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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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!

The Journal is Indexed

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

Milivoje ILIĆ, Đorđe MAKSIMOVIĆ, Norbert PAVLOVIĆ, Ivan BELOŠEVIĆ

The concept of risk analysis is especially important because it examines and analyses in a detailed manner the factors that affect the normal functioning of a system. In this paper, the level crossing is considered as one system, composed of several elements. The failures of those elements were analysed with the aim of showing which are the most frequent and most critical failures. A multi-methodological approach was used in the analysis. The failure modes and effects analysis (FMEA) method was used to determine risk factors, after that a multi-criteria model was created in a fuzzy environment, and as output, it gave a ranking list of critical failures in the system. Through the discussion of the results, a comparison of the basic model with two other similar ones was made, and the comparative results were analysed. The main aim of this paper is to present one of the possible ways to analyse the risk of the system of level crossings with the aim of improving traffic safety at the crossing.

2024 (Vol 36), Issue 5

Luka DEDIĆ, Miroslav VUJIĆ

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

2024 (Vol 36), Issue 5

Xuelong ZHENG, Xuemei CHEN, Yaohan JIA

Vehicle trajectory prediction plays a critical role before the decision planning of autonomous vehicles in complex and dynamic traffic environments. It helps autonomous vehicles better understand the traffic environments and ensure safe and efficient tasks. In this study, a hierarchical trajectory prediction method is proposed. The graph attention network (GAT) model was selected to estimate the interactions of surrounding vehicles. Considering the behaviour of surrounding agents, the future trajectory of the target vehicle is predicted based on the long short-term memory network (LSTM). The model has been validated in real traffic environments. By comparing the accuracy and real-time performance of target vehicle trajectory prediction, the proposed model is superior to the traditional single trajectory prediction model. The results of this study will provide new modelling ideas and a theoretical basis for the vehicle trajectory prediction in urban traffic environments.

2024 (Vol 36), Issue 5

Mesut ULU, Yusuf Sait TÜRKAN

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

2024 (Vol 36), Issue 5

Baojie WANG, Wei YANG, Ziyao LIU, Guohua LIANG

In China and other developing countries, some bicycle riders exhibit retrograde behaviour, which affects the riding safety of normal cyclists. The effect of retrograde behaviour on visual search and cycling behaviours of normal cyclists is investigated and quantified in this study. First, cyclists are instructed to wear an SMI iView ETG head-mounted mobile eye tracker and a mobile phone equipped with a Global Positioning System real-time location monitoring function to cycle on a road to obtain the times of fixation, saccade and blink, as well as the pupil diameter, gaze position and velocity in normal and retrograde conditions. Subsequently, the effect of retrograde behaviour on the attention of normal cyclists is analysed using three indexes: proportion of fixation time, coefficient of variation of pupil diameter and area of interest. Then, the effect of cycling behaviour is analysed using three indexes: the cycling trajectory, the velocity at three stages and the coefficient of variation of velocity. Finally, polynomial regression analysis is performed to analyse the visual and cycling behaviour impact indexes under the retrograde condition. The results show that retrograde behaviour significantly affects the vision and cycling behaviour of normal cyclists and that the two indexes are positively correlated.

2024 (Vol 36), Issue 5

Yubin ZHENG, Cheng CHENG, Yong ZHANG, Lingyi WANG, Qixuan LI, Hailin ZHANG

Vehicle turn-in rate is a critical and widely adopted input for expressway rest area design and operation. With the implementation of expressway ETC gantries, the ERA turn-in rate can be further estimated by measuring the travel speed distribution via ETC gantry data. This paper proposed an adaptive density peak clustering Gaussian mixture model (ADPC-GMM) for ERA turn-in rate estimation. The ADPC algorithm is applied to generate the GMM’s inputs accommodating to the traffic characteristic of ERA expressway segments and GMM would further provide the turn-in rate estimation results. To validate the model precision, the turn-in rate data of four selected ERAs in Sichuan, China, as well as the ETC gantry data of their corresponding expressway sections are obtained. According to the estimation results, the MAE and RMSE are 0.0228 and 0.0267 for the passenger car scenario and 0.0264 and 0.0356 for the commercial truck scenario, respectively. These results are also at the lowest level compared with the results acquired from ordinary GMM, K-Means and DBSCAN algorithms. The proposed method has good applicability for vehicle turn-in rate estimation and can be deployed at different ERAs, especially those ERAs without traffic monitoring.

2024 (Vol 36), Issue 5

Special Issue Call

We invite you to contribute to our special issue

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.

Read more...

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Editor's Choice Papers

Explore the selection of scientific papers handpicked by the editor

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Ahmed Jaber, Bálint Csonka

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

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

Marko Orošnjak, Mitar Jocanović, Branka Gvozdenac-Urošević, Dragoljub Šević, Ljubica Duđak, Velibor Karanović

The research on Bus Fleet Management (BFM) has undergone significant changes. It is unclear whether these changes are accepted as technological change or as a paradigm shift. Perhaps unintentionally, BFM is still perceived as routing and scheduling by some, and by others as maintenance and replacement strategy. Therefore, the authors conducted a Systematic Literature Review (SLR) to overview the existing concepts and school of thoughts about how stakeholders perceive the BFM. The SLR post-study exposed that BFM should be acknowledged as a multi-realm system rather than a uniform dimension of fulfilling timely service. Nonetheless, the work encapsulates BFM evolution which shows the need for the multi-realm research abstracted as "Bus Fleet Mobility Management" and "Bus Fleet Asset Management". The difficulties of transport agencies and their ability to switch from conventional to Zero-Emission Buses (ZEBs) illustrates why we propose such an agenda, by which the research is validated through needs both in academia and in practice.

2020 (Vol 32), Issue 6

Emma Strömblad, Lena Winslott Hiselius, Lena Smidfelt Rosqvist, Helena Svensson

In search for measures to reduce greenhouse gas emissions from transport, insights into the characteristics of all sorts of trips and specifically trips by car are needed. This paper focuses on everyday leisure trips for social and recreational purposes. Travel behaviour for these purposes is analysed considering individual and household factors as well as properties of the trip, based on Swedish national travel survey data. The analysis reveals that everyday leisure trips are often of joint character and that the average distance travelled per person and day increases with, for example, income, cohabitation, children in the household and residence in rural areas. The result also shows that the studied characteristics vary between studied trip purposes, influencing the sustainability potential of a reduction in car use and suggested measures. For instance, the largest share of passenger mileage comes from social trips, whereas trips for exercise and outdoor life have the largest share of car trips below 5 km. Several characteristics indicate difficulties in transferring trips by car to, for example, bicycle or public transport due to convenience, economy, start times, company etc. The study indicates that there is a need to take a broader view of the effective potential.

2022 (Vol 34), Issue 4

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

Ying Chen, Zhigang Du, Zehao Jiang, Congjian Liu, Xuefeng Chen

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


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