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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

Prachiti SHINDE, Marin MARINOV

The rapid expansion of the metro network, driven by urbanisation and a heightened focus on environmental sustainability, underscores the need for efficient and sustainable public transportation systems. This study utilises the West Midlands Metro system as a case study to investigate operational efficiency and utilisation challenges that are common across metro networks globally. Employing advanced simulation modelling with SIMUL8, this research evaluates the existing timetables and utilisation rates of the West Midlands Metro to uncover inefficiencies and untapped potential. Various scenarios, including increased service frequencies and disruptions at high-traffic stations, were simulated to provide actionable insights for optimising metro operations. Findings revealed that increasing service frequency from every 10 minutes to every 5 minutes enhanced utilisation levels and boosted the total number of completed services. Meanwhile, disruptions at major stops resulted in a reduction in utilisation in a negligible range. These results demonstrate that improved service frequency significantly bolsters operational efficiency and showcases resilience to disruptions with minimal impact on overall performance. As to future research, the study suggests that implementing adaptive scheduling through AI-driven maintenance and infrastructure improvements can further elevate the efficiency and passenger experience of metro operations.

2025 (Vol 37), Issue 2

Patricija BAJEC, Eva PODOVŠOVNIK

Crowdshipping has garnered increasing interest due to its potential benefits for various stakeholders. However, despite challenges in attracting crowdshippers, limited research explores their preferences, including socio-demographic factors and the practical challenges providers face when testing or implementing crowdshipping. This study aims to identify key factors influencing willingness-to-work (WTW) among potential crowdshippers, both in general and within business-to-business (B2B) and business-to-customer (B2C) contexts. Based on the literature review, this paper identifies 19 barriers influencing WTW and develops 22 corresponding enablers to address these barriers. Using a survey of 432 participants from Slovenia, the overall significance of these factors without differentiating business models was first assessed. Then, chi-squared automatic interaction detection analysis was applied to predict WTW in B2B and B2C contexts, identifying variations across these models. The disclosure of a mobile number emerged as the most influential predictor in both settings. Other notable differences in enablers and barriers were observed depending on the business model. These findings emphasise the need to consider business models in future preference analyses and provide a foundation for targeted recruitment strategies for crowdshippers.

2025 (Vol 37), Issue 2

Zhenbao WANG, Yanfang HE, Xueqiao ZHAO, Yuqi LIANG, Shihao LI

Previous studies have primarily focused on the effect of the built environment on ridership during weekdays and weekends. This paper aims to investigate the spatial heterogeneity of the effect of built environment factors on ridership at metro stations during National Day holidays. Beijing is divided into three zones from inner to outer areas. Taking metro station boarding and alighting ridership during National Dayholidays as the dependent variable, 13 built environment factors were selected as independent variables according to the “7D” dimension of the built environment. The recommended pedestrian catchments (PCA) combinations for the three zones in Beijing are 400 m_500 m_400 m by using the Multi-Scale Geographically Weighted Regression (MGWR) model. We investigated the effect of built environment factors on metro ridership and spatial heterogeneity. The influencing factors that have significant effects on both boarding and alighting ridership are building density, number of commercial facilities, bus lines density, number of entrance and exit, number of office facilities, mixed utilization of land and road density. The MGWR model results are helpful to propose targeted strategies for revitalising the built environment around metro stations.

2025 (Vol 37), Issue 2

Xiaoyu CAI, Zimu LI, Wufeng QIAO, Xiling CHENG, Bo PENG, Dong ZHANG

To accurately prevent and warn of traffic accidents, this article proposes a method for predicting urban road traffic safety risks based on vehicle driving behaviour data and information entropy theory. This method uses data from radar video-integrated sensors to calibrate the thresholds for identifying unsafe driving behaviour, introduces recognition principles and algorithms, and analyses spatiotemporal distribution patterns. By incorporating entropy theory, an evaluation system with traffic safety entropy as the primary indicator and the unsafe driving behaviour rate as the secondary indicator is established. Clustering algorithms determine the classification number and threshold of traffic safety entropy, constructing a tunnel traffic safety risk assessment model, which is validated with road accident data. Using 13 days of data from the left lane of Qingdao Jiaozhou Bay Tunnel, the model divides traffic operation risk into high and low categories based on K-means clustering results of accident and safety entropy data. The study finds that when the safety entropy classification threshold is 0.0507, the classification accuracy is the highest at 92%. These results provide technical support for identifying road traffic safety risk points and preventing accidents.

2025 (Vol 37), Issue 2

Meiling HE, Guangrong MENG, Xiaohui WU, Xun HAN, Jiangyang FAN

With the acceleration of urbanisation and the rapid increase in road traffic volume, the scientific prediction of traffic accidents has become crucial for improving road safety and enhancing traffic efficiency. However, traffic accident prediction is a complex and multifaceted problem that requires the comprehensive consideration of multiple factors, including people, vehicles, roads and the environment. This paper provides a detailed analysis of traffic accident prediction based on multi-source data. By thoroughly considering data sources, data processing and prediction methods, this paper introduces the various aspects of traffic accident prediction from different perspectives. It helps readers understand the characteristics of different data and methods, the process of accident prediction and the key technologies involved. At the end of the paper, the main challenges and future directions in road crash prediction research are summarised. For example, the lack of efficient data sharing between different departments and fields poses significant challenges to the integration of multi-source data. In the future, combining deep learning models with time-sensitive data, such as social media and vehicle network data, could effectively improve the accuracy of real-time accident prediction.

2025 (Vol 37), Issue 2

Yanhong YIN, Zihao YE, Qiuyan HAN, Li'ao HUANG

This study focuses on understanding the effects of shared mobility on travel behaviours and transport energy in the university campus. Using survey data collected from college students in Ningbo, China, a substitution model was developed to identify changes in travel modes with the introduction of shared mobility on college campuses and to quantify its impact on net energy saving. Considering the average time travelled and the life cycle energy unit of the trip, the before-and-after analysis was conducted to determine the travel behaviours and related transport energy of college students in 2016 and 2019. Compared with the data of 2016 when no shared mobility was introduced, 2019 data revealed three changes in travel behaviours. First, although the total number of trips per person decreased slightly, the trip distance increased in 2019. Second, the energy for trips by each student increased by 25% from 19,809 KJ in 2016 to 24,897 KJ in 2019. Third, the overall energy efficiency of the trips decreased. In conclusion, the effect of shared mobility introduced in the university campus on reducing the transport energy of college students has not been satisfactory.

2025 (Vol 37), Issue 2

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Current Special Issue Call

Rethinking the European Railway System

Guest Editors: Armando Carrillo Zanuy, PhD; Juan de Dios Sanz Bobi, PhD

Editor: Borna Abramović, PhD

Deadline: September 10, 2025

The European railway system has played a pivotal role in shaping the continent’s economic integration, cultural exchange, and sustainable mobility solutions. However, this system now faces unprecedented challenges, including climate change imperatives, digital transformation, and the need for revitalised cross-border connectivity.

Addressing funding mechanisms and harmonising regulatory and operational standards are equally vital to achieving seamless cross-border mobility. The current lack of coordination among national rail systems creates significant barriers to forming an interconnected seamless European rail network, underscoring the urgency of developing solutions for improved interoperability, technical standardisation, increased safety, passenger experiences, active participation in supply chain management, unified organisation, and aligned policy frameworks.

This call for papers seeks innovative approaches to rethinking European railways' governance, technology, and infrastructure in the context of 21st-century demands. Original research papers and reviews are welcome.

Read more...

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Editor's Choice Papers

Explore the selection of scientific papers handpicked by the editor

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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

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

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

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


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