Freeway Performance Measurement in a Connected Vehicle Environment Utilizing Traffic Disturbance Metrics

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic measures that can be used in combination with macroscopic measures for better estimation of traffic safety and mobility. This dissertation examines the use of microscopic measures in combination with the usually used macroscopic measures for traffic congestion evaluation, traffic state categorization, traffic flow breakdown prediction, and estimation of traffic safety. The considered macroscopic measures are the mean speed, traffic flow rate, and occupancy. The investigated microscopic measures for the stated purpose are: standard deviations of individual vehicle’s speeds, standard deviation of vehicles’ speed, and disturbance metrics. The utilized disturbance metrics to capture the stop-and-go operations are: the number of oscillations and a measure of disturbance durations in terms of the time exposed time–to–collision (TET), which has been used in other studies as a safety surrogate measure. However, this measure of disturbance duration requires the location and speed of both the leading and following vehicles and therefore cannot be measured accurately with low sample sizes of connected vehicles (CV). Thus, this study derived a model to estimate this measure based on speed parameters. The developed model was tested using real-world trajectory data from two locations that were not used in the development of the model. Moreover, the percentage of vehicles in the platoon and the platoon size distribution were evaluated as additional indicators of congestion. The relationship between the platooning and disturbance metrics and the speed parameters were further explored. It is recognized that the parameters required to identify the platoons, such as the time headway, will not be available based on data from low market penetrations of CV. Thus, a model was developed that utilize other measures for the estimation of the platooning measures at lower CV market penetrations. For the purpose of traffic state recognition and prediction, first, the study used a hybrid of two unsupervised clustering techniques to classify traffic states into “breakdown” and “non-breakdown”. The study found that adding the disturbance metrics in data clustering when identifying the traffic states will result in better traffic state recognition and traffic flow breakdown identification by capturing the disturbances in the traffic stream. The categorized traffic state was then used as a binary response to the macroscopic and microscopic measures, as features, to train supervised machine learning techniques for predicting traffic flow breakdown in the following 5-minute interval in real-time operations. The study found that utilizing disturbance and safety surrogate metrics in the real-time classification of traffic flow state increases the accuracy of prediction. Also, the study showed that the investigated disturbance metrics and associated models and thresholds are significantly related to crash frequencies and thus can be used in the activation of transportation management strategies to reduce the probability of unsafe traffic and ease traffic disturbances that have adverse impact on traffic safety

ComplexWorld Position Paper

The Complex ATM Position Paper is the common research vehicle that defines the high-level, strategic scientific vision for the ComplexWorld Network. The purpose of this document is to provide an orderly and consistent scientific framework for the WP-E complexity theme. The specific objectives of the position paper are to: - analyse the state of the art within the different research areas relevant to the network, identifying the major accomplishments and providing a comprehensive set of references, including the main publications and research projects; - include a complete list of , a list of application topics, and an analysis of which techniques are best suited to each one of those applications; - identify and perform an in-depth analysis of the most promising research avenues and the major research challenges lying at the junction of ATM and complex systems domains, with particular attention to their impact and potential benefits for the ATM community; - identify areas of common interest and synergies with other SESAR activities, with special attention to the research topics covered by other WP-E networks. An additional goal for future versions of this position paper is to develop an indicative roadmap on how these research challenges should be accomplished, providing a guide on how to leverage on different aspects of the complexity research in Air Transport

Technical approaches for measurement of human errors

Human error is a significant contributing factor in a very high proportion of civil transport, general aviation, and rotorcraft accidents. The technical details of a variety of proven approaches for the measurement of human errors in the context of the national airspace system are presented. Unobtrusive measurements suitable for cockpit operations and procedures in part of full mission simulation are emphasized. Procedure, system performance, and human operator centered measurements are discussed as they apply to the manual control, communication, supervisory, and monitoring tasks which are relevant to aviation operations

베이지안 네트워크를 활용한 교통상태의 확률론적 예측

학위논문 (박사)-- 서울대학교 대학원 공과대학 건설환경공학부, 2017. 8. 고승영.Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent flow breakdown. Therefore, it is necessary to perform traffic state predictions that reflects the stochastic process of traffic flow. However, traffic state transition is affected complexly and simultaneously by many factors, which lead to a lack of understanding and accurate prediction. Meanwhile, the Bayesian network is a methodology that not only is suitable for a problem with uncertainty but also can improve the understanding of a problem. Also, it is possible to derive fair probability with incomplete information, which allows the analysis of various situations. In this study, we developed a traffic state prediction model using the Bayesian network to reflect dynamic and stochastic traffic flow characteristics. In order to improve the structure of the Bayesian network, which has been used simply in transportation problems, we proposed a modeling procedure using mixture of Gaussians (MOGs). Also, spatially extended variables were used to consider the spatiotemporal evolution of traffic flow pattern. In particular, traffic state identification was performed by estimating the link speed in order to consider the spatial propagation of congestion. In the performance evaluation, the Bayesian network has better performance than logistic regression and has the same level of performance as artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provided the understanding of traffic state prediction and the guidelines for model improvement. Therefore, the Bayesian network developed in this study can be considered as a traffic state prediction model with good prediction accuracy and provides insights for traffic state prediction.Chapter 1. Introduction 1 1.1 Research background and purpose 1 1.2 Research scope and procedure 4 Chapter 2. Literature Review 8 2.1 Characteristics of traffic state 8 2.2 Traffic state estimation and prediction 14 2.3 Bayesian network 37 2.4 Originality of this research 41 Chapter 3. Data Collection and Preparation 46 3.1 Data collection and validity check 46 3.2 Traffic state identification 47 3.3 Data Description 63 Chapter 4. Bayesian Network Modeling 66 4.1 Modeling procedure 66 4.2 Description of interface mechanism 69 4.3 Module design 74 4.4 Eliciting the structure 81 4.5 Verification 81 4.6 Parameter learning 85 Chapter 5. Model Evaluation 87 5.1 Evaluation results 87 5.2 Comparison with other methodologies 92 5.3 Sensitivity analysis 104 Chapter 6. Conclusions 127 6.1 Summary 127 6.2 Guidelines for traffic state prediction 128 6.3 Limitations of the study 129 6.4 Applications and future research 130 References 135Docto

Applying complexity science to air traffic management

Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised