Before-after safety analysis of a shared space implementation

Innovative advanced systems have been proposed for improving the safety and efficiency in the mobility of road users in Europe. Particularly for road transport, new ITS-based Cooperative Services for preventing accidents in critical points and situations are emerging where road users, vehicles and a sensorised integrative infrastructure are the main actors. With this motivation, and taking into account the road infrastructure's capability to incorporate new Cooperative Systems technology, this study aims to provide a first set of guidelines for speeding up the convergence process among a broad set of multi-heterogeneous sensor data fusion, high level situation understanding and assessment, and communications capabilities, all of them necessary for the development of advanced ITS safety-related services previously mentioned. Analysis is focused on vulnerable road users (VRUs) and on how this kind of services will contribute to provide more intelligent and safer urban traffic environments. A typical scenario involving VRU is studied as a preliminary analysis of the impact that these systems have on the safety of a specific group of road users. Some of the underlying principles of the proposed architecture will be validated in a field operational test FP7 European project.

Battling serious injuries and long-term consequences in road traffic remains one important issue in the overall goal of resilient future road transport and its Vision Zero. Challenges of increasing personal transport in urban areas coupled with autonomous vehicles and shuttles, new mobility devices and the overall need to ensure the safety of vulnerable road users as well as all types of vehicle occupants’ require solid medical background knowledge paired with innovative engineering approaches. EU Horizon Europe is currently funding the ProtAct-Us consortium (2024-2027) that aims at protecting all Road User Groups from serious injury and long-term physical, cognitive and mental health consequences related to road crashes through innovatively interlinked research action between medical and engineering methods and approaches. ProtAct-Us works on the following goals and challenges: i) Medical data correlation, standardisation and classification of long-term consequences of road crashes; ii) Robust and reliable medical, epidemiological and engineering tools like agreed methods to collect information on long-term consequences, upgraded Human Body Models and virtual assessment methods for the extremities, head, neck and face allowing for effective countermeasure development for all road users; iii) Reduction of long-term consequences and related socio-economic cost of road crash related injuries for all road users. The ProtAct-Us solutions will influence new standards in respect of extending current injury codification system with relevant long-term aspects, allowing new physical as well as virtual safety assessment procedures and adaption of rules and regulations to be implemented. Finally, scientifically well-founded suggestion for future implementation into policy, regulatory, and standardization guidelines for an inclusive safety improvement approach for in- as well as post-crash will be provided.

An intercept study of footbridge users and non-users in Malaysia

Modeling mixed-traffic motion and interactions is crucial to assess safety, efficiency, and feasibility of future urban areas. The lack of traffic regulations, diverse transport modes, and the dynamic nature of mixed-traffic zones like shared spaces make realistic modeling of such environments challenging. This paper focuses on the generalizability of the motion model, i.e., its ability to generate realistic behavior in different environmental settings, an aspect which is lacking in existing works. Specifically, our first contribution is a novel and systematic process of formulating general motion models for pedestrians and cars, and application of this process is to extend our Game-Theoretic Social Force Model (GSFM) towards a general model for generating a large variety of motion behaviors of pedestrians and cars from different shared spaces. Our second contribution is to consider different motion patterns of pedestrians and cars by calibrating motion-related features of individual road users and clustering them into groups. We analyze three clustering approaches. The calibration and evaluation of our model are performed on three different shared space data sets. The results indicate that our model can realistically simulate a wide range of motion behaviors and interaction scenarios, and that model performance improves by considering heterogeneity in road users’ motion.

This paper aims to clearly establish the origin and evolution of the shared space concept from a New Zealand perspective by reviewing the literature in the disciplines of both urban design and transportation engineering. The review process involves investigating the use and function of a public road space in the context of the changing of public expectations and how this can relate to a number of interconnected street design approaches (e.g. traffic calming and self-explaining roads (SERs)). These approaches have been used to minimise the influence of motor vehicles since the beginning of the automobile era. The shared space concept, when applied in public road environments in activity centres, has increasingly been embraced by urban planners, transportation engineers and regulatory agencies. A shared space diverges from a conventional road where all road users are encouraged to legitimately occupy the same road space with little physical separation. To achieve this in a safe and efficient manner, the design aims to reduce the dominance of the motor vehicle by promoting pedestrian and cycling activity and utilising the road space as a ‘place’ in addition to its ‘transport’ mobility and access purposes. Given the fundamental conceptual differentiation between a traffic-calmed street and a shared space, the paper argues that there are certain design elements, constituting a shared space, and without them, it would be difficult for a public street to function as a genuine shared space for all road users.

Evaluation of Human Behaviour at Pedestrian Crossings

The impact of Automated Vehicles (AVs) on road traffic safety has become the focus of discussions among governmental organizations, academia, stakeholders, and OEMs. Questions about how safe the automated driving features should be and how the road infrastructure should be improved for the arrival of this new technology must be clarified to enable full acceptance by the customers and society and prepare the mobility of future cities. The fundamental architecture of automated vehicles comprises perception, planning, decision, and actuation. The operation of the perception system, which is responsible for understanding the environment in which the vehicle is inserted, relies mainly on the onboard sensors. However, the available ranging and vision sensors, e.g., LiDAR, radar, and camera, have several limitations. Scenarios with occlusion present a real challenge for state-of-the-art perception systems. The occlusion, caused by obstructing the sensors’ detection field, limits the vehicle’s perception ability and inhibits the detection of other road users in the surroundings, especially Vulnerable Road Users (VRUs). Infrastructure composed of Roadside Units (RSUs) equipped with infrastructure-based sensors can overcome the perception limitations of a system based solely on onboard sensors by monitoring the road environment with a larger field of view and reduced sensitivity to occlusion. This paper presents a collaborative approach for smart infrastructures and automated vehicles for vulnerable road users’ collision avoidance. The proposed extended perception system comprises four main modules: traffic monitoring, long-term motion prediction, collision risk assessment, and trajectory planning. In the event of a safety-critical scenario, the infrastructure generates a safe and comfortable evasive maneuver to avoid a possible collision. Hence, the proposed approach provides a complete solution to overcome scenarios with occluded VRUs. It allows AVs to react to a critical situation with a longer time-to-collision than other systems relying only on onboard sensors, increasing the chance of successful avoidance even when implementing smoother maneuvers. This contributes considerably to the safe and comfortable operation of automated vehicles.

Atypical intersection of a bicycle path and a carriageway with marked car priority: Which car drivers give way anyway?

<div class="section abstract"><div class="htmlview paragraph">The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for Vehicle-to-VRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.</div></div>

Ensuring the safety of vulnerable road users (VRUs), including pedestrians, cyclists, electric scooter riders, and motorcyclists, remains a major challenge for advanced driver assistance systems (ADAS) and connected and automated vehicles (CAV) technologies. Real-world VRU tests are expensive and sometimes cannot capture or repeat rare and hazardous events. In this paper, we present a lightweight, configurable simulation framework that follows European New Car Assessment Program (Euro NCAP) VRU testing protocols. A rule-based finite-state machine (FSM) is developed as a motion planner to provide vehicle automation during the VRU interaction. We also integrate ego-vehicle perception and idealized Vehicle-to-Everything (V2X) awareness to demonstrate safety margins in different scenarios. This work provides an extensible platform for rapid and repeatable VRU safety validation, paving the way for broader case-study deployment in diverse, user-defined settings, which will be essential for building a more VRU-friendly and sustainable intelligent transportation system.

In shared spaces, vulnerable road users (i.e., pedestrians) are encouraged to directly interact with other road users (i.e., vehicles). They self-organize to give or take right-of-way without being regulated by explicit traffic rules. The safety based on their behavior patterns in such areas is, hence, critical and needs to be fully investigated. In this paper, we first carry out a collision probability method based on safety distance to quantitatively study behavior patterns in mixed traffic. Then we propose a Long Short-Term Memories recurrent neural networks model that takes 2D trajectory data at discrete time steps and incorporates collision probability that captures user behavior patterns as a density mapping function for trajectory prediction. The model handles collisions explicitly based on the relative positions of the neighboring users in an ego user's vicinity with considering the impact of personal space and vehicle geometry. It also provides a friend flock detection mechanism to allow close interactions between friends. After training by real-world trajectories, the model outputs comparative results to the state-of-the-art methods for predicting three-second trajectories in complicated situations in a shared space. It can be applied for intent detection and on-board alarming system for autonomous driving when interacting with multimodal road users in shared spaces.

Toll-based congestion pricing (CP) policies are increasingly implemented globally for alleviating road traffic congestion. Several interconnected factors affecting or induced by CP implementation include air quality/emissions, travel time, and road user safety. We sought to examine and characterize research output and patterns across several domains (e.g., health, policy acceptability) surrounding toll-based CP policies, in order to identify where research has focused and where gaps exist. We conducted a structured review and identified 2333 relevant publications, using semi-supervised and machine learning strategies combined with manual review. Annual publication counts peaked in 2015 (n = 122). Themes identified from title and abstract terms included policy implementation characteristics, advanced transportation modeling methods and approaches, and public perception and acceptability. Authorship networks indicated a lack of interdisciplinary research. Country analyses identified the US, China, and the UK as the most frequently represented countries, and underrepresentation from low-income countries. Findings indicate that research focused on specific road user types (e.g., pedestrians) and safety impacts, and equity considerations were relatively sparse compared to other topics (e.g., policy economics, public perception). Additional research on these critical topics is necessary to ensure that such policies are designed to promote positive and equitable effects on road user health and safety.

A physics-informed road user safety field theory for traffic safety assessments applying artificial intelligence-based video analytics

About 20–50 million people are involved in road accidents every year caused by human errors, with more than 500,000 of the victims being low-mobility, high-physical-impact-prone users of the road such as pedestrians, cyclists, motorbikes, scooter, and the likes. Such road users are called vulnerable road users (VRUs). Taking VRU awareness message (VAM) for assessment of the potential collision risk, we argue that the state-of-the-art metric, time-to-collision, is insufficient for accurate collision risk analysis and collision avoidance. A new metric, minimum safe distance, is proposed for realistic collision risk analysis based on VAM exchange to enhance VRU safety. Results show feasibility of pre-emptive triggering of collision warning at approaching vehicle thus enhancing VRU safety. Furthermore, VRU cluster formation and management schemes are proposed for high density VRU s to achieve at least one order of magnitude reduction in VAM messaging overhead.

EXPERT ASSESSMENTS IN DECISION MAKING: RISKS AND SAFETY The aim of research, based on the results of which this article was prepared, is the analysis of the main methodological provisions of providing an expert assessment (audit) of engineering and planning decisions, organizational and management measures to ensure road safety (RS) for vulnerable road users, as well as determining ways to improve it in Ukraine taking into account the specific features of the movement of the specified categories of the population and progressive world practices. Since the current level of mortality and injuries due to traffic accidents (TA) in Ukraine significantly exceeds the average European indicators, and the level of effectiveness of scientific and methodological support of activities in the field of road safety is insufficient, the relevance and social significance of the research topic are obvious. The research results are presented in the form of the authors' opinions regarding the following problems (areas of activity) in the RS field, which should be subject to expert assessment: – the actual level of traffic accidents among the main categories of vulnerable road users in Ukraine; – modern domestic and foreign scientific– methodical and engineering– planning approaches to the formation of individual elements of an effective and safe transport infrastructure, as well as progressive traffic management systems (TS); –  the possibilities and expediency of implementing engineering and planning solutions, organizational and management measures to improve RS in Ukraine in accordance with the principles of sustainable mobility, taking into account the interests of certain types of vulnerable traffic participants (pedestrians, low- mobility population groups, children, cyclists).