Search

Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators., Author(s): Amir Hossein Karbasi [1]; Nan Zheng (corresponding author) [2]; Steve O'Hern (corresponding author) [3,4] 1. Introduction Transportation networks face many challenges such as high travel times, traffic congestion, crashes, [...]

Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables. With the arrival of the responders at the accident scene, more information is gained; therefore, the second stage model is activated, which updates the remaining accident duration time. We used eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, and Shapley Additive exPlanations (SHAP) for making predictions and interpreting results, respectively. The results show that our framework predicts traffic levels with overall accuracies of around 80%, and duration models have high forecast accuracy with mean absolute percentage errors ranging between 7.26% and 21.59%. Overall, in the absence of accident information, SHAP values identified that weather factors, the traffic speed difference before and after an accident, traffic volume, and the percentage of heavy vehicles before the accident are the most important variables. However, accident variables, including the occurrence of injury or fatal accidents, rear-end collisions, and the number of involved vehicles, are among the most important variables in the second stage of the framework. The findings have practical implications for real-time traffic management of accident events. Road operators could manage post-accident traffic conditions more effectively, and road users could be alerted to take another route or manage their trip., Author(s): Amirhossein Abdi [1]; Seyedehsan Seyedabrishami (corresponding author) [1]; Steve O'Hern [2] 1. Introduction Road accidents, extreme weather, and large-scale events can significantly impact traffic conditions. Traffic congestion caused by [...]

Cycling delivers public health benefits and reductions in carbon dioxide emissions compared to motor vehicle travel. However, riding a bicycle has a higher injury rate per kilometres travelled. Therefore, the shift from cars to bicycles has the potential to cause undesired impacts in terms of road safety. Among cycling injuries, single-bicycle crashes (SBCs) constitute a significant number of all injuries, but the size of the problem is somewhat unknown. This study focuses on the data mainly from the 2010s based on the scientific publications, and explores the proportion and the characteristics of SBCs internationally. Altogether 22 relevant studies were found. In the different studies, the share of SBCs among injured cyclists varies considerably from 17% to 85%. When considering studies based on larger samples and more representative data, the share of SBCs varies between 52% and 85%. It is suggested that SBCs are underreported in certain datasets depending on the methodology chosen to analyse SBCs. The proportion of SBCs has not changed notably during the early twenty-first century. The main characteristics related to SBC events are loss of control or skidding in slippery conditions. The interplay between SBC-related factors such as the infrastructure, the cyclist and other road users, and the bicycle should be further investigated to better understand the causes of SBCs. [ABSTRACT FROM AUTHOR]

Riding a bicycle is increasingly encouraged as a sustainable transport solution, especially in urban areas. However, safety concerns, both perceived and actual, can significantly lower the willingness to ride among the population. To support cycling planning and policy in the double task of increasing the levels of cycling while mitigating crash risk, there is a need to better understand the behaviours and attitudes of bicycle riders. In this manuscript, we study a cohort of Finnish bicycle riders through four questionnaires, the Cycling Behaviour Questionnaire (CBQ), Cyclist Risk Perception and Regulation Scale (RPRS), Cyclist Anger Scale (CAS), and Cyclist Aggression Expression Inventory (CAX). Our findings show low self-reported errors and violations, and high levels of knowledge regarding traffic rules among Finnish bicycle riders. Most participants report low levels of aggression, which is generally dealt with in constructive ways, while anger was most commonly a result of interactions with motor vehicles and less with other road users such as pedestrians. To further reduce the crash risk in cycling, our results point to the need for further separation between bicycle riders and motorised vehicles, and for the development of risk perception and positive behaviours among riders, particularly those engaging in risky behaviours. [ABSTRACT FROM AUTHOR]