@article{MTMT:34726783,
	title = {Empirical model and side friction factor effect on desired operating speed at horizontal curved road},
	url = {https://m2.mtmt.hu/api/publication/34726783},
	author = {Jima, Debela and Sipos, Tibor},
	doi = {10.1016/j.jer.2024.01.026},
	journal-iso = {J ENG RES-KUWAIT},
	journal = {JOURNAL OF ENGINEERING RESEARCH},
	unique-id = {34726783},
	issn = {2307-1877},
	abstract = {The road surface and its geometric condition affect the vehicle's desired operating speed. This paper is framed to examine an empirical model for the desired operating speed due to the variation of the side friction factor at horizontal road curvature across the design period. The minimum radius equation developed by the American Association of State Highway and Transportation Officials (AASHTO) was used to analyze the desired operating speed at the confessed minimum radius, maximum side friction factor, and superelevation. This empirical formula indicated that speed and side friction factors had a direct relationship. Even though this empirical equation cannot define the relationship between desired speed and side friction across the design period except at the opening phase of the road, based on the research gap indicated above, this study proposes an empirical model used to explore the desired operating speed that resulted from the variation of the side friction factor across the design period. To have a structured flow of ideas, a theoretical model was used to create the approach of the specific research inquiry. For exploration purposes, this study used a maximum superelevation of 8 %, the same minimum radius, and the AASTHO recommended side friction factor. Based on the proposed empirical model, this study confirmed that the desired speed increases and decreases as side friction decreases and increases, respectively. Except that vehicular movement is in a static state, at an unrealistic side friction factor (f = 0), and at a critical zone. © 2024 The Authors},
	keywords = {Surface roughness; Empirical model; Side friction factor; Design (assumed) speed; Desired operating speed},
	year = {2024},
	eissn = {2307-1885}
}

@article{MTMT:34714781,
	title = {Exploring factors influencing consumer preferences for automated driving vehicles},
	url = {https://m2.mtmt.hu/api/publication/34714781},
	author = {ABDULLAH, PIRES and Sipos, Tibor},
	doi = {10.1016/j.treng.2024.100233},
	journal-iso = {TRANSPORT ENGIN},
	journal = {TRANSPORTATION ENGINEERING},
	volume = {16},
	unique-id = {34714781},
	abstract = {Adopting automated driving vehicles (AVs) promises to transform transportation systems, yet understanding individual preferences is essential for effective implementation. This study uses decision tree analysis to investigate the factors influencing preferences for partially automated vehicles (AV3) and fully automated vehicles (AV5). Surveys were conducted in the Duhok-Kurdistan Region of Iraq to explore attitudes toward AV technology. Respondents selected their preferred option from AV3 and AV5, with various factors considered in the analysis, including age, gender, frequency of trips, and mobility concerns. Results revealed a preference for AV3, with trip frequency emerging as a key determinant. Older respondents tended towards AV3. The number of trips was the most significant determinant in their decision-making process, with individuals who frequently traveled showing a preference for fully automated AVs. Furthermore, understanding these preferences can inform strategies for promoting AV adoption and integration into transportation systems, shaping future mobility. The study's contributions lie in its localized focus and the identification of key factors influencing AV preferences in a specific region. This insight can assist in developing tailored strategies to promote AV adoption and address mobility challenges.},
	year = {2024},
	eissn = {2666-691X}
}

@article{MTMT:34685782,
	title = {Who should Communicate to Make the World a Greener Place? Car Brand Loyalty as an Essential Attribute of Pro-Environmental Education},
	url = {https://m2.mtmt.hu/api/publication/34685782},
	author = {Jana, Majerova and Lubica, Gajanova and Margareta, Nadanyiova and Sipos, Tibor},
	doi = {10.12700/APH.21.7.2024.7.7},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34685782},
	issn = {1785-8860},
	abstract = {This article aims to investigate the socio-economic nature of car brand loyalty. 
		The hypothesis is that brand loyalty is a complex structure with a complicated socioeconomic background. Nowadays, massive research is focused mainly on the 
		psychographic background of the brand loyalty phenomenon. However, a socio-economic 
		aspect of consumers is also entering into brand loyalty creation. It is supposed that brands 
		with loyal consumers have more educative strength than brands where brand loyalty is 
		missing. As it is challenging to identify consumers with a psychographic profile suitable for 
		loyal brands, the changes in consumer behaviour are hard to reach. However, if the socioeconomic profile of loyal consumers is detected, environmental education can be focused 
		precisely and with higher effectiveness. Some preliminary data have been collected in 
		Slovakia to investigate the socio-economic background of car brand loyalty. To collect 
		these data, a questionnaire survey has been used. It was realised in the last quarter of the 
		year 2021 on the sample of 2035 Slovak inhabitants older than 15 years. Statistical 
		analysis has been provided via a decision tree approach. It has been found that two 
		relevant factors significantly influence brand loyalty - respondents' age and income. Based 
		on these findings, it can be stated that, in general, brands with the higher proenvironmental communication potential in the case of the Slovak Republic are Škoda, Audi 
		and BMW. An approach that includes the strength of pro-environmental orientation of 
		consumers (focusing on age categories) confirms this order. However, in the case of the 
		income perspective, Peugeot's list of car brands should be extended, which has been 
		surprisingly identified as a car brand with the potential to be used widely in the scope of 
		pro-environmental education in specific area’s conditions in the Slovak Republic.},
	year = {2024},
	eissn = {1785-8860}
}

@article{MTMT:34498267,
	title = {Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity},
	url = {https://m2.mtmt.hu/api/publication/34498267},
	author = {ABDULLAH, PIRES and Sipos, Tibor},
	doi = {10.3311/PPtr.22392},
	journal-iso = {PERIOD POLYTECH TRANSP ENG},
	journal = {PERIODICA POLYTECHNICA TRANSPORTATION ENGINEERING},
	volume = {52},
	unique-id = {34498267},
	issn = {0303-7800},
	abstract = {This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study. © 2024 Budapest University of Technology and Economics. All rights reserved.},
	keywords = {Hungary; computer software; Decision Trees; machine learning; machine learning; Budapest; Roads and streets; Decision tree; Machine-learning; road traffic accidents; High level languages; Motor transportation; Accident analysis; injury severity; Data analytics; road safety; road safety; Primary objective; Black spot; Highway accidents; black spots; traffic accident analysis; Traffic accident analyse},
	year = {2024},
	eissn = {1587-3811},
	pages = {33-39}
}

@inproceedings{MTMT:34557733,
	title = {Analyzing Traffic Accidents Based on Peak and Off-Peak Time},
	url = {https://m2.mtmt.hu/api/publication/34557733},
	author = {ABDULLAH, PIRES and Sipos, Tibor},
	booktitle = {XIII. International Conference on Transport Sciences / XIII. Nemzetközi Közlekedéstudományi Konferencia, Győr},
	unique-id = {34557733},
	year = {2023},
	pages = {53-58}
}

@article{MTMT:34554389,
	title = {Traffic Accidents Analysis Using QGIS and Binary Decision Tree},
	url = {https://m2.mtmt.hu/api/publication/34554389},
	author = {ABDULLAH, PIRES and Sipos, Tibor},
	doi = {10.1016/j.trpro.2023.11.640},
	journal-iso = {TRANSP RES PROCEDIA},
	journal = {TRANSPORTATION RESEARCH PROCEDIA},
	volume = {72},
	unique-id = {34554389},
	issn = {2352-1465},
	abstract = {Traffic accident data includes many factors in order to be investigated. The main aim of this study is to analyze traffic accidents in terms of the spatial location in which they have occurred. Moreover, to identify the main socioeconomic factors that lead to frequent accidents by drivers. The questionnaire included items related to the location of the accident and the number of accidents a driver has had in a 10-year period. The study took place in the city of Duhok in the Kurdistan Region of Iraq. The study's findings showed that the city's center was the main area of accidents in general, while severe crashes had different "black spots" across the city's road network. Furthermore, the Decision Tree's classification of the driver's multiple accidents placed the level of education at the top, followed by gender and age, respectively. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)},
	keywords = {machine learning; black spot analysis; decision tree clasifier; trafffic accidents},
	year = {2023},
	eissn = {2352-1457},
	pages = {1677-1684}
}

@inproceedings{MTMT:34414519,
	title = {Examining the Role of Distracting Factors and Cognitive Elements in Pedestrian Safety},
	url = {https://m2.mtmt.hu/api/publication/34414519},
	author = {Krizsik, Nóra and Sipos, Tibor},
	booktitle = {2023 IEEE 2nd International Conference on Cognitive Mobility (CogMob)},
	unique-id = {34414519},
	year = {2023},
	pages = {107-112},
	orcid-numbers = {Krizsik, Nóra/0000-0002-4784-2219}
}

@inproceedings{MTMT:34414513,
	title = {Classification of Traffic Accident Severity Using Machine Learning Models},
	url = {https://m2.mtmt.hu/api/publication/34414513},
	author = {Sipos, Tibor and Hamdan, Noura},
	booktitle = {2023 IEEE 2nd International Conference on Cognitive Mobility (CogMob)},
	unique-id = {34414513},
	year = {2023},
	pages = {101-106},
	orcid-numbers = {Hamdan, Noura/0009-0002-0236-1355}
}

@inproceedings{MTMT:34402156,
	title = {Seasonal Impact on Cycling Accidents. Exploring Cognitive Abilities and Black Spot Distribution},
	url = {https://m2.mtmt.hu/api/publication/34402156},
	author = {Sipos, Tibor},
	booktitle = {2023 IEEE 2nd International Conference on Cognitive Mobility (CogMob)},
	unique-id = {34402156},
	year = {2023},
	pages = {81-86}
}

@article{MTMT:34382606,
	title = {Evaluation of Stated Preference Surveys with Statistical Methods},
	url = {https://m2.mtmt.hu/api/publication/34382606},
	author = {Sipos, Tibor},
	doi = {10.7307/ptt.v35i5.259},
	journal-iso = {PROMET-ZAGREB},
	journal = {PROMET-TRAFFIC & TRANSPORTATION},
	volume = {35},
	unique-id = {34382606},
	issn = {0353-5320},
	abstract = {In this paper, the author investigated the stated preference survey in transport modelling. The research was conducted to ensure that the best fractional orthogonal design of stated preference paired comparison survey would not increase the error or uncertainty in transport-related decision modelling. The research was conducted based on artificial Monte Carlo simulated respondents, and the results were assessed with standard mathematical-statistical tools. Although the assessment should have resulted in 0% errors, according to our 2,000 sample, a minor 5% of errors occurred. The problem to be investigated in this paper is that the best-designed survey could have some errors.},
	year = {2023},
	eissn = {1848-4069},
	pages = {655-661},
	orcid-numbers = {Sipos, Tibor/0000-0003-0304-7659}
}