Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems

Simulating Train Dispatching Logic with High-Level Petri Nets

Railway simulation is commonly used as a tool for planning and analysis of railway traffic in operational, tactical and strategical level. During the simulation, a typical problem is a deadlock, i.e. a specific composition of trains on a simulated section positioned in such a way that they are blocking each other\u27s paths. Deadlock avoidance is very important in the simulation of railways because deadlock can stop the simulation, and significantly affect the simulation results. Simulation of train movements on a single track line requires implantation of additional rules and principles of train spacing and movement as train paths are more often in conflict than on a double track line. A High-level Petri Nets simulation model that detects and manages train path conflicts on a single track railway line is presented. Module for train management is connected to other modules on a hierarchical High-level Petri net. The model was tested on a busy single track mainline between Hrpelje-Kozina and Koper in south-western Slovenia

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Adaptive Railway Traffic Control using Approximate Dynamic Programming

Railway networks around the world have become challenging to operate in recent decades, with a mixture of track layouts running several different classes of trains with varying operational speeds. This complexity has come about as a result of the sustained increase in passenger numbers where in many countries railways are now more popular than ever before as means of commuting to cities. To address operational challenges, governments and railway undertakings are encouraging development of intelligent and digital transport systems to regulate and optimise train operations in real-time to increase capacity and customer satisfaction by improved usage of existing railway infrastructure. Accordingly, this thesis presents an adaptive railway traffic control system for realtime operations based on a data-based approximate dynamic programming (ADP) approach with integrated reinforcement learning (RL). By assessing requirements and opportunities, the controller aims to reduce delays resulting from trains that entered a control area behind schedule by re-scheduling control plans in real-time at critical locations in a timely manner. The present data-based approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using RL techniques. By using this approximation, ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this thesis, formulations of the approximation function and variants of the RL learning techniques used to estimate it are explored. Evaluation of this controller shows considerable improvements in delays by comparison with current industry practices

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Deep learning architecture for UAV traffic-density prediction

The research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average

The effect of infrastructure changes on railway operations.

This paper makes use of standard simulation programs in combination with the tools of applied statistics to simulate railway operations. The purpose of the use of this tool is to evaluate and compare different possible kinds of railway infrastructure, like different types of signaling procedures, different network configuration or operational procedures. A railway system is a logistic network and because of the demand for improved railway operation, much work has been undertaken lately in this scientific field. However the author postulates the hypothesis based on a literature review that in a lot of these works there is a lack of full application of statistics. With this paper the author makes use of standard simulation programs for detailed simulation of railway operation especially with respect to the signaling and operation procedures. Additionally the influence of delays, which occur during real life railway operation is taken into account for a first time. This allows statistical evaluation of the results based on statistical significance. Also sensitivity analysis could be performed. It is demonstrated, that the results of such simulation runs show superior results when compared to other techniques not taking into account the variability. Additionally, procedures were developed to find the capacity of a railway network with the help of additional software tools. In this work the software package ARENA is used to simulate the operation of trains in railway networks. For this approach two major obstacles have to be solved: the simulation of train travelling times and the simulation of block rules used in railway operation. By introduction of visualization the confidence in the results of simulation, even for stakeholders not familiar with this technique, is increased. In this paper it is shown that with ARENA it is possible to calculate the capacity of different railway networks (scenarios). The results, which are calculated using quasi steady state simulation without variation, are similar to those obtained with other calculation methods. Additionally in one scenario the rule of thumb for the quotient between theoretical capacity and practical capacity in a railway network is confirmed by simulation including random variation. It is also demonstrated that OptQuest, an additional software package available for ARENA, is a suitable tool to find near optimal timetables in a scenario including delays. The results of this work may be not only of interest for railway operators, but also for operators of other automated transport systems. Such systems may be unmanned transport vehicles in a factory, transporting goods between different manufacturing stations. But also for automation of road traffic the results may be of interest

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is propose