Timetable stability depends on the regularity of trains. Any deviation from the planned timetable leads to its instability. Railway network characteristics determine the capacities of the transport service. Depending on the capacity calculation method, time components are added to the minimum headway to ensure timetable stability. The UIC 405 method is simple and can be used on all railways. The disadvantage is that the calculations are based on average data. According to the method, the minimum headway consists of the time of the average headway interval, additional time and the buffer time. The additional time is precisely defined by the number of APB sections, while the buffer time is in the average value. When creating the timetable, the goal is optimal utilisation of the infrastructure. If the headway is too long, the capacity is not used, and if it is too short, timetable instability will ensue. Instead of averaging, this work calculates a buffer time that depends on the ratio of the travel time of the previous and the following trains. In this way, the headway is optimised and the calculation of the UIC 405 method is improved.

Physiological signal index can accurately reflect the degree of fatigue, but the contact detection method will greatly affect the driver's driving. This paper presents a non-contact method for detecting tired driving. It uses cameras and other devices to collect information about the driver's face. By recording facial changes over a period and processing the captured video, pulse waves are extracted. Then the frequency domain index and nonlinear index of heart rate variability were extracted by pulse wave characteristics. Finally, the experiment proves that the method can clearly judge whether the driver is tired. In this study, the Imaging Photoplethysmography (IPPG) technology was used to realise non-contact driver fatigue detection. Compared with the non-contact detection method through identifying drivers' blinking and yawning, the physiological signal adopted in this paper is more convincing. Compared with other methods that detect physiological signals to judge driver fatigue, the method in this paper has the advantages of being non-contact, fast, convenient and available for the cockpit environment.

Urban transport system planning has been moving towards sustainability in recent years, and the concept of urban freight logistics is an integral part of this planning. Urban freight logistics involves many stakeholders that participate in its operation and should be considered in the system planning process. The paper presents one of the approaches to the methodology for the selection of key indicators suitable for the evaluation and monitoring of a sustainable system of urban freight logistics in such a way that it reflects as much as possible the needs of all involved stakeholders. This is done by applying the selected method of multi-criteria analysis with the involvement of various urban freight logistics stakeholders. Based on the proposed methodology, the paper defines significant indicators that can be considered for further evaluation of the level of a sustainable urban freight logistics system. In addition to the possibilities of further development of this methodology, the application of determined significant indicators for calculating the proposed index of sustainable urban freight logistics is discussed. The proposed procedure can be implemented in the preparatory steps in the framework of the creation of sustainable urban logistics plans (SULPs).

For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver's driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities.

It is generally known that traffic safety is influenced by humans, vehicles, and roads. Nowadays, when new technologies have taken over a large part of the traffic industry, the selection of relevant software, whose competition is great, presents a big problem for the decision-maker. Intelligent systems, such as Motion, SCOOT, and SCATS, are used for the implementation of a control strategy in order to manage signals on the traffic network, with the goal of increasing efficiency and traffic safety. These systems operate on the demand-responsive principle and have logic for traffic optimization which represents their main difference along with the optimization subject and method of the system functioning. The research included consideration of mentioned differences, software, and hardware architecture that have a significant impact on the system’s functionality since the detector’s locations themselves depend on the optimization subject. The implementation benefits are considered through the existing world’s projects. Based on the obtained data, the criteria used for the comparative analysis of these three systems were defined, from the aspect of traffic safety.

Although computer vision-based methods have seen broad utilisation in evaluating traffic situations, there is a lack of research on the assessment and prediction of near misses in traffic. In addition, most object detection algorithms are not very good at detecting small targets. This study proposes a combination of object detection and tracking algorithms, Inverse Perspective Mapping (IPM), and trajectory prediction mechanisms to assess near-miss events. First, an instance segmentation head was proposed to improve the accuracy of the object frame box detection phase. Secondly, IPM was applied to all detection results. The relationship between them is then explored based on their distance to determine whether there is a near-miss event. In this process, the moving speed of the target was considered as a parameter. Finally, the Kalman filter is used to predict the object's trajectory to determine whether there will be a near-miss in the next few seconds. Experiments on Closed-Circuit Television (CCTV) datasets showed results of 0.94 mAP compared to other state-of-the-art methods. In addition to improved detection accuracy, the advantages of instance segmentation fused object detection for small target detection are validated. Therefore, the results will be used to analyse near misses more accurately.

In recent years several projects have been realised in the field of transportation, but there is a lack of systematic analysis of research challenges connected to these projects. Thus, the main aim of this paper is to provide an overview of these challenges through EU funded projects in the field of smart, green and integrated transport. Based on EU strategic documents, reports and roadmaps, 10 topics are identified playing a crucial role in transportation-related research. A systematic analysis of the projects is realised, where the projects collected from an online database in the Horizon 2020 framework programme from 2015 to 2020 are categorised into these topics. The results show that travel behaviour, big data and open data, sustainable mobility planning and smart solutions are covered by several projects which reflect the main research trends. While active and shared modes, multimodal transportation, trip optimisation and Mobility as a Service are also popular topics. Based on the results, the most underrepresented research areas are artificial intelligence and social networks. The analysis of the connections between the research topics could enable the achievement of a long-term paradigm shift in urban mobility, which is beneficial for researchers, professionals and policy makers.

Aiming at the lack of an anti-clogging ability index in the road network traffic evaluation index, an anti-clogging ability index was proposed to measure the anti-clogging ability of urban road traffic network: Κ-anti-clogging coefficient, which is used to measure the shortest path between any pair of starting and ending points on the urban road traffic network. After the current edge of the shortest path is blocked, the shortest path is selected from the current node of the shortest path. If the current edge of the shortest path is blocked again, the selection continues until the shortest path to the ending point is selected. In the case of unrecoverable congestion, the properties of the anti-clogging coefficient vector on any origin-destination pair, a path, and the whole traffic network are analysed, and the algorithm of the anti-clogging coefficient and its complexity are given. Finally, an example analysis is carried out using a local traffic network in a city.

Fatigue detection based on vision is widely employed in vehicles due to its real-time and reliable detection results. With the coronavirus disease (COVID-19) outbreak, many proposed detection systems based on facial characteristics would be unreliable due to the face covering with the mask. In this paper, we propose a robust visual-based fatigue detection system for monitoring drivers, which is robust regarding the coverings of masks, changing illumination and head movement of drivers. Our system has three main modules: face key point alignment, fatigue feature extraction and fatigue measurement based on fused features. The innovative core techniques are described as follows: (1) a robust key point alignment algorithm by fusing global face information and regional eye information, (2) dynamic threshold methods to extract fatigue characteristics and (3) a stable fatigue measurement based on fusing percentage of eyelid closure (PERCLOS) and proportion of long closure duration blink (PLCDB). The excellent performance of our proposed algorithm and methods are verified in experiments. The experimental results show that our key point alignment algorithm is robust to different scenes, and the performance of our proposed fatigue measurement is more reliable due to the fusion of PERCLOS and PLCDB.

Cities located on a path of intercity railroads tend to be in a corridor system to increase commerce and economic profits. Theoretic and practical work concerning train station locations can be used to provide factors that affect the choice of train station sites. In this paper, we find optimal places to locate train stations according to location theories and particular natural and socioeconomic characteristics. The methods we used are focused on maximising users’ economic profits associated with passenger and cargo transportation by finding optimal stations on the route. Furthermore, we created a general algebraic modelling system (GAMS) with linear planning, which is solvable using a C programming language (CPLEX) interactive optimiser. Case studies on 14 stations along the 820 km long Milak-Chabahar corridor helped us to simulate our model and test its feasibility at three alternative times to prove the outcomes. The stations would increase an average profit of 38.6%, 42.94% and 50.85%, but the growth varied in the stations. This research contributes to freight and passenger transportation engineers in railroad design. They benefit from the model to determine the location of a train station to obtain maximum user profits in that station and its surrounding areas.

For organisations to take preventive measures and eliminate potential accidents, the information gained through voluntary reporting is essential. Employees do not, however, report voluntarily for a number of reasons. In this study, we examine why train drivers, who are vital to maintaining rail safety, fail to report hazardous occurrences, leading to employee silence. The measurement tool, which has already been developed specifically for aviation employees, has been applied to 346 train drivers working on Turkish Railways. The scale used for research purposes has proven to be valid and reliable for organisations involved in railways. As a result, it was determined that the drivers did not participate in voluntary reporting due to relational and prosocial, disengaged, quiescence and acquiescence and fear and defensive factors. The highest score for the reasons for non-reporting was observed in the dimension of quiescence and acquiescence. The results of the correlation analysis between dimensions, which are assumed to be the reasons for non-reporting, point to strong positive relationships between each dimension.