Lidé na fakultě
Dr. Ing. Jan Přikryl

2025, Transactions on Transport Sciences, 16, p. 44-48), ISSN 1802-9876

This paper reports on the initial phase of research into automated traffic conflict resolution for suburban railway operations. It defines railway traffic conflicts, categorising types such as catch-up, crossing, and proximity, and establishes optimisation criteria focused on punctuality, efficiency, safety, and passenger satisfaction. Promising machine learning approaches are reviewed, including supervised learning for conflict prediction, reinforcement learning for adaptive resolution, and unsupervised methods for identifying conflict-prone scenarios. The study concludes by proposing a simulation framework for empirical evaluation, providing a foundation for AI-driven advancements in railway traffic management.

Článek v periodiku excerpovaném databází Scopus cizojaz.

2017, 2017 Smart Cities Symposium Prague (SCSP) - IEEE PROCEEDINGS, New York, IEEE Press), ISBN 978-1-5386-3825-5

Automated driving has become an important research trend in the field of cooperative intelligent transportation systems and their applications in smart cities. Automated driving both increases road capacity and eliminates human errors, one of the most common reason of traffic accidents. Both these aspects influence significantly the quality of life, which is a major goal of smart city initiatives. Management of automated vehicles by urban road infrastructure is a rather new subject and not so much practical development has been reported yet. Unfortunately, the available information is spread over fragments within other resources dealing with automated vehicles. In this paper, we provide an overview of major functionalities that have to be provided by both road infrastructure and an automated vehicle so that the vehicle can optimally navigate through an urban network with signalized intersections and we review the state-of-the-art publications providing approaches relevant to these core functionalities.

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2017, Proceedings of Seminar Programs and Algorithms of Numerical Mathematics 18, Praha, Matematický ústav AV ČR, v. v. i.), p. 89-96), ISBN 978-80-85823-67-7

A key element of microscopic traffic flow simulation is the so-called car-following model, describing the way in which a typical driver interacts with other vehicles on the road. This model is typically continuous and traffic micro-simulator updates its vehicle positions by a numerical integration scheme. While increasing the order of the scheme should lead to more accurate results, most micro-simulators employ the simplest Euler rule. In our contribution, inspired by [1], we will provide some additional details that have to be addressed when implementing higher-order numerical integration schemes for CFMs and we will show that the theoretical gain of higher-order methods is unfortunately masked out by the stochastic nature of real-world traffic flow.

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