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).@en

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.@en

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.@en

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

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

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.@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

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.@en

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.@en

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.@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