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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

This paper attempts to determine the role of street lighting in the spatial clustering of night-time crashes involving pedestrians in the Republic of Croatia. Five-year (2018–2022) night-time pedestrian crash data were used in conditions with and without street lighting. First, distance-based statistical methods were used to assess the spatial clustering and deviations from complete spatial randomness (CRS) of the crash patterns. Second, the global Moran’s I analysis was conducted to investigate a degree of spatial autocorrelation of the annual crash counts aggregated in 21 counties of Croatia. Finally, the local indicators of spatial association (LISA) were used to identify the locations of the crash count hotspots. The results of the ANND analysis confirm a significant clustering of crashes for both street lighting conditions. However, different global Moran’s I values for both conditions were obtained with a high and statistically significant positive value for the crash counts without street lighting. Local Moran's I analysis reveals that the High-High (H-H) county clusters are located in coastal regions of Croatia, while the Low-Low (L-L) county clusters appear in the East continental part, next to Slavonia. The results suggest that inadequate lighting conditions have an impact on the clustering of pedestrian crashes at night.

2024 (Vol 36), Issue 6

2024 (Vol 36), Issue 6

This paper presents a novel traffic flow prediction method emphasising heterogeneous vehicle characteristics and visual density features. Traditional models often overlook the variety of vehicles, resulting in inaccuracies. The proposed method utilises visual techniques to quantify traffic features, such as mixed flow and vehicle accumulation, enhancing dynamic density estimation and flow fluidity. We introduce a spatio-temporal prediction model that integrates various data types, capturing complex dependencies and improving accuracy. This research advances traffic flow prediction by considering the diverse nature of vehicles and leveraging visual data, offering valuable insights for intelligent transportation systems. Experimental results demonstrate the superiority of this approach over conventional methods, especially in capturing traffic flow fluctuations.

2024 (Vol 36), Issue 6

With the rapid expansion of ride-hailing services, it has gradually become a new travel choice for urban residents. Various research studies have focused on market relationships and platform strategies from the perspective of platform competition. However, little research has been studying issues related to the platform integration of ride-hailing services from the corporate perspective. Based on an analysis of integration modes and travellers’ behavioural factors, we established an evolutionary game model to study travellers’ choice behaviour under the integration of ride-hailing platforms. Furthermore, this study employed methods of model deduction and numerical study. The findings indicate the following. (1) When the travel risk associated with platform integration is high, travellers are less likely to choose ride-hailing services, and the integration strategy of ride-hailing platforms will not be pursued. (2) Ride-hailing platforms tend to interconnect with larger-scale platforms. (3) As the negative effect of perceived sacrifice decreases, ride-hailing platforms are more likely to interconnect with other platforms, and travellers are more inclined to choose ride-hailing services. (4) A higher cost of platform integration will decrease the probability of ride-hailing platforms adopting an integration strategy, but it will not significantly impact travellers’ behaviour.

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

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

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Special Issue Is Out

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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.

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Editor's Choice Papers

Explore the selection of scientific papers handpicked by the editor

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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

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

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

Snežana Tadić, Mladen Krstić, Milovan Kovač, Nikolina Brnjac

The negative effects of goods flows realisation are most visible in urban areas as the places of the greatest concentration of economic and social activities. The main goals of this article were to identify the applicable Industry 4.0 technologies for performing various city logistics (CL) operations, establish smart sustainable CL solutions (SSCL) and rank them in order to identify those which will serve as the base points for future plans and strategies for the development of smart cities. This kind of problem requires involvement of multiple stakeholders with their opposing goals and interests, and thus multiple criteria. For solving it, this article proposed a novel hybrid multi-criteria decision-making (MCDM) model, based on BWM (Best-Worst Method) and CODAS (COmbinative Distance-based ASsessment) methods in grey environment. The results of the model application imply that the potentially best SSCL solution is based on the combination of the concepts of micro-consolidation centres and autonomous vehicles with the support of artificial intelligence and Internet of Things technologies. The main contributions of the article are the definition of original SSCLs, the creation of a framework and definition of criteria for their evaluation and the development of a novel hybrid MCDM model.

2022 (Vol 34), Issue 5

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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