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The application of traffic conflicts in road safety assessment is increasingly favored, owing to its critical importance in conducting real‐time safety analyses and formulating proactive safety management strategies. The study aims to construct an accurate model to predict the frequency and occurrence of traffic conflicts on highways within a short time frame. Utilizing the highD dataset, we construct traffic characteristic indicators as covariates, including traffic volume (TV), speed variation coefficient (SVC), the proportion of large vehicles (PLV), lane‐to‐lane average speed difference (LASD), and front‐to‐rear vehicle average speed difference (FASD). Traffic conflict frequency, measured by the time‐to‐collision threshold of fewer than 4 s, serves as the dependent variable. The study employs zero‐inflated Poisson, zero‐inflated negative binomial, hurdle Poisson, and hurdle negative binomial models to fit conflict frequency, accounting for interaction effects, random effects, and random parameters. The findings indicate that models incorporating interaction effects yield a better fit than those not considering interaction effects. Furthermore, models considering interaction effects, random effects, and random parameters show superior fit compared to models only considering interaction effects, albeit with increased complexity under the hurdle Poisson and hurdle negative binomial distributions. These results offer valuable insights for managing and controlling highway traffic safety.

The increasingly widespread application of drones and the emergence of urban air mobility leads to a challenging question in airspace modernisation: how to create a safe and scalable air traffic management system that can handle the expected density of operations. Increasing the number of vehicles in a given airspace volume and enabling routine operations are essential for these services to be economically viable. However, a higher density of operations increases risks, poses a great challenge for coordination and necessitates the development of a novel solution for traffic management. This paper contributes to the research towards such a strategy and the field of airspace management by calculating and analysing the conflict probability in an en-route, free-flight scenario for autonomous vehicles. Analytical methods are used to determine the directional dependence of conflict probabilities for exponential and normal prescribed speed probability distributions. The notions of geometric and speed conflict are introduced and distinguished throughout the calculations of the paper. The effect of truncating the set of available flight speeds is also investigated. The sensitivity of the calculated results to speed and heading perturbations is studied within the analytical framework and verified by numerical simulations. Results enable a fresh approach to conflict detection and resolution through distribution shaping and are intended to be used in an integrated, stochastic coordination framework.

Efficient urban traffic management is crucial for mitigating congestion and enhancing road safety. This study introduces a novel algorithm, with code provided, to generate a traffic light conflict matrix, identifying potential signal conflicts solely based on road network topology. Unlike existing graphical approaches that are difficult to execute automatically, our method leverages readily available topological data and adjacency matrices, ensuring broad applicability and automation. While our approach deliberately focuses on topology as a stable foundation, it is designed to complement rather than replace dynamic traffic analysis, serving as an essential preprocessing layer for subsequent temporal optimization. Implemented in MATLAB, with specific functionality for Vissim users, the algorithm has been tested on various networks with up to 547 traffic lights, demonstrating high efficiency, even in complex scenarios. This tool enables focused allocation of computational resources for traffic light optimization and is particularly valuable for prioritizing emergency vehicles. Our findings make a significant contribution to traffic management strategies by offering a scalable and efficient tool that bridges critical gaps in current research. As urban areas continue to grow, this algorithm represents a step forward in developing sustainable solutions for modern transportation challenges.

Real‐time safety evaluation of urban signalized intersections is a prerequisite for proactive traffic safety management. Due to its independence from historical data, the traffic conflict technique has gained increasing popularity as a tool for real‐time safety evaluation in transportation systems. However, different types of conflicts (e.g., rear‐end and side‐impact conflicts) may lead to differences in safety evaluation, which has led previous studies to typically analyze crash risk based on various conflict types separately. Therefore, this study develops the hierarchical Bayesian extreme value theory (block maxima (BM)) model based on different conflict types to form a novel real‐time safety evaluation approach. The proposed model is applied to the real‐time safety evaluation of five signalized intersections in Harbin, China. The results show that (1) the proposed BM model exhibits good prediction performance when there is sufficient observation duration and a sufficient number of samples; (2) the proposed model is superior to other baseline models developed based on only one conflict type in terms of prediction accuracy. The empirical findings of this study establish innovative frameworks and theoretical foundations for advancing proactive safety protocols and autonomous mobility systems.

Today’s air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deterioration of flight safety, greater costs, more delays, and higher emissions. Transforming the ATM into the “next generation” requires complex human-integrated systems that provide better abstraction of airspace and create situational awareness, as described in the literature for this problem. This paper makes the following contributions: (a) It outlines the complexity of the problem. (b) It introduces a digital assistance system to detect conflicts in air traffic by systematically analyzing aircraft surveillance data to provide air traffic controllers with better situational awareness. For this purpose, long short-term memory (LSTMs) networks, which are a popular version of recurrent neural networks (RNNs) are used to determine whether their temporal dynamic behavior is capable of reliably monitoring air traffic and classifying error patterns. (c) Large-scale, realistic air traffic models with several thousand flights containing air traffic conflicts are used to create a parameterized airspace abstraction to train several variations of LSTM networks. The applied networks are based on a 20–10–1 architecture while using leaky ReLU and sigmoid activation function. For the learning process, the binary cross-entropy loss function and the adaptive moment estimation (ADAM) optimizer are applied with different learning rates and batch sizes over ten epochs. (d) Numerical results and achievements by using LSTM networks to predict various weather events, cyberattacks, emergency situations and human factors are presented.

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.

The aim of this paper is to provide preliminary results on a traffic coordination framework based on stochastic task allocation. General trends and the predicted advent of personal aerial vehicles increase traffic rapidly, but current air traffic management methods admittedly cannot scale appropriately. A hierarchical system is proposed to overcome the problem, the middle layer of which is elaborated in this paper. This layer aims to enable stochastic control of traffic behaviour using a single parameter, which is achieved by applying distributed stochastic task allocation. The task allocation algorithm is used to allocate speeds to vehicles in a scalable way. By regulating the speed distribution of vehicles the conflict rates remain manageable. Multi-agent simulation results show that it is possible to control ensemble dynamics and together with that traffic safety and throughput via a single parameter. Using transient simulations the dynamic performance of the system is analysed. It is shown that the traffic conflict reduction problem can be transformed into a control design problem. The performance of a simple controller is also evaluated. It was shown that by applying the controller, quicker transients can be achieved for the mean speed of the system.

This paper examines the performance of bus rapid transit (BRT) systems in over-crowded travel corridors. A case study of King Faisal Street in Greater Cairo Region GCR, Egypt, was adopted. A simulation model was developed using PTV VISSIM simulation platform for the study area. A data collection effort was exerted to collect operational traffic data required for model development/calibration. As BRT systems share the roadway with other modes of travel (vehicles, pedestrians, etc.), handling conflicts is a major challenge that faces operations, especially in over-crowded travel corridors. Four BRT scenarios were developed with different conflict treatment methodologies (vehicle/BRT conflicts at U-turns and vehicle/passenger conflicts near BRT stations), varying from signal control at each BRT station (scenario 1) to complete grade separation (scenario 4). Each of the developed scenarios was thoroughly assessed based on disaggregated segment travel times, aggregated corridor travel times, capacity to accommodate travel demand, and corridor level of service. A wide range of results was reported for the impact of the proposed systems on corridor traffic operations varying from 80% increase in overall travel time (scenario 1) to 18% reduction (scenario 4). Such results highlight the potential of BRT systems in improving traffic operations in over-crowded traffic corridors and the vital role of conflict treatments in achieving successful operations.

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

A procedure was developed for using Surrogate Safety Assessment Model (SSAM) and VISSIM for safety assessment at freeway merge areas. The simulated conflicts generated by the VISSIM simulation models and identified by the SSAM approach to those measured in the field using traditional traffic conflicts techniques. Of particular interest was to identify if the consistency between the simulated and the field-measured traffic conflicts could be improved by calibrating the VISSIM simulation models and adjusting the threshold values in SSAM. A two-stage procedure was proposed to calibrate and validate the VISSIM simulation models. The transferability of the calibrated simulation models was also tested. It was found that the two-stage calibration procedure reduced the mean absolute prevent error (MAPE) for total conflicts from 78.1 to 33.4%. More specifically, the MAPE value was reduced from 76.6 to 33.5% for the rear-end conflicts, and from 79.5 to 35.8% for the lane-change conflicts. Linear regression models and the Spearman rank correlation coefficient were also developed to study the relationship between the simulated and the observed conflicts. Data analysis results showed that there was a reasonable consistency between the simulated and the observed conflicts.