Search

The Internet of Things (IoT) constantly offers new opportunities and features to monitor and analyze driver behavior through wide use of smartphones, effective data collection and Big Data analysis, resulting in assessment and improvement of driver behavior and safety. The objective of the present study is to investigate the impact of detailed trip characteristics on the frequency of harsh acceleration and harsh braking events through an innovative smartphone application developed within the framework of BeSmart project. A 200-driver naturalistic experiment spanning 12 months is carried out since July 2019. During the first two months, participants were asked to drive in the way they usually did, without receiving any feedback on their driving behavior from the application. Over the subsequent two months, participants were provided with personalized feedback, a trip list and a scorecard regarding their driving behavior, allowing them to identify their critical deficits or unsafe behaviors. Some of the most important risk factors, such as speed and driving above the speed limit, usage of mobile phone while driving and harsh events (acceleration and braking) are recorded through the application and subsequently analyzed. Generalized Linear Mixed-Effects Models were fitted to the trips of car drivers who made frequent trips for both experiment phases in order to model the frequencies of harsh events. Results indicate that maximum speed, the percentage of speeding duration and total trip duration are positively correlated with both harsh acceleration and harsh braking frequencies. On the other hand, the exposure metric of total trip distance was found to be negatively correlated with both harsh event types. A small positive correlation of the percentage of mobile use duration with harsh accelerations was also detected.

Introduction: COVID-19 has disrupted daily life and societal flow globally since December 2019; it introduced measures such as lockdown and suspension of all non-essential movements. As a result, driving activity was also significantly affected. Still, to-date, a quantitative assessment of the effect of COVID-19 on driving behavior during the lockdown is yet to be provided. This gap forms the motivation for this paper, which aims at comparing observed values concerning three indicators (average speed, speeding, and harsh braking), with forecasts based on their corresponding observations before the lockdown in Greece. Method: Time series of the three indicators were extracted using a specially developed smartphone application and transmitted to a back-end platform between 01/01/2020 and 09/05/2020, a time period containing normal operations, COVID-19 spreading, and the full lockdown period in Greece. Based on the collected data, XGBoost was employed to identify the most influential COVID-19 indicators, and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models were developed for obtaining forecasts on driving behavior. Results: Results revealed the intensity of the impact of COVID-19 on driving, especially on average speed, speeding, and harsh braking per 100 km. More specifically, speeds were found to increase by 2.27 km/h on average compared to the forecasted evolution, while harsh braking/100 km increased to almost 1.51 on average. On the bright side, road crashes in Greece were reduced by 49% during the months of COVID-19 compared to the non-COVID-19 period.

This paper tries to identify and investigate the most significant factors that influenced the relationship between COVID-19 pandemic metrics (i.e., COVID-19 cases, fatalities and reproduction rate) and restrictions (i.e., stringency index and lockdown measures) with driving behavior in the entire 2020. To that aim, naturalistic driving data for a 12-month timeframe were exploited and analyzed. The examined driving behavior variables included harsh acceleration and harsh braking events concerning the time period before, during and after the lockdown measures in Greece. The harsh events were extracted using data obtained by a specially developed smartphone application which were transmitted to a back-end telematic platform between the 1st of January and the 31st of December, 2020. Based on the collected data, XGBoost feature analysis algorithms were deployed in order to obtain the most significant factors. Furthermore, a comparison among the first COVID-19 lockdown (i.e., February to May 2020), the second one (i.e., August to November 2020) and the period without COVID-19 restrictions was drawn. COVID-19 new cases and new fatalities were the most significant factors related to COVID-19 metrics impacting driving behavior. Additionally, the correlation between driving behavior with other factors (i.e., distance travelled, mobile use, driving requests, driving during risky hours) was revealed. Furthermore, the differences and similarities of the harsh events between the two lockdown periods were identified. This paper tries to fill this gap in existing literature concerning a feature analysis for the entire 2020 and including the first and second lockdown restrictions of the COVID-19 pandemic in Greece.

Powered Two-Wheeler (PTW) riders constitute a very vulnerable group of road users, while riding a PTW is considerably more dangerous than using any other motor vehicle. Behavioral issues have been identified major moderating factors to PTW crashes, as riders display great variability in their attitudes towards road safety. Τhe aim of this paper is to present a thorough, overarching structure of relationships correlating various unsafe stated PTW rider behaviors (riding after alcohol consumption, speeding, helmet use and texting) with several self-reported attitude parameters and factors regarding rider perspectives on traffic rule strictness and social desirability. A structural equation model (SEM) was developed using data from the ESRA2 survey, which provided a broad sample encompassing 5,958 respondent riders from 32 countries. Numerous statistical relationships were discovered and quantified correlating the four examined unsafe rider behaviors with eight latent unobserved variables. All covariances between unsafe behaviors were found to be positive and statistically significant, indicating that a rider who will engage more frequently in every single one of the four examined unsafe riding behaviors is more likely to also engage in all the others as well.

Driving under the influence of alcohol, drugs and fatigue are all important factors of crash causation. Exploring the link between driver attitudes and crash involvement provides understanding on these important issues. To that end, questionnaire answers of car drivers disclosing their attitudes on the impacts of driving under the influence of alcohol, drugs and fatigue, and their relationship with past crash involvement as car drivers were analysed. A two-step approach is adopted: Principal Component Analysis (PCA) was employed to consolidate relative questions in numeric factor quantities. Afterwards, binary logistic regression was implemented on the calculated component scores to determine the impact of perspectives of road users for each factor on past crash involvement of car drivers. Data from the international ESRA2015 survey were utilized. PCA indicated that it is possible to meaningfully merge 29 ESRA2015 questions relevant to driving under the influence of alcohol, drugs and fatigue into 8 informative components accounting for an adequate percentage of variance. Binary logistic analysis indicated that components involving overall personal and communal acceptance of impaired driving, overall and past year personal behaviour towards impaired driving and frequency of typical journey checks by traffic police were all quantities positively correlated with past crash involvement.

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

Historical road crash data are the main indicator for measuring road safety outcomes. Over the past few decades, significant efforts have been made in obtaining and exploiting Surrogate Safety Measures (SSMs). SSMs have the potential to provide excellent sustainable road safety indicators and proxy measurements which can complement traditional historical crash analyses or even substitute them. By using SSMs, crash data collection demands can be bypassed and areas can be investigated before crashes occur. Due to such advantages, the objective of the present research is to provide a review of the scientific literature regarding studies exploiting SSMs for historical crash record investigations. Specifically, 34 studies were examined, providing insights on the different types of SSMs collected under real road environment conditions, the way they are collected, their connection with specific road crash types, and the type of the developed statistical models are examined and discussed. Particular focus is also placed on the temporal dimension of the collection period of both SSMs and road crashes. Finally, the overall trends deriving from the reviewed studies are summarized and future research directions are provided.

Introduction: The number of road fatalities have been falling throughout the European Union (EU) over the past 20 years and most Member States have achieved an overall reduction. Research has mainly focused on protecting car occupants, with car occupant fatalities reducing significantly. However, recently there has been a plateauing in fatalities amongst ‘Vulnerable Road Users’ (VRUs), and in 2016 accidents involving VRUs accounted for nearly half of all EU road deaths. Method: The SaferWheels study collected in-depth data on 500 accidents involving Powered Two-Wheelers (PTWs) and bicycles across six European countries. A standard in-depth accident investigation methodology was used by each team. The Driver Reliability and Error Analysis Method (DREAM) was used to systematically classify accident causation factors. Results: The most common causal factors related to errors in observation by the PTW/bicycle rider or the driver of the other vehicle, typically called ‘looked but failed to see’ accidents. Common scenarios involved the other vehicle turning or crossing in front of the PTW/bicycle. A quarter of serious or fatal injuries to PTW riders occurred in accidents where the rider lost control with no other vehicle involvement. Conclusions: Highly detailed data have been collected for 500 accidents involving PTWs or bicycles in the EU. These data can be further analyzed by researchers on a case-study basis to gain detailed insights on such accidents. Preliminary analysis suggests that ‘looked but failed to see’ remains a common cause, and in many cases the actions of the other vehicle were the critical factor, though PTW rider speed or inexperience played a role in some cases. Practical Applications: The collected data can be analyzed to better understand the characteristics and causes of accidents involving PTWs and bicycles in the EU. The results can be used to develop policies aimed at reducing road deaths and injuries to VRUs.

The objective of the present study is twofold: (i) to explore the riding behavior of motorcyclists while speeding, based on detailed riding analytics collected by smartphone sensors, and (ii) to investigate whether personalized feedback can improve motorcyclist behavior. In order to achieve the objective, a naturalistic riding experiment with a sample of 13 motorcyclists based on a smartphone application developed within the framework of the BeSmart project was conducted. Using risk exposure and riding behavior indicators calculated from smartphone sensor data, Generalized Linear Mixed-Effects Models are calibrated to correlate the percentage of riding time over the speed limit with other riding behavior indicators. An overall model was developed for all trips, as well as separate models for the parts of trips realized on different road types (urban and rural). Results indicate that the parameters of trip duration, distance driven during risky hours, morning peak hours and the number of harsh accelerations are all determined as statistically significant and positively correlated with the percentage of speeding time. Additionally, the provision of rider feedback and riding during afternoon peak hours are statistically significant and correlated with decreased percentages of speeding time. The outcomes of this study entail both scientific and social impacts. The present research contributes a preliminary example of the quantitative documentation of the impact of personalized rider feedback on one of the most important human risk factors; speeding. The ultimate objective when providing feedback to riders is to: (i) trigger their learning and self-assessment process, thus enabling them to gradually improve their performance and (ii) monitor the shift of riding behavior as the application provides feedback. The present results capture and quantify the positive effects of rider feedback, thus providing needed impetus for larger-scale applications as well as relevant policy interventions.

The objective of this study is the investigation of the effect of tourism on road crashes. For this purpose, police data on road crashes in Greece for the 5-year period of 2011 to 2015 were collect...

Autonomous point to point shuttles are an emerging paradigm of a future mobility-on-demand ecosystem. However, the traffic and environmental impacts of their operation are largely under researched especially in relation to influential infrastructure related factors and service-related specifications. The scope of this study is to reveal the factors that may affect the degree and magnitude of the road segment level impacts of an autonomous urban shuttle service (AUSS) operating in a city using microsimulation and structural equation modeling (SEM). For the purposes of this research, a systematic framework is developed and applied in the city center of Athens (Greece), which encompasses different scenarios of operations including: (i) Baseline (no AUSS operation), (ii) AUSS operation with a dedicated lane during peak hour, (iii) AUSS operation mixed with regular traffic during peak hour and (iv) AUSS operation mixed with regular traffic during off-peak hour. Two connected automated vehicle (CAV) profiles were used to model the advent of automation in the overall traffic: a cautious profile is introduced first, followed by a more aggressive profile. SEM findings indicate that the AUSS operation has a significant effect on cumulative travel time per segment and CO2 emissions per segment only during the scenario of mixed operation with traffic during off-peak hours. Additionally, the influence of the network geometry is correlated with reduced travel time and with increased CO2 emissions. Road traffic density was found to be positively correlated with both travel time and CO2 emissions, while the penetration of both cautious and aggressive CAVs was found to be negatively correlated with both indicators.

The aim of the present study is to conduct spatial analysis of harsh events of driving behavior across road segments of an urban road network. The adopted approach involved automating the segment characteristic extraction process for the urban network study area. Subsequently, naturalistic driving big data from an innovative smartphone application were map-matched to the segments that each driver traversed, and thus geometrical, road network and driver behavior spatial data frames were obtained per road segment. Global and local Moran's I coefficients were calculated based on a nearest-neighbour scheme, and indicated the presence of a certain degree of positive spatial autocorrelation both for harsh brakings (HBs) and for harsh accelerations (HAs). Furthermore, the creation of empirical and theoretical spherical variograms indicated that on average, about 190 m from each road segment centroid there is no observable spatial autocorrelation for HBs; the respective distance is 200 m for HAs. Geographically Weighted Poisson Regression (GWPR) models were used to model harsh event frequencies. Segment length and pass count are positively correlated with HB frequencies, while gradient and neighbourhood complexity are negatively correlated with HB frequencies. Curvature, segment length, pass count and the presence of traffic lights are positively correlated with HA frequencies. Road type and lane number were found to have a more circumstantial effect overall.

Planning of horizontal transport is a significant problem with material impact on the development budget and productivity of a container terminal. This contribution uses Queuing Theory, Petri Networks and Discrete Event Simulation to address the fleet size determination problem for tactical planning. Considering the different information and modelling effort required for the three methods, it is recommended that Queuing Theory be applied in the preliminary planning stage as it is conservative, while Discrete Event Simulation which can yield significantly more cost-efficient results is applied for the detailed planning stage. Further development would be still required towards an easily applicable tool based on Petri Nets for practitioners to use in current planning problems, but the methodology itself can provide reasonable yet conservative results at a preliminary planning stage.

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.

Introduction: Pedestrians are vulnerable road users exposed at risk during their interaction with vehicles in uncontrolled urban areas. In this paper, a critical overview of the literature and meta-analyses were conducted on the topic of pedestrian gap acceptance choice during midblock street crossings. Method: Α large number of studies examining personal and contextual factors affecting midblock crossing choices were identified. In an effort to condense research outputs, a quantitative approach was adopted alongside qualitative assessments. Meta-analyses were conducted to summarize the impacts of various predictor variables on pedestrian gap acceptance probabilities from binary logistic models in midblock locations. After application of a rigorous set of criteria, 14 publications were considered appropriate to conduct meta-analyses on beta coefficients of four factors: vehicle speed (VS), gap size (GS), waiting time (WT) and frequency of attempts (FA). Results: Statistically significant results were obtained for VS, GS and FA. Specifically, it was determined that for one unit increase in incoming VS, there is a 10% decrease (OR=0.9) in the odds of pedestrians crossing the road (accepting the incoming time gap) for pedestrians. For one unit increase in temporal GS, the odds of crossing the road become 7.22 times larger for pedestrians. For each crossing attempt, as measured by FA, the odds of crossing the road become 16.6 times larger for pedestrians. WT was determined to have a non-significant impact on pedestrian crossing odds. Conclusions: To the best of the authors' knowledge, this is the first time that a meta-analysis of the critical factors influencing gap acceptance of pedestrians in midblock street crossings is carried out. Study findings are useful for practitioners and policymakers to formulate appropriate management plans to reduce interactions of pedestrians and vehicles at uncontrolled urban midblock locations. Future research is needed to further understand the determinants of gap choice decision making.

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.

This study aims to estimate the overall impact of distraction due to operating in-vehicle information systems (IVIS) and similar devices while driving on road crashes. While similar research has been undertaken investigating the issue, varying results have been reported so far. Therefore a two-step approach was adopted: initially a review of the literature was conducted to identify key high quality studies and the parameters that they examined. Afterwards, meta-analyses were applied in order to estimate the overall effects of operating IVIS while driving on the absolute proportion of crashes (i.e. the proportion of total crashes due to IVIS). After applying a random effects meta-analysis to the findings of existing studies, it was found that 1.66% of crashes occur due to operating devices in total. In addition, it is indicated that about 0.6% of safety-critical incidents for professional drivers are due to in-vehicle device operation. The odds of crashes influenced by IVIS operation were also estimated and were found to be very low. From the findings of the present review and the meta-analysis, it is suggested that device operation as a risk factor while driving is a less researched aspect of driver distraction than others, and more studies would improve result estimates and transferability, especially for professional drivers. This study summarizes concisely the current effect of driver interaction with in-vehicle information systems on crashes, which might become considerably pertinent in view of the increasing deployment of vehicles with increasing levels of automation.

Economic evaluations of road safety measures are only rarely published in the scholarly literature. We collected and (re-)analyzed evidence in order to conduct cost-benefit analyses (CBAs) for 29 road safety measures. The information on crash costs was based on data from a survey in European countries. We applied a systematic procedure including corrections for inflation and Purchasing Power Parity in order to express all the monetary information in the same units (EUR, 2015). Cost-benefit analyses were done for measures with favorable estimated effects on road safety and for which relevant information on costs could be found. Results were assessed in terms of benefit-to-cost ratios and net present value. In order to account for some uncertainties, we carried out sensitivity analyses based on varying assumptions for costs of measures and measure effectiveness. Moreover we defined some combinations used as best case and worst case scenarios. In the best estimate scenario, 25 measures turn out to be cost-effective. 4 measures (road lighting, automatic barriers installation, area wide traffic calming and mandatory eyesight tests) are not cost-effective according to this scenario. In total, 14 measures remain cost-effective throughout all scenarios, whereas 10 other measures switch from cost-effective in the best case scenario to not cost-effective in the worst case scenario. For three measures insufficient information is available to calculate all scenarios. Two measures (automatic barriers installation and area wide traffic calming) even in the best case do not become cost-effective. Inherent uncertainties tend to be present in the underlying data on costs of measures, effects and target groups. Results of CBAs are not necessarily generally valid or directly transferable to other settings.

Spatial analyses of crashes have been adopted in road safety for decades in order to determine how crashes are affected by neighboring locations, how the influence of parameters varies spatially and which locations warrant interventions more urgently. The aim of the present research is to critically review the existing literature on different spatial approaches through which researchers handle the dimension of space in its various aspects in their studies and analyses. Specifically, the use of different areal unit levels in spatial road safety studies is investigated, different modelling approaches are discussed, and the corresponding study design characteristics are summarized in respective tables including traffic, road environment and area parameters and spatial aggregation approaches. Developments in famous issues in spatial analysis such as the boundary problem, the modifiable areal unit problem and spatial proximity structures are also discussed. Studies focusing on spatially analyzing vulnerable road users are reviewed as well. Regarding spatial models, the application, advantages and disadvantages of various functional/econometric approaches, Bayesian models and machine learning methods are discussed. Based on the reviewed studies, present challenges and future research directions are determined.

Currently studies exploring the effect of real-time traffic and weather characteristics on crash severity are relatively limited, and studies focusing on powered two-wheelers (PTWs) are eve...

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as “hot topics” of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings - and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 ‘Synopses’ (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors).

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.

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out: Identification of human related risk factors - creation of a taxonomy, Consultation of relevant stakeholders and policy papers for identification of topic with high, priority ('hot topics'), Systematic literature search and selection of relevant studies on identified risk factors, Coding of studies, Analysis of risk factors on basis of coded studies, Synopses of risk factors, including accident scenarios, The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is, further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently.It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring.The following analysed risk factors were assessed as 'risky', 'probably risky' or 'unclear'. No risk factors were identified as 'probably not risky'.

Driving while fatigued is a considerably understudied risk factor contributing to car crashes every year. The first step in mitigating the respective crash risks is to attempt to infer fatigued driving from other parameters, in order to gauge its extend in road networks. The aim of this study is to investigate the extent to which declared fatigued driving behavior can be predicted based on overall driver opinions and perceptions on that issue. For that purpose, a broad cross-country questionnaire from the ESRA2 survey was used. The questionnaire is related to self-declared beliefs, perception, and attitudes towards a wide range of traffic safety topics. Initially, a binary logistic regression model was trained to provide causal insights on which variables affect the likelihood that a driver engaged in driving while fatigued. Drivers reporting driving under the influence of drugs, fatigue, or alcohol, as well as speeding, safety, and texting while driving or drivers who were more acceptable of fatigued driving were more likely to have recently driven while fatigued. In contrast, acceptability of other hazardous behaviors, namely mobile phone use and drunk driving, was negatively correlated with fatigued driving behavior, as were more responsible driver perspectives overall. To provide a more accurate detection mechanism, which would also incorporate non-linear effects, a Deep Neural Network (DNN) was subsequently trained on the data, slightly outperforming the binary logistic model. From the results of both models, it was concluded that declared fatigued driving behavior can be predicted from questionnaire data, providing new insights to fatigue detection.