State of the art on measuring driver state and technology-based risk prevention and mitigation Findings from the i-DREAMS project

Advanced vehicle automation and the incorporation of more digital technologies in the task of driving, bring about new challenges in terms of the operator/vehicle/environment framework, where human factors play a crucial role. This paper attempts to consolidate the state-of-the-art in driver state measuring, as well as the corresponding technologies for risk assessment and mitigation, as part of the i-DREAMS project. Initially, the critical indicators for driver profiling with regards to safety risk are identified and the most prominent task complexity indicators are established. This is followed by linking the aforementioned indicators with efficient technologies for real-time measuring and risk assessment and finally a brief overview of interventions modules is outlined in order to prevent and mitigate collision risk. The results of this review will provide an overall multimodal set of factors and technologies for driver monitoring and risk mitigation, essential for road safety researchers and practitioners worldwide<br

A systematic cost-benefit analysis of 29 road safety measures

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.acceptedVersio

Correlation of traffic characteristics with road accident severity and probability

205 σ.Ο στόχος της Διπλωματικής Εργασίας είναι η συσχέτιση των κυκλοφοριακών μεγεθών με τη σοβαρότητα και την πιθανότητα εμφάνισης οδικών ατυχημάτων. Για την επίτευξη των συγκεκριμένων στόχων συλλέχθηκαν δεδομένα για τα οδικά ατυχήματα που συνέβησαν στην λεωφόρο Κηφισίας στην Αθήνα κατά τη διάρκεια της πενταετίας 2006 - 2010 μέσω του Συστήματος Ανάλυσης Τροχαίων Ατυχημάτων του Τομέα Μεταφορών και Συγκοινωνιακής Υποδομής του Ε.Μ.Π.. Στη συνέχεια, τα στοιχεία οδικών ατυχημάτων συμπληρώθηκαν με τα αντίστοιχα κυκλοφοριακά δεδομένα της ταχύτητας και του κυκλοφοριακού φόρτου από το Κέντρο Διαχείρισης Κυκλοφορίας Αττικής. Για την ανάλυση αναπτύχθηκαν μαθηματικά μοντέλα λογιστικής παλινδρόμησης. Από την εφαρμογή των μοντέλων φαίνεται ότι η σοβαρότητα παθόντα οδικού ατυχήματος εξαρτάται από τον λόγο του κυκλοφοριακού φόρτου προς την ταχύτητα, τον τύπο οχήματος και τον τύπο ατυχήματος. Όταν τα ατυχήματα διαχωρίζονται σε εντός και εκτός ωρών αιχμής, μόνο ο λόγος των κυκλοφοριακών μεγεθών εμφανίζεται ως στατιστικά σημαντικός παράγοντας επιρροής. Επιπλέον, ο κυκλοφοριακός φόρτος αποτελεί τη μόνη παράμετρο η οποία φάνηκε να επηρεάζει στατιστικά σημαντικά την πιθανότητα εμφάνισης οδικού ατυχήματος.The objective of this Diploma Thesis is to correlate traffic characteristics with road accident severity and probability. In order to achieve these objectives, data concerning the road accidents occurred on Kifisias Avenue in Athens, Greece, during the period 2006 - 2010 were collected from the database of the Department of Transportation Planning and Engineering of the NTUA. Subsequently, traffic data were obtained from the Traffic Management Centre of Athens. For the analysis, logistic regression mathematical models were developed. The application of these models indicates that road accident severity is correlated with the logarithm of traffic density, the type of vehicle and the type of accident. When data are separated in two groups of peak and off-peak hour accidents, the parameter of traffic density is the only one appearing to be statistically significant. Furthermore, traffic volume is the only parameter found with a statistically significant impact on accident probability.Απόστολος Ν. Ζιακόπουλο

Χωρική ανάλυση οδικής ασφάλειας και συμπεριφοράς κυκλοφορίας με χρήση πολυπαραμετρικών δεδομένων υψηλής ευκρίνειας

The main objective of the present doctoral dissertation is the spatial analysis of harsh event frequencies in road segments using multi-parametric data, including (i) high resolution naturalistic driving and driver behavior data from smartphone sensors, (ii) microscopic road segment geometry and road network characteristic data from digital maps and (iii) high resolution traffic data. Naturalistic driving data were collected and processed with purpose-made spatial processing algorithms, performing critical functions such as derivation of additional geometrical characteristics, data merging and map-matching. The resulting spatial data-frames were then analyzed and modelled on a road segment basis. Moran's I coefficients, as well as merged and directional variograms were calculated. Spatial analyses were performed on two parallel pillars: (i) Prediction models were developed in an urban road network training area, with the intent to transfer them to a second urban road network testing area and assess their predictive performance and (ii) Causal models including road user behavior and traffic input data were calibrated in an urban arterial study area per traffic state, in order to investigate additional underlying correlations in an effort to further understand the phenomena of harsh braking and harsh acceleration frequencies. Geographically Weighted Poisson Regression (GWPR) models, Bayesian Conditional Autoregressive Prior (CAR) models and Extreme Gradient Boosting algorithms with random cross-validation (RCV XGBoost) and spatial cross-validation (SPCV XGBoost) were implemented. From the spatial analyses, numerous informative results were obtained. Spatial autocorrelation was identified in both harsh braking and harsh acceleration frequencies, and its range of influence was determined for each study area. In urban networks, certain geometrical characteristics were found to affect harsh braking frequencies per road segment: Segment length is positively correlated with harsh brakings, while gradient and neighborhood complexity are negatively correlated with them. Different geometrical characteristics were found to affect harsh acceleration frequencies per road segment: Segment length, curvature and the presence of traffic lights are positively correlated with harsh accelerations. For both harsh event types, pass count increased frequencies of both types of harsh events, while lane number and road type have more unclear circumstantial effects, depending on the utilized models. Furthermore, successful spatial predictions were conducted by averaging the results of all four methods, achieving accuracy of 87% for harsh brakings and 89% for harsh accelerations. In urban arterial segments, segment length and pass count were consistently positively correlated with harsh event occurrence overall. In addition, it was determined that different variables are significantly correlated with harsh event occurrence per traffic state: For harsh brakings in free flow conditions, speed difference between traffic and driver was found to exert a positive influence, while the influence of the averaged standardized current traffic volume was found to be negative. In synchronized flow conditions, average occupancy assumes a statistically significant positive correlation for harsh braking frequencies, while the influence of traffic volume was found to be circumstantially negative. For harsh accelerations in free flow conditions, the influence of average occupancy was found be consistently positive, as was the average mobile use seconds of drivers. In synchronized flow conditions, traffic volume was found to be positively correlated with harsh accelerations as well. In both traffic states, geometric and road network characteristic variables were found to have very circumstantial effects

A Review of Surrogate Safety Measures Uses in Historical Crash Investigations

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

Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests

The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought

Exploring speeding behavior using naturalistic car driving data from smartphones

The present research aimed to identify critical factors that affect speeding behavior. For that purpose, high-resolution smartphone data collected from a naturalistic driving experiment of 88 drivers were utilized, augmented with data from self-reported questionnaires. Using risk exposure and driving behavior indicators calculated from smartphone sensor data, as well as demographic characteristics and self-reported driving performance, statistical analysis was carried out for modelling the percentage of driving time over the speed limit, namely by means of generalized linear mixed-effects models. More precisely, an overall model was developed for all road environments, and additional separate models were developed for driving on urban and rural roads. The results from the interpretation of the estimated parameters of the models can be summarized as follows: the parameters of trip distance and mobile phone use while driving have been determined as statistically significant and positively correlated with the percentage of speeding time during a driver's trip. In the same context, male drivers and drivers in the age group of 18–34 also increase the percentages of speeding instances while driving. Regarding driving behavior as stated on the questionnaire, it seems that low frequencies of self-declared speeding (never or rarely) are statistically significant and negatively correlated with the percentage of speeding time. It is expected that this research can provide considerable gains to society, since the stakeholders including policy makers and industry could rely on the results and recommendations regarding risk factors that appear to be critical for safe driving

To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossings

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.</p

Comparing Machine Learning Techniques for Predictions of Motorway Segment Crash Risk Level

Motorways are typically the safest road environment in terms of injury crashes per million vehicle kilometres; however, given the high severity of crashes occurring therein, there is still space for road safety improvements. The objective of this study is to compare the classification performance of five machine learning techniques for predictions of crash risk levels of motorway segments. To that end, data on crash risk levels, driving behaviour metrics, and road geometry characteristics of 668 motorway segments were exploited. The utilized dataset was divided into training and test subsets, with a proportion of 75% and 25%, respectively. The training subset was used to train the models, whereas the test subset was used for the evaluation of their performance. The response variable of the models was the crash risk level of the considered motorway segments, while the predictors were various road design characteristics and naturalistic driving behaviour metrics. The techniques considered were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Among the five techniques, the Random Forest model achieved the best classification performance (overall accuracy: 89.3%, macro-averaged precision: 89.0%, macro-averaged recall: 88.4%, macro-averaged F1 score: 88.6%). Moreover, the Shapley additive explanations were calculated in order to assist with the interpretation of the model’s outcomes. The findings of this study are particularly useful as the Random Forest model could be used as a highly promising proactive road safety tool for identifying potentially hazardous motorway segments

A meta-analysis of the impacts of operating in-vehicle information systems on road safety

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