The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.

The article is focused on sustainable transport development solutions in cities, such as bike-sharing systems. We discuss the main principles of bike-sharing, its generations, types, and benefits to system users and entire urban transport systems. The aim of the article is to present a comparison of bikesharing systems found in Polish and Chinese cities. The authors also consider new market practices, which can be implemented when introducing or improving current bike-sharing systems.

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The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.

Micro-electromechanical-systems (MEMS) based sensors are used for monitoring the state of machines in condition-based maintenance tasks. This approach is applied at tram depots for the purpose of identifying faulty wheels on trams in order to eliminate defective trams at the entry or dispatch gates. The application of MEMS-based sensors for the detection of wheel faults is the focus of this study. A method for processing of the collected sensor data is developed. It is based on assessing the energy of vibrations at different frequency bands. Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) is used for obtaining a description of the sensor data. The task of finding the energy threshold for detecting faulty wheels, frequency band and parameters of MODWPT which most distinctly distinguish the wheels is the goal of the method. The weighted difference (DW) between the extreme values of energy in a frequency band for normal and faulty wheels is proposed as the measure of the ability to distinguish the wheels. The search for the solution is formulated as a discrete optimisation problem of maximising this measure. Both the simulation and experimental results indicate that faulty wheels have greater vibration energy than normal wheels. The properties of this approach are discussed and evaluated.

The article deals with wheel-rail contact analysis at railway turnout using a finite element modelling approach. The focus is understanding the wheel-rail contact problems and finding the means of reducing these problems at railway turnouts. The main aim of the work reported in this article is to analyse fatigue life and simulate the wheel-rail contact problems for a repeated wheel loading cycle by considering the effect of normal and tangential contact force impact under different vehicle loading conditions. The study investigates the impact of tangential contact force generated due to different-angled shapes of the turnout and aims to reveal how it affects the life of contacting surfaces. The obtained results show that the maximum von-Mises equivalent alternating stress, maximal fatigue sensitivity, and maximum hysteresis loop stresses were observed under tangential contact force. These maximum stresses and hysteresis loops are responsible for rolling contact fatigue damage, and excessive deformation of the wheel-rail contact surface. At a constant rotational velocity, the tangential contact force has a significant impact on the fatigue life cycle and wheel-rail material subjected to fatigue damage at lower cycles compared to the normal contact force. The finite element modelling analysis result indicated that the contact damages and structural integrity of the wheel-rail contact surface are highly dependent on contact force type and can be affected by the track geometry parameters.