The European road safety decision support system. A clearinghouse of road safety risks and measures, Deliverable 8.3 of the H2020 project SafetyCube

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) that 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. The core of the SafetyCube project is a comprehensive analysis of accident risks and the effectiveness and cost-benefit of safety measures, focusing on road users, infrastructure, vehicles and post-impace care, framed within a Safe System approach ,with road safety stakeholders at the national level, EU and beyond having involvement at all stages. The present Deliverable (8.3) outlines the methods and outputs of SafetyCube Task 8.3 - ‘Decision Support System of road safety risks and measures’. A Glossary of the SafetyCube DSS is available to the Appendix of this report. The identification and assessment of user needs for a road safety DSS was conducted on the basis of a broad stakeholders’ consultation. Dedicated stakeholder workshops yielded comments and input on the SafetyCube methodology, the structure of the DSS and identification of road safety "hot topics" for human behaviour, infrastructure and vehicles. Additionally, a review of existing decision support systems, was carried out; their functions and contents were assessed, indicating that despite their usefulness they are of relatively narrow scope.... continue

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub

Developing personalised braking and steering thresholds for driver support systems from SHRP2 NDS data

Examining the relationships between the factors associated with the crash development enabled the realisation of driver support systems aiming to proactively avert and control crash causation at various points within the crash sequence. Developing such systems requires new insights in personalised pre-crash driver behaviour with respect to braking and steering to develop crash prevention strategies. Therefore, the current study utilises Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS) data to investigate personalised steering and braking thresholds by examining the last stage of a crash sequence. More specifically, this paper carried out an in-depth examination of braking and steering manoeuvres observed in the final 30 s prior to safety critical events. Two algorithms were developed to extract braking and steering events by examining deceleration and yaw rate and another developed and applied to determine the sequence of the manoeuvres. Based on the analysis, thresholds for detecting emerging situations were recommended. The investigation of driver behaviour before the safety critical events, provides valuable insights into the transition from normal driving to safety critical scenarios. The results indicate that 20% of the drivers did not react to the impending event suggesting that they were not aware of the imminent safety critical situation. Future development of Advanced Driver Assistance Systems (ADAS) can focus on individual drivers’ needs with tailored activation thresholds. The developed algorithms can facilitate driver behaviour and safety analysis for NDS while the thresholds recommended could be exploited for the design of new driver support systems

What came before the crash? An investigation through SHRP2 NDS data

Investigating crash progression through naturalistic driving studies (NDS) could give valuable insights in crash causation analysis and thus, benefit crash prevention. This study utilises NDS data from the Strategic Highway Research Program 2 (SHRP2 NDS data) to look into the whole crash sequence, from a normal driving situation until a crash or a near-crash event. The objectives are to explore vehicle kinematics before the event, investigate the feasibility of crash risk indicators to detect the early stages of crash development and further examine the factors affecting Time To Collision (TTC) values during the crash sequence. An empirical approach and a multilevel mixed effects modelling technique were followed. The results reveal that longitudinal acceleration, lateral acceleration and yaw rate can be reliable indicators for detecting deviations from normal driving. Moreover, TTC values are affected by vehicle type, speed of the ego vehicle, longitudinal acceleration and time within the crash sequence. The model indicates a timestamp where a detectable reduction in TTC values occurs, which could be a first step towards more effective Advanced Driver Assistance Systems (ADAS) aiming to halt early deviations before they evolve to mishaps

The impact of traffic flow distribution over arms at junctions on crash risk

In locations where primary and secondary roads cross, the distribution of traffic flow over the arms of a junction can introduce a potential road safety risk. Although the road traffic flow is a frequently considered variable, it is not easy to make a definitive conclusion about the specific effect that the distribution of traffic flow over the arms of a junction has on safety outcomes. This is due to the different variables that published studies use to express the specific risk factor. The aim of the present research is to (i) provide a literature overview of the phenomenon and to (ii) attempt to overcome said uncertainty by conducting a meta-analysis on the effects of traffic flow distribution over arms at junctions. Findings show that where there is an increase in: (i) the traffic volume on the minor or major road, or (ii) the number of turn lanes, crash frequency tends to increase. Where there is a significant flow imbalance between the junction branches (i.e. major and minor roads), mixed results were found in the literature, with crash rates both increasing and decreasing. Meta-analysis findings show that the amount of traffic flow of the secondary road can result in an increase in the number of crashes at a 95% confidence level. This conclusion can be exploited to inform principles of junction design that can consequently improve road safety.</p

Detailed list of sub-use cases, applicable forecasting methodologies and necessary output variables

Work package 4 (WP4) within LEVITATE is concerned with gathering city visions and developing feasible paths of automated vehicles related interventions to achieve policy goals. City visions contributed to the project in assessing the impact indicators that are needed to be addressed for a useful policy support tool (PST). Previous deliverables of WP4 (deliverable 4.2 and 4.3) used backcasting methods to develop feasible pathways to reach these goals by using policy interventions related to connected and automated transport systems (CATS). These were carried out for the city of Vienna, Amsterdam and Greater Manchester.This deliverable summarises the work that has been conducted in the frame of WP4 and sets the scene for the core LEVITATE work packages (WPs 5, 6 and 7), which address the three main use cases of the project: Urban transport, Passenger cars and Freight transport. Further, the goal of this deliverable is to summarise a timewise implementation of different sub-use cases, and the forecasting methodologies that need to be employed to assess the direct, wider and systemic impacts of CATS. Discussion on the specific ways to study the impacts of the interventions using micro-simulation technique is conducted and the necessary outcome variables of the forecasting models are specified.The main contribution of deliverable 4.4 is a consolidated list of sub-use cases and output variables, and an indicative timewise implementation of the interventions. The list of sub-use cases and interventions was evaluated against the available methods by performing a decision-making exercise among the project partners. From this evaluation, downselection was carried out during a plenary project meeting at the Hague in October 2019, to select the most appropriate and feasible sub-use cases and interventions. Later, these items were arranged on a timeline from present (2020) to 2040 to indicate possible arrival of the services, technologies or interventions due to the anticipated arrival of CATS. This gives an insight into what changes are to be expected in a future city.A small extract from Deliverable 3.2 (methods that could be applied to measure societal level impacts from CATS) is included in the current deliverable to provide a short summary of the methods available for forecasting societal level impacts. Since the systemic and wider impacts are somewhat dependent on the direct impact, traffic micro-simulation method is the first choice to initially get direct impact. Therefore, this method is described in more detail. Further research is being undertaken in WPs 5, 6 and 7 to assess the impacts from specified sub-use cases in the most efficient way. To determine these impacts quantitatively, a list of impact indicators is presented as output variables for the various methods that will be employed.</p

Impacts of on-street parking regulations on cooperative, connected, and automated mobility - a traffic microsimulation study

This study aims to investigate the mobility impacts of on-street parking regulations for Connected and Automated Vehicles (CAVs) under mixed traffic fleets. A calibrated and validated network model of the city of Leicester in the UK was selected to test the implementation under various deployment scenarios. The modelling results indicated that replacing on-street parking with driving lanes, cycle lanes and public spaces can potentially lead to better traffic performance (27% to 30% reduction in travel time, 43% to 47% reduction in delays) compared to the other tested measures. The less significant impact of replacement with pick-up/drop-off points is due to increased stop-and-go events while vehicles pick-up and drop-off passengers, consequently leading to more interruptions in the flow and increased delays. The paper provides examples of interventions that can be implemented for on-street parking during the implementation of CAVs for regional decision-makers and local authorities.</p

Analysing the impacts of parking price policies with the introduction of connected and automated vehicles

It is known that parking prices can affect multiple characteristics such as traffic flow, delays, and congestion. Connected and autonomous vehicles (CAVs) do not need drivers and may return to the origin, if necessary, avoiding parking fees. However, if the destination area is not near the origin, it may not be economically viable to return. Hence, in the present study, four scenarios were tested to find the optimal parking strategy: (i) enter and park inside area (ii) enter, drop off and return to the origin (iii) enter, drop off and return to outside parking and (iv) enter and drive around. Different parking prices were used to determine the suitable option. The ‘Balanced’ scenario with multiple parking choices was found to be better compared to other scenarios, where the flow and travel distance were moderately (-19 and -26.3%) affected. Emissions were reduced significantly with CAVs.</p

Examining parking choices of connected and autonomous vehicles

Raising parking charges is a measure that restricts the use of private vehicles. With the introduction of connected and autonomous vehicles (CAVs), the demand for parking has the potential to reduce as CAVs may not park at ‘pay to park’ areas as they are able to “cruise” or return home. However, it might not be financially feasible for them to return to their origin if the destination region is far away. Therefore, the question is: how could we develop parking policies in the CAVs era? To determine the best parking strategy for CAVs, four scenarios were tested in this paper: (i) enter and park within the destination area, (ii) enter, drop off, and return to the origin, (iii) enter, drop off, and return to outside parking and (iv) enter and drive around. Since real-world parking demand data for CAVs are not available, a simulation model of the road network in Santander (Spain) was employed to collect data on both CAV operations (e.g., conservative versus aggressive behaviors) and parking choices. Multinomial logistic regression model was used to identify the best parking option for CAVs. Performance indicators such as traffic, emissions, and safety were employed to compare the performance of a range of parking alternatives. It was found that the balanced scenario (i.e., combination of all parking choices) performs better with the greatest change in delay (around 32%). With 100% CAV market penetration, traffic crashes were reduced by 67%. This study will help local authorities formulate parking policies so that CAVs can park efficiently