The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

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Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

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Driver behavior is a key determinant of road safety. Risky behaviors can be measured and quantified using traditional and modern methods, which enables the assessment of the driver's behavior. Following lessons from safety science, it is important to analyse and manage risks factors to optimize interventions in the transport context at the territory. The main objective of the paper is to explore the star rating of the driver's behavior to provide credible road safety monitoring and identification of the factors that most contribute to risky driving behaviors. The Data Envelopment Analysis method for selection of most significant factors is used for star rating of driver's behavior. The subject of the analysis encompasses 18 factors for 27 police administration units (PAUs) in the Republic of Serbia. The results are analysed regarding the following aspects: 1) star rating of driver's behavior and 2) defining the stages of the periodic factors monitoring for each PAU analysed. Using a mobile phone while driving, not using child restraints system, alcohol drunk driving during the night and seat belt use at rear seats of passenger vehicles were identified as the most significant factors associated with risky driving behaviors. Monitoring a broader set of factors helps to identify the strengths and weaknesses of a territory's road safety system. Star rating of driver's behavior as a tool intended for decision-makers ensures monitoring, management, the exchange of evidence-based and customized best practices and defining earlier goal-oriented actions to manage driver's behavior and to prevent risky driving.@en

Connected, Cooperative and Automated Mobility (CCAM) are expected to be introduced in increasing numbers over the next decade based on the rapidly developing capability of modern technologies. The need for policies around the introduction of CCAM is starting to arise, based on the evaluation of the likely impact of different technologies, with the aim to capture the benefits of automation and ensure that new technologies contribute to wider policy objectives. The Horizon 2020 Levitate project aims to investigate the potential short, medium and long term impacts of CCAM, through an innovative multi-disciplinary impact assessment methodology, which will be incorporated within a new web-based policy support tool to enable city and other authorities to forecast impacts of CCAM on urban areas. The aim of the present paper is to provide an insight on the development of the Levitate Policy Support Tool (PST), the use cases, parameters and impacts considered and the methodologies applied for the estimation of relationships and impacts of connected and automated transport systems. This policy support tool will comprise a knowledge and an estimator module and will include forecasting and backcasting systems providing estimates for different types of impacts and allowing comparative analyses. The Policy Support Tool will integrate the methodologies and findings of the Levitate project, in order to develop an overall framework for the assessment of impacts, benefits and costs of CCAM for different automation and penetration levels and on different time horizons, as well as a public toolkit and a decision support system allowing the testing of various policy scenarios on the basis of the needs of relevant stakeholders.@en

Rapid technological advances leave limited margins for the preparation of cities to receive Connected, Cooperative and Automated Mobility (CCAM). The LEVITATE project endeavours to develop an open access web-based Policy Support Tool (PST), that will provide decision makers at all levels with access to LEVITATE methodologies and results. The aim of the PST is to consolidate the outputs of different methods into an overall framework for the assessment of impacts, benefits and costs of CCAM, for different automation and penetration levels and on different time horizons. The PST comprises two modules: the Knowledge and the Estimator module, which includes a forecasting and a backcasting sub-system. The present research provides an insight of the PST, by presenting the studied automation use cases, parameters and impacts of CCAM, the applied methodologies and the online tool.@en

Automation in urban freight transport is an important milestone for city logistics, but it will most likely be challenging due to the complex traffic situations. The aim of the present paper is to provide an insight in the impact assessment method used and the results related to parcel delivery in Vienna. While the parcel volume is soaring due to the popularity of e-commerce–and especially accelerated by COVID, cities are thinking about the future delivery system. Automation and consolidation are expected to bring disruptive changes to the system we know today. By applying analytical methods, we show which impacts at what magnitude we may expect from the changes brought by automation in freight transport. We consider the direct impacts consisting of fleet size, freight mileage and fleet operation costs, as well as the wider impacts consisting of parking space, public health and road safety.@en

This paper attempts to shed light on the temporal evolution of driving safety efficiency with the aim to acquire insights useful for both driving behavior and road safety improvement. Data exploited herein are collected from a sophisticated platform that uses smartphone device sensors during a naturalistic driving experiment, at which the driving behavior from a sample of two hundred (200) drivers during 7-months is continuously recorded in real time. The main driving behavior analytics taken into consideration for the driving assessment include distance travelled, acceleration, braking, speed and smartphone usage. The analysis is performed using statistical, optimization and machine learning techniques. The driver's safety efficiency index is estimated both in total and in several consecutive time windows to allow for the investigation of safety efficiency evolution in time. Initial data analysis results to the most critical components of microscopic driving behaviour evolution, which are used as inputs in the k-means algorithm to perform the clustering analysis. The main driving characteristics of each cluster are identified and lead to the conclusion that there are three main driving groups of the a) moderate drivers, b) unstable drivers and c) cautious drivers.@en

Intraindividual variability is a fundamental behavioural characteristic of aging but has been examined to a very limited extent in driving. This study investigated intraindividual variability in driving simulator measures in healthy drivers of different ages using the coefficient of variation (COV) as a variability measure. Participants were healthy volunteers who were regular drivers, who were divided into a “young” group, a “middle-aged” group, and an “old” group. They drove in two environments (rural, 72 drivers; urban, 60 drivers), under conditions of moderate and high traffic load, without and with distraction (conversation). Significant differences in COV were observed in the rural condition for headway distance and lateral position as a function of traffic load, with high traffic (without and with distraction) resulting in increased COV of headway and decreased COV of lateral position. Significant differences in COV were observed in the urban condition for headway distance only, with high traffic (without and with distraction) resulting in increased COV of headway. No age effects were found for any of the driving conditions. The results indicate that traffic load affected headway distance and lateral position in opposite directions in all three age groups: high traffic resulted in increased variability of headway in both rural and urban conditions but in decreased variability of lateral position in the rural conditions compared to moderate traffic irrespective of distraction. The study indicates that driving conditions affect the intraindividual variability of driving measures in selective ways, which may be linked to the extent of automatization of the driving variables and to adaptive changes to traffic condition challenges.

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The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.@en

This research aims to correlate drivers' characteristics with their safety performance. In order to achieve this objective, two different data sets were used deriving from 12 drivers who participated on an on-road driving experiment while being assessed by a safety behaviour expert. Drivers participating in the experiment also responded to the questionnaire administered, which mainly included self-evaluation questions along with other that concerned the condition of their vehicle, demographics, driving experience etc. The analysis of drivers' behavior was carried out using the statistical methods of factor analysis and linear regression analysis. Three groups that characterize drivers' perception of careless, aggressive and cautious driving behavior were derived from factor analysis. Moreover, linear regression analysis revealed that driving experience, headways, self-reported driving skill and defensive driving positively affect the overall on-road driving performance score. More precisely, it was ascertained that driving experience leads to statistically significant increase in overall on-road driving performance score. Other factors such as defensive driving and keeping safe distances from other vehicles. The findings of this research could be exploited for providing feedback on drivers regarding their driving behavior in order to further reduce their driving risk.@en

This paper deals with the problem of improving the existing optimization techniques for Data Envelopment Analysis (DEA). The algorithm proposed herein is a combination of the "quickhull algorithm" and a DEA algorithm written in Python programming language. To the best of the authors' knowledge no prior effort has been made to date to propose a methodology for reducing the running time of a DEA problem that incorporates multiple inputs and outputs. The algorithmic implementation is applied on the existing problem of driving efficiency evaluation by exploiting a driving data sample of 10,088 trips collected from smartphone devices. Results indicate that the proposed algorithm is performing relatively well for Big Data compared to other existing DEA algorithmic methodologies that yield the same optimal solution such as Standard DEA and RBE DEA methodologies. The results obtained are calculated for the test sets of 100, 500, 1000, 5,000 and 10,088 Decision-Making-Units (DMUs) and compared in terms of running time of each of the algorithms applied. The results of per trip analysis can be exploited in order to classify trips into different efficiency categories (such as efficient, less efficient, non-efficient) and present their main characteristics.@en

The aim of this paper is the development of driver speed models based on detailed driving data collected from smartphone sensors. More specifically, this research investigates to which extent various driving behaviour parameters (harsh acceleration and deceleration events, driving distance, percentage of driving time per different road types, etc.) interact with each other and how these might potentially serve as driving speed predictors. Real time driving behaviour data collection was carried out using a smartphone application. Data were collected from 100 drivers and a total of 18,853 trips between July and December 2016. Six different linear regression models are developed to predict average driving speed under different driving conditions. One model for each road type (urban, rural and highways), one model for the risky hours period and one for the rest of the day, and a general model. The results indicated that the distance covered by the driver, the number of the harsh events occurred and the average deceleration are the strongest predictors of the average speed of a driver.@en

Objectives: To examine the driving variables that predict accident probability in mild dementia due to Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy older control drivers in simulated driving. To compare the three groups in mean performance and in frequency of scores exceeding 1.5 SD from the mean. Methods/Design: Participants were 37 drivers with MCI, 16 drivers with AD, and 21 control drivers over the age of 52. Driving measures were derived from four rural driving conditions: moderate traffic without and with distraction and high traffic without and with distraction. The measures were z-transformed based on the performance of 90 control drivers of different ages. Two unexpected incidents occurred per condition, requiring the sudden breaking to avoid an accident. Results: Drivers with AD showed significantly lower average speed, speed variability, greater headway distance, headway variability and average reaction time (RT) than control drivers. Drivers with MCI showed significantly lower average speed, greater headway distance and average RT than control drivers in the two conditions of distraction. No differences were found in accident probability. Drivers with AD had more deviant scores than both control drivers and drivers with MCI in most comparisons. Predictors of accident probability were average RT, speed variability and lateral position variability but MCI and AD status were not significant predictors in any of the regression models. Conclusions: Despite significant differences in performance, drivers with MCI and AD did not differ in accident probability from control drivers. An individualized approach of examining individual driving performance is recommended.

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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.@en

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).

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The European Road Safety Decision Support System (roadsafety-dss.eu) is an innovative system providing the available evidence on a broad range of road risks and possible countermeasures. This paper describes the scientific basis of the DSS. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation instrument (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs.@en

The aim of this paper is to explore driving behaviour during mobile phone use on the basis of detailed driving analytics collected by smartphone sensors. The data came from a sample of one hundred drivers (18,850 trips) during a naturalistic driving experiment over four months. A specially developed smartphone application was used, through which driving exposure and behaviour metrics are captured by the smartphone sensors and transmitted to a back-end platform. The data are processed by Machine Learning algorithms yielding exposure (e.g. distance travelled per road type and time of day) and behaviour indicators (e.g. speeding, speed and acceleration variations, harsh braking, harsh manoeuvring, use of mobile phone etc.). Mixed binary logistic regression models were developed to investigate whether mobile phone use during a trip is correlated with other driving metrics, and can be accurately “detected” based on them. A model for all trips was developed, as well as models for trips on different road types (urban, rural, highway). Exposure metrics found to be significantly associated with the probability of mobile phone use are trip length, and driving off-morning rush. Exceeding the speed limits and the number of harsh events (particularly harsh cornering), are all negatively associated with the probability of mobile phone use. A general pattern of less speeding and smoother driving appears indicative of mobile phone use, in line with known assumptions of driver compensatory behaviour. The results suggest that mobile phone use while driving may be accurately predicted by the model in more than 70% of cases.

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This paper aims to provide a methodological framework for the comparative evaluation of driving safety efficiency based on Data Envelopment Analysis (DEA). The analysis considers each driver as a Decision Making Unit (DMU) and aims to provide a relative safety efficiency measure to compare different drivers based on their driving performance. The last is defined based on a set of driving analytics (e.g. distance travelled, speed, accelerations, braking, cornering and smartphone usage) collected using an innovative data collection scheme, which is based on the continuous recording of driving behavior analytics in real time, using smartphone device sensors. Safety efficiency is examined in terms of speed limit violation, driving distraction, aggressiveness and safety on urban, rural and highway road and in an overall model. DEA models are identifying the most efficient drivers that lie on the efficiency frontier and act as peers for the rest of the non-efficient drivers. The proposed methodological framework is tested on data from fifty-six (56) drivers during a 7-months period. Findings help distinguish the most efficient drivers from those that are less efficient. Moreover, the efficient level of inputs and outputs to switch from non-efficiency to the efficiency frontier is identified. Results also provide a potential for classification of the driving sample based on drivers’ comparative safety efficiency. The main characteristics of the most and less efficient drivers are consequently analyzed and presented. Most common inefficient driving practices are identified (aggressive, risky driving, etc.) and driving behavior is comparatively evaluated and analyzed.@en