Promet - Traffic & Transportation Journal
Pioneering the future of mobility
Welcome to the world of Promet - Traffic&Transportation, where we delve into shaping the future of traffic and transportation through innovation and research. Our platform is dedicated to uncovering the latest insights, trends, and technological advancements impacting transportation systems worldwide.
Through an interdisciplinary approach, we explore how intelligent technologies, sustainable solutions, and transportation planning collectively shape the path towards safer, more efficient, and sustainable traffic and transportation systems.
Welcome to Promet - Traffic&Transportation, where we explore shaping the future of traffic and transportation through innovation and research. Discover the latest insights and technological advancements influencing transportation systems worldwide, aiming for safer, more efficient, and sustainable solutions.
Open Access
We truly believe in knowledge without boundaries!
Journal's metrics
WoS: IF 0.8
Scopus: Citescore 2023 1.9
SJR: Q3 (Engineering)
Latest Issue
Browse through the selection of our newest research
Vessel trajectory prediction is important in maritime traffic safety and emergency management. Vessel trajectory prediction using vessel automatic identification system (AIS) data has attracted wide attention. Deep learning techniques have been widely applied to vessel trajectory prediction tasks due to their advantages in fine-grained feature learning and time series modelling. However, most deep learning-based methods use a unified approach for modelling AIS data, ignoring the diversity of AIS data and the impact of noise on prediction performance due to environmental factors. To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. The model employs TCN to capture the complex correlation of the time series, utilises CNN to capture the fine-grained covariate features and then captures the dynamics and complexity of the trajectory sequences through ConvLSTM to predict vessel trajectories. Experiments are conducted on real public datasets, and the results show that the TCC model proposed in this paper outperforms the existing baseline algorithms with high accuracy and robustness in vessel trajectory prediction.
2024 (Vol 36), Issue 6
To reveal the speed control behaviour and manoeuvring characteristics of direct vehicles that stop-go through signalised intersections, a large-scale field driving test was carried out in Chongqing to collect vehicle data under natural driving conditions. The characteristics of speed, longitudinal acceleration rate and their two-dimensional correlation were analysed for deceleration and acceleration behaviour at signalised intersections. Further, a sensitivity analysis of the simulation model on measured data was done with the micro-traffic simulation experiment of a signalised intersection. The following were observed: (1) Drivers’ speed-selection behaviours become more concentrated with closer distance from the stop point. The transects ±25 m from the stop point are abrupt change points in the discrete nature of driver speed-selective behaviours. (2) Drivers’ desire to decelerate during the stop-go through signalised intersections is more robust, with the magnitude of pedal manoeuvres for deceleration behaviours being more intense than that for acceleration behaviours. (3) There is a nonlinear correlation between longitudinal acceleration rate and speed. The longitudinal acceleration rate increased with increase in speed and then decreased with the inflection point at 15 km/h. (4) The micro-traffic simulation’s acceleration rate model is sensitive to measured acceleration rate parameters. This study guides the parameter setting of speed, deceleration rate and acceleration rate models for microscopic traffic simulation and for parameter calibration of the car-following model.
2024 (Vol 36), Issue 6
As a critical component of urban transportation, metro systems demand rigorous passenger flow safety management. This study proposes a comprehensive decision-making analysis method for metro station passenger flow safety management by integrating the entropy weight and TOPSIS methods. It aims to develop an evaluation model that accurately assesses and ranks the safety management practices of metro stations. To achieve this, 17 indicators related to station scale, safety management equipment, safety or security measures, investment in safety management and the effects of passenger flow management are selected to form an evaluation indicator system. The entropy weight method is employed to allocate weights to these indicators, reflecting their interrelatedness and importance. Subsequently, the TOPSIS method is used to establish a decision model that calculates the closeness of each station’s management practice to an optimal plan, allowing for the ranking of different stations’ safety management practices. The algorithms are developed and optimised using MATLAB, enabling efficient calculation and analysis. A case study involving real metro stations is conducted to validate the feasibility and effectiveness of the proposed evaluation method. The results demonstrate that this model provides an accurate assessment of metro station passenger safety management and offers decision-makers clear directions for improvement.
2024 (Vol 36), Issue 6
The study comprehensively evaluates the safety of contraflow left-turn lane intersection, characterised by unique traffic operational features distinct from conventional intersections. The evaluation specifically focuses on the process of left-turning vehicles entering the receiving lane within the intersection. The vehicle arrival rate of left-turning vehicles is analysed to identify vertical conflict features in contraflow left-turn lane design. By subdividing lanes within the intersection, the study delves into the lateral displacement of left-turning vehicles to establish lateral conflict features. To quantify the overall conflict potential, a multiple unit conflicts index is derived by integrating both vertical and lateral conflict features. Furthermore, the double index left-turn conflict model is constructed by introducing the potential collisions severity index during the conflict process. The results indicate that conflict hotspots along the vehicle travel path are primarily concentrated in two regions: (1) at pedestrian crosswalks and within a 2-meter extension; (2) within a range of 6 to 18 meters from the pedestrian crosswalk. The proposed model demonstrates good evaluation effectiveness, providing valuable insights into enhancing the safety of contraflow left-turn lane intersections.
2024 (Vol 36), Issue 6
The aim of this paper is to highlight the vulnerability of Maritime Autonomous Surface Ships (MASS) to cyber-attack and to illustrate, through a simulation experiment on a testbed, how to mitigate a cyber-attack on the MASS thruster controllers during low-speed motion. The first part of the paper is based on a scoping review of relevant articles in the field, including some MASS projects, related cyber threats and modelling techniques to improve cyber resilience. In the second part of the paper, a cyber-attack on the MASS thruster controllers at low speed motion is illustrated along with the impact of the attack on the trajectory motion. The Kalman filter, as an additional device to the thruster controllers, is used as a cyber-attack mitigation aid. Under the conditions of a simulated intrusion on the input and output signals of the thruster, the experiments conducted in the MATLAB Simulink environment provide an insight into the behaviour of the MASS propulsion subsystem from the perspective of the low-speed trajectory, with and without the Kalman filter.
2024 (Vol 36), Issue 6
This paper focuses on the online energy-saving operation control problem for passenger and freight trains running in a single-track railway line. Firstly, we design a centralised optimisation method to generate energy-saving reference profiles for both passenger and freight trains, in order to improve the punctuality of passenger trains and to reduce the total running time of freight trains in a central way. Secondly, we propose the distributed model predictive control (DMPC) based online trajectory optimisation problems for both types of trains, subject to their respective operational constraints including safety, punctuality, static speed limits and temporary speed restrictions. Then we formulate an online train operation control algorithm based on the centralised optimisation method for the initialisation of train trajectories and the DMPC method for the online trajectory planning. Finally, the proposed algorithm is applied to case studies of passenger and freight trains in a single track railway, and the numerical simulation results show that the proposed algorithm can realise online control for energy-saving train operation in the presence of input disturbances and temporary speed restrictions.
2024 (Vol 36), Issue 6
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Innovation and New Technologies in Transport and Logistics
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD
Transport and logistics, essential components of today's interconnected and globalized world, serve as the backbone of economies worldwide. They facilitate the seamless movement of goods and people, driving trade, commerce, and societal development. However, amidst their significance, contemporary transport and logistics sectors face multifaceted challenges that demand innovative solutions.
Ensuring accessibility of transportation services in both urban and rural areas remains a pressing concern. Additionally, environmental sustainability and the imperative for eco-friendly transportation and logistics solutions are paramount. Crafting responsive transport services that adapt to evolving demands and integrating diverse transport modes within the same infrastructure poses significant challenges. The precision and reliability of transportation providers are also critical factors in meeting modern logistics demands.
Stay Focused
Read about the latest news in the T&T landscape
10th International Ergonomics Conference - ERGONOMICS 2024
It is with great pleasure that we invite you to participate in the 10th International Ergonomics Conference - ERGONOMICS 2024, which will be held from December 5th to 6th, 2024 in Zagreb, Hotel International.
Read moreWorkshop - improving the publication process and developing the support system for the journal
Editorial Board meeting of the Promet – Traffic&Transportation journal took place on May 16th, 2023 as a workshop aimed at improving the publication process and developing the support system for the journal under the leadership of the Editor-in-Chief, Assoc. Prof. Ivona Bajor, PhD.
Read moreCooperation between Faculty of Transportation Engineering and Vehicle Engineering, Budapest and journal Promet – Traffic & Transportation
On March 9, 2023, Editor-in-Chief Ivona Bajor and Assistant Editor-in-Chief, Luka Novačko met with long-term partners of the scientific journal Promet – Traffic & Transportation, representatives of the Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Vice-Dean for science and international cooperation Dr. Adam Torok and Dr. Tibor Šipoš.
Read moreFaculty of Logistics signed a co-publishing agreement
On December 21, 2022, the Faculty of Transport and Traffic Sciences, as the publisher of the scientific journal Promet-Traffic&Transportation and the University of Maribor, Faculty of Logistics signed a co-publishing agreement
Read moreSuradnja vezana uz izdavanje međunarodnog časopisa Promet – Traffic&Transportation
Prošlog tjedna održao se sastanak u Celju kojem su prisustvovali glavna urednica časopisa Promet – Traffic and Transportation, doc. dr. sc. Ivona Bajor i zamjenik glavne urednice izv. prof. dr. sc. Luka Novačko sa predstavnicima Univerze v Mariboru, Fakulteta za logistiko, dekanicom Majom Fošner i prodekanom za financije Andrejem Lisecom.
Read moreEditor's Choice Papers
Explore the selection of scientific papers handpicked by the editor
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.
2023 (Vol 35), Issue 4
Meixian Jiang, Guoxing Wu, Jianpeng Zheng, Guanghua Wu
This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning.
2021 (Vol 33), Issue 2
The purpose of this research is to investigate the effect of land use, built environment and public transportation facilities’ locations on destinations of bike-sharing trips in an urban setting. Several methods have been applied to determine the relationship between predicting variables and trip destinations, such as ordinary least squares regression, spatial regression and geographically weighted regression. Additionally, a comparison between the proposed models, count models and random forest has been conducted. The data were collected in Budapest, Hungary. It has been found that touristic points of interest, and healthcare and educational points have a positive impact on bike-sharing destinations. Public transportation stops for buses, trains and trams attract bike-sharing users, which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial and educational zones. Other variables, such as road length and water areas, form as constraints to bike-sharing trip destinations. Geographically weighted and spatial regression performs better than count models and random forest. This study helps decision-makers in predicting the origin-destination matrix of bike-sharing trips based on the transportation network and land use.
2023 (Vol 35), Issue 1
Pavle Bugarčić, Nenad Jevtić, Marija Malnar
Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to overcome this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward function in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.
2022 (Vol 34), Issue 6
Junzhuo Li, Wenyong Li, Guan Lian
Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper proposes a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive capabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Winters model.
2022 (Vol 34), Issue 6
Laura Eboli, Maria Grazia Bellizzi, Gabriella Mazzulla
Evaluating air transport service quality is fundamen-tal to ensure acceptable quality standards for users and improve the services offered to passengers and tourists. In the transportation literature there is a wide range of studies about the evaluation of public transport service quality based on passengers’ perceptions; however, more recently, the evaluation of air transport service quality is becoming a relevant issue. Evaluating service quality in air transport sector represents a more stimulating chal-lenge, given the complexity of air transport system in re-gards to the other systems; in fact, air transport service is characterised by a great variety of service aspects relat-ing to services offered by the airlines and provided by the companies managing airports. The complexity of such a service requires a deep investigation on the methods adopted for collecting and analysing the data regarding passengers’ perceptions. We propose this paper just for treating these interesting aspects and to provide an ex-haustive literature review of the studies analysing ser-vice quality from the passengers’ point of view, where the opinions of the passengers are collected by the Customer Satisfaction Surveys (CSS). We decided to select papers published within the last decade (2010–2020) in journals indexed in important databases such as Scopus and WoS.
2022 (Vol 34), Issue 2