffic and weather data constitutes a fruitful approach when analyzing accident risk. However, the vast majority of relevant research has no specific focus on vulnerable road users such as powered 2-wheelers (PTWs). Moreover, studies using data from urban roads and arterials are scarce. This study aims to add to the current knowledge by considering real-time traffic and weather data from 2 major urban arterials in the city of Athens, Greece, in order to estimate the effect of traffic, weather, and other characteristics on PTW accident involvement. Methods: Because of the high number of candidate variables, a random forest model was applied to reveal the most important variables. Then, the potentially significant variables were used as input to a Bayesian logistic regression model in order to reveal the magnitude of their effect on PTW accident involvement. Results: The results of the analysis suggest that PTWs are more likely to be involved in multivehicle accidents than in single-vehicle accidents. It was also indicated that increased traffic flow and variations in speed have a significant influence on PTW accident involvement. On the other hand, weather characteristics were found to have no effect. Conclusions: The findings of this study can contribute to the understanding of accident mechanisms of PTWs and reduce PTW accident risk in urban arterials.

A review of powered-two-wheeler behaviour and safety

Powered-two-wheelers (PTWs) constitute a very vulnerable type of road users. The notable increase in their share in traffic and the high risk of severe accident occurrence raise the need for further research. However, current research on PTW safety is not as extensive as for other road users (passenger cars, etc.). Consequently, the objective of this research is to provide a critical review of research on Power-Two-Wheeler behaviour and safety with regard to data collection, methods of analysis and contributory factors, and discuss the needs for further research. Both macroscopic analyses (accident frequency, accident rates and severity) and microscopic analyses (PTW rider behaviour, interaction with other motorised traffic) are examined and discussed in this paper. The research gaps and the needs for future research are identified, discussed and put in a broad framework. When the interactions between behaviour, accident frequency/rates and severity are co-considered and co-investigated with the various contributory factors (riders, other users, road and traffic environment, vehicles), the accident and injury causes as well as the related solutions are better identified.

Relationship Between Motorcyclists’ Attitudes, Behavior, and Other Attributes With Declared Accident Involvement in Europe

Objective: The objective of this study is to investigate patterns of road safety attitudes and behaviors of motorcyclists in Europe on the basis of the results of the pan-European questionnaire-based survey SARTRE-4, carried out in late 2010 in 18 European countries and Israel. In addition, we attempt to explore the link between attitudes, behaviors, and other motorcyclist attributes with motorcyclist involvement in accidents in the past 3 years, in which someone, including the rider, was injured and received medical attention as stated in the motorcyclists’ responses. Methods: The various components of motorcyclist attitudes and behaviors such as reasons for driving a motorcycle, driving while impaired, perceived risk factors, and risk-taking behavior were determined by means of a principal component analysis (PCA) on 38 variables contained in the survey. A binary logistic regression model was then applied in order to link the attitudes and the stated behavior with the declared involvement in past accidents. Results: The results revealed 8 components. Component 1 (driving while impaired and speeding accident factors), component 2 (motorcycle benefits), component 3 (perceived risk of maneuvers), component 4 (sensation seeking), component 5 (road, vehicle, and environmental risk factors), component 7 (no modal options), and component 8 (attitudes toward drinking and friends’ drinking) are associated with stated preferences and attitudes, whereas component 6 (dangerous and angry behaviors) is associated with stated behavior. Moreover, it was found that motorcyclists who tend to have dangerous attitudes and behaviors as well as younger motorcyclists are more likely to have been involved in an accident. It was also showed that driving exposure is positively associated with increased probability of a past accident. Conclusions: The findings of the study provide some insight into the association between attitudes, behaviors, and declared past accident involvement. Furthermore, the analysis of such large databases with the inclusion of many different countries constitutes a step for further research in the field of motorcyclists’ behaviors and safety. Supplemental materials are available for this article. Go to the publishers online edition of Traffic Injury Prevention to view the supplemental file.

Meta-analysis of the effect of road work zones on crash occurrence

There is strong evidence that work zones pose increased risk of crashes and injuries. The two most common risk factors associated with increased crash frequencies are work zone duration and length. However, relevant research on the topic is relatively limited. For that reason, this paper presents formal meta-analyses of studies that have estimated the relationship between the number of crashes and work zone duration and length, in order to provide overall estimates of those effects on crash frequencies. All studies presented in this paper are crash prediction models with similar specifications. According to the meta-analyses and after correcting for publication bias when it was considered appropriate, the summary estimates of regression coefficients were found to be 0.1703 for duration and 0.862 for length. These effects were significant for length but not for duration. However, the overall estimate of duration was significant before correcting for publication bias. Separate meta-analyses on the studies examining both duration and length was also carried out in order to have rough estimates of the combined effects. The estimate of duration was found to be 0.953, while for length was 0.847. Similar to previous meta-analyses the effect of duration after correcting for publication bias is not significant, while the effect of length was significant at a 95% level. Meta-regression findings indicate that the main factors influencing the overall estimates of the beta coefficients are study year and region for duration and study year and model specification for length.

Time series and support vector machines to predict powered-two-wheeler accident risk and accident type propensity: A combined approach

ABSTRACT Predicting road accident probability by exploiting high-resolution traffic data has been a continuously researched topic in the last years. However, there is no specific focus on powered-two-wheelers. Furthermore, urban arterials have not received adequate attention so far because the majority of relevant studies considers freeways. This study aims to contribute to the current knowledge by utilizing support vector machine (SVM) models for predicting powered-two-wheeler (PTW) accident risk and PTW accident type propensity on urban arterials. The proposed methodology is applied on original and transformed time series of real-time traffic data collected from urban arterials in Athens, Greece, for 2006 to 2011. Findings suggest that PTW accident risk and PTW accident type propensity can be adequately defined by the prevailing traffic conditions. When predicting PTW accident risk, the original traffic time series performed better than the transformed time series. On the other hand, when PTW accident type is investigated, neither of the two approaches clearly outperformed the other, but the transformed time series perform slightly better. The results of the study indicate that the combination of SVM models and time-series data can be used for road safety purposes especially by utilizing real-time traffic data.

How many crashes are caused by driver interaction with passengers? A meta-analysis approach

INTRODUCTION Conversation and other interactions with passengers while driving induce a level of distraction to the person driving. METHOD This paper conducts a qualitative literature review on the effect of passenger interaction on road safety and then extends it by using meta-analysis techniques. RESULTS The literature review indicates that the distraction due to passengers is a very frequent risk factor, with detrimental effects to various driving behavior and safety measures (e.g., slower reaction times to events, increased severity of injuries in crashes), associated with non-negligible proportions of crashes. Particular issues concern the effect of passenger age (children, teenagers) on which the literature is inconclusive. Existing studies vary considerably in terms of study methods and outcome measures. Nevertheless, a meta-analysis could be carried out regarding the proportion of crashes caused by this distraction factor. The selection of studies for the meta-analysis was based on a rigorous method including specific study selection criteria. The findings of the random-effects meta-analyses that were carried out showed that driver interaction with passengers causes a non-negligible proportion of road crashes, namely 3.55% of crashes regardless of the age of the passengers and 3.85% when child and teen passengers are excluded. Both meta-estimates were statistically significant, revealing the need for further research, especially considering the role of passenger age. PRACTICAL APPLICATIONS Stakeholders could make good estimates on future crash numbers and causes and take action in order to counter the effects of passenger interaction.

Meta-Analysis of Crash-Risk Factors in Freeway Entrance and Exit Areas

AbstractEntry and exit areas are considered critical parts of freeways and expressways. In order to meet traffic safety and operation requirements, it is important that ramps and speed-change lanes...