SCIENCE ARTICLE
Social knowledge and information management in the field of urban intelligent transportation
 
 
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Management Faculty, Czestochowa University of Technology, Poland
 
 
Submission date: 2024-08-27
 
 
Final revision date: 2025-02-03
 
 
Acceptance date: 2025-03-24
 
 
Online publication date: 2025-04-14
 
 
Publication date: 2025-05-09
 
 
Corresponding author
Marta Daroń   

Management Faculty, Czestochowa University of Technology, Poland
 
 
Management 2025;(1):297-314
 
KEYWORDS
JEL CLASSIFICATION CODES
D83
L91
M38
O18
 
TOPICS
ABSTRACT
Research background and purpose: Urban Intelligent Transportation (UIT) systems are integral to modern city mobility, yet societal awareness and knowledge of these solutions remain underexplored. This study investigates the level of knowledge and recognition of UIT among residents of selected European countries and examines the role of municipal authorities in disseminating relevant information. The purpose of the study was to investigate the level of knowledge and acceptance of innovative solutions in the field of intelligent urban transportation among society. Design/methodology/approach: A survey using the CAWI method was conducted in Poland, Turkey, and other European countries. A total of 572 responses were collected and analyzed using statistical methods, including logistic regression, to identify key determinants of UIT knowledge. The study also evaluated respondents’ perceptions of local government actions in UIT communication. Findings: The results indicate that 58% of respondents are familiar with the term UIT, but only 40.9% recognize UIT solutions in their cities. Significant differences were found across countries, with Poland exhibiting the lowest recognition rates. Demographic factors, including professional activity and mode of transport, significantly influence awareness levels, whereas age and gender do not. The study also highlights a strong expectation for municipal authorities to enhance their communication efforts, particularly in Poland, where 90% of respondents demand more UIT-related information. Value added and limitations: This study provides empirical insights into the societal perception of UIT, emphasizing the need for targeted awareness campaigns. It offers recommendations for city authorities to improve UIT communication strategies. However, the study is limited by sample representation disparities between countries and the self-reported nature of responses. Future research should extend the geographic scope and incorporate qualitative methods to deepen the analysis.
REFERENCES (25)
1.
Ali, A., Qureshi, M. A., Shiraz, M., & Shamim, A. (2021). Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control. Sustainable Computing: Informatics and Systems, 32, 100608. https://doi.org/10.1016/j.susc....
 
2.
Almeida, A., Brás, S., Sargento, S., & Oliveira, I. (2023). Exploring bus tracking data to characterize urban traffic congestion. Journal of Urban Mobility, 4, 100065. https://doi.org/10.1016/j.urbm....
 
3.
Belbachir, A., El Fallah-Seghrouchni, A., Casals, A., & Pasin, M. (2019). Smart mobility using multi-agent system. Procedia Computer Science, 151, 447-454. https://doi.org/10.1016/j.proc....
 
4.
Bucsky, P., & Juhász, M. (2022). Long-term evidence on induced traffic: A case study on the relationship between road traffic and capacity of Budapest bridges. Transportation Research Part A: Policy and Practice, 157, 244-257. https://doi.org/10.1016/j.tra.....
 
5.
Cederstav, F., Wandel, S., Segerborg-Fick, A., Rylander, D., Asp, T., & Ranäng, S. (2023). High capacity city transport with intelligent access - A Swedish case study of transporting excavated material. Transportation Research Procedia, 72, 712-718. https://doi.org/10.1016/j.trpr....
 
6.
Chen, J., Hu, M., & Shi, C. (2023). Development of eco-routing guidance for connected electric vehicles in urban traffic systems. Physica A: Statistical Mechanics and its Applications, 618, 128718. https://doi.org/10.1016/j.phys....
 
7.
Głębocki, K. (2024). Smart city strategic planning: Are there social grounds in medium-sized Polish cities? Polish Journal of Management Studies, 29(1), 132-143. https://doi.org/10.17512/pjms.....
 
8.
He, R., Xiao, Y., Lu, X., Zhang, S., & Liu, Y. (2023). ST-3DGMR: Spatio-temporal 3D grouped multiscale ResNet network for region-based urban traffic flow prediction. Information Sciences, 624, 68-93. https://doi.org/10.1016/j.ins.....
 
9.
Hou, Q., Li, W., Zhang, X., Fang, Y., Duan, Y., Zhang, L., & Liu, W. (2020). Intelligent urban planning on smart city blocks based on bicycle travel data sensing. Computer Communications, 153, 26-33. https://doi.org/10.1016/j.comc....
 
10.
Hou, Y., Chen, J., & Wen, S. (2021). The effect of the dataset on evaluating urban traffic prediction. Alexandria Engineering Journal, 60(1), 597-613. https://doi.org/10.1016/j.aej.....
 
11.
Kujawski, A., & Dudek, T. (2021). Analysis and visualization of data obtained from camera mounted on unmanned aerial vehicle used in areas of urban transport. Sustainable Cities and Society, 72, 103004. https://doi.org/10.1016/j.scs.....
 
12.
Liu, W., Gong, Y., Chen, W., & Zhang, J. (2020). EvoTSC: An evolutionary computation-based traffic signal controller for large-scale urban transportation networks. Applied Soft Computing, 97, 106640. https://doi.org/10.1016/j.asoc....
 
13.
Liu, Z., Jia, H., & Wang, Y. (2020). Urban expressway parallel pattern recognition based on intelligent IoT data processing for smart city. Computer Communications, 155, 40-47. https://doi.org/10.1016/j.comc....
 
14.
Minh, Q. T., Tan, P. D., Le Hoang, H. N., & Nhat, M. N. (2022). Effective traffic routing for urban transportation capacity and safety enhancement. IATSS Research, 46(4), 574-585. https://doi.org/10.1016/j.iats....
 
15.
Ouallane, A., Bakali, A., Bahnasse, A., Broumi, S., & Talea, M. (2022). Fusion of engineering insights and emerging trends: Intelligent urban traffic management system. Information Fusion, 88, 218-248. https://doi.org/10.1016/j.inff....
 
16.
Pomianowski, A. (2023). The use of smartphone applications when buying public transport and paid parking zone tickets on the example of Szczecin. Procedia Computer Science, 225, 2449-2456. https://doi.org/10.1016/j.proc....
 
17.
Sobral, T., Galvão, T., & Borges, J. (2020). An ontology-based approach to knowledge-assisted integration and visualization of urban mobility data. Expert Systems with Applications, 150, 113260. https://doi.org/10.1016/j.eswa....
 
18.
Tay, L., Lim, J. M., Liang, S., & Tay, Y. H. (2023). Urban traffic volume estimation using intelligent transportation system crowdsourced data. Engineering Applications of Artificial Intelligence, 126, 107064. https://doi.org/10.1016/j.enga....
 
19.
Tu, Q., Cheng, L., Yuan, T., Cheng, Y., & Li, M. (2020). The constrained reliable shortest path problem for electric vehicles in the urban transportation network. Journal of Cleaner Production, 261, 121130. https://doi.org/10.1016/j.jcle....
 
20.
Waqar, A., Alshehri, A. H., Alanazi, F., Alotaibi, S., & Almujibah, H. R. (2023). Evaluation of challenges to the adoption of intelligent transportation system for urban smart mobility. Research in Transportation Business & Management, 51, 101060. https://doi.org/10.1016/j.rtbm....
 
21.
Xia, D., Zheng, L., Tang, Y., Cai, X., & Sun, D. (2022). Dynamic traffic prediction for urban road network with the interpretable model. Physica A: Statistical Mechanics and its Applications, 605, 128051. https://doi.org/10.1016/j.phys....
 
22.
Xu, Y., Cai, X., Wang, E., Liu, W., Yang, Y., & Yang, F. (2023). Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction. Information Sciences, 621, 580-595. https://doi.org/10.1016/j.ins.....
 
23.
Yannis, G., & Chaziris, A. (2022). Transport System and Infrastructure. Transportation Research Procedia, 60, 6-11. https://doi.org/10.1016/j.trpr....
 
24.
Yin, G., Huang, Z., Yang, L., Ben-Elia, E., Xu, L., Scheuer, B., & Liu, Y. (2023). How to quantify the travel ratio of urban public transport at a high spatial resolution? A nowel computational framework with geospatial big data. International Journal of Applied Earth Observation and Geoinformation, 118, 103245, https://doi.org/10.1016/j.jag.....
 
25.
Zheng, Y., Wang, S., Dong, C., Li, W., Zheng, W., & Yu, J. (2022). Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism. Physica A: Statistical Mechanics and its Applications, 608, 128274. https://doi.org/10.1016/j.phys....
 
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ISSN:1429-9321 (1997-2019)
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