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Intelligent mobility : a model for assessing the safety of children traveling to school on a school bus with the use of intelligent bus stops

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the article is to develop a model for assessing the safety of children’s travel. Safety is the most important indicator describing the mobility system of children, even more important than the costs of operating it. Due to the dynamic development of intelligent solutions, it is possible to undertake additional activities supporting the improvement of children’s safety when traveling to and from school. However, their implementation requires an adequate assessment of a children’s mobility system. Currently, there are no solutions that could comprehensively support the decision-making process in this sphere. The article presents the issues of children’s mobility, a literature review in this area, mathematical model for assessing school bus travel, and a computational example. The presented approach is an original solution allowing for evaluation of the existing systems and their development scenarios. In addition, it enables the comparison of children mobility systems of different complexity and scale.
Rocznik
Strony
695--706
Opis fizyczny
Bibliogr. 44 poz., rys., tab.
Twórcy
  • Warsaw University of Technology, Faculty of Transport, ul. Koszykowa 75, 00-662, Warsaw, Poland
  • Warsaw University of Technology, Faculty of Transport, ul. Koszykowa 75, 00-662, Warsaw, Poland
  • Warsaw University of Technology, Faculty of Mechanical and Industrial Engineering, ul. Narbutta 85, 02-524 Warsaw, Poland
  • Warsaw University of Technology, Faculty of Transport, ul. Koszykowa 75, 00-662, Warsaw, Poland
  • Motor Transport Institute, ul. Jagiellońska 80, 03-301 Warsaw, Poland
Bibliografia
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  • 5. Eguizábal S E, Berodia J L M, Portilla Á I, Ponce J B. Optimization model for school transportation design based on economic and social efficiency. Transport Policy 2018; 67: 93–101, https://doi.org/10.1016/j.tranpol.2018.01.015.
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  • 10. Izdebski M, Jacyna-Gołda I, Gołda P. Minimisation of the probability of serious road accidents in the transport of dangerous goods. Reliability Engineering & System Safety 2022; 217: 108093, https://doi.org/10.1016/j.ress.2021.108093.
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  • 12. Jacyna M, Semenov I. Models of vehicle service system supply under information uncertainty. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22(4): 694–704, https://doi.org/10.17531/ein.2020.4.13.
  • 13. Jacyna M, Wasiak M, Jachimowski R et al. The Concept of EPLOS Database of the Transport Infrastructure. In Kersys R (ed): Transport Means 2019. Sustainability: Research and Solutions. Proceedings of 23rd International Scientific Conference, Publishing House “Technologija”:2019: 1250–1255.
  • 14. Jacyna M, Wasiak M, Lewczuk K et al. Decision problems in developing proecological transport system. Annual Set The Environment Protection 2018; 20: 1007–1025.
  • 15. Jankowska D M. Integrated system for safe transportation of children to school - with the use of intelligent transport systems (ITS). Journal of KONES 2008; Vol. 15, No. 2: 137–144.
  • 16. Jankowska-Karpa D, Wnuk A. Strategie planowania podróży, jako narzędzie poprawiające bezpieczeństwo dzieci w ruchu drogowym. Transport Samochodowy 2015; z. 2: 65–88.
  • 17. Kamal M, Atif M, Mujahid H et al. IoT Based Smart City Bus Stops. Future Internet 2019, https://doi.org/10.3390/fi11110227.
  • 18. Kattan L, Tay R, Acharjee S. Managing speed at school and playground zones. Accident Analysis & Prevention 2011; 43(5): 1887–1891, https://doi.org/10.1016/j.aap.2011.04.009.
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  • 20. Lewczuk K, Kłodawski M. Logistics information processing systems on the threshold of IoT. Zeszyty Naukowe. Transport / Politechnika Śląska 2020; 107: 85–94, https://doi.org/10.20858/sjsutst.2020.107.6.
  • 21. Mandic S, Barra S L de la, Bengoechea E G et al. Personal, social and environmental correlates of active transport to school among adolescents in Otago, New Zealand. Journal of Science and Medicine in Sport 2015; 18(4): 432–437, https://doi.org/10.1016/j.jsams.2014.06.012.
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  • 23. Mehdizadeh M, Nordfjaern T, Mamdoohi A R, Mohaymany A S. The role of parental risk judgements, transport safety attitudes, transport priorities and accident experiences on pupils’ walking to school. Accident Analysis & Prevention 2017; 102: 60–71, https://doi.org/10.1016/j.aap.2017.02.020.
  • 24. Nasrudin N, Nor A R M. Travelling to School: Transportation Selection by Parents and Awareness towards Sustainable Transportation. Procedia Environmental Sciences 2013; 17: 392–400, https://doi.org/10.1016/j.proenv.2013.02.052.
  • 25. Raj J T, Sankar J. IoT based smart school bus monitoring and notification system. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017: 89–92, https://doi.org/10.1109/R10-HTC.2017.8288913.
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  • 27. Rosenberg D E, Sallis J F, Conway T L et al. Active Transportation to School Over 2 Years in Relation to Weight Status and Physical Activity. Obesity 2006; 14(10): 1771–1776, https://doi.org/10.1038/oby.2006.204.
  • 28. Rudyk T, Szczepański E, Jacyna M. Safety factor in the sustainable fleet management model. Archives of Transport 2019; 49(1): 103–114, https://doi.org/10.5604/01.3001.0013.2780.
  • 29. Sadeghian P, Håkansson J, Zhao X. Review and evaluation of methods in transport mode detection based on GPS tracking data. Journal of Traffic and Transportation Engineering (English Edition) 2021; 8(4): 467–482, https://doi.org/10.1016/j.jtte.2021.04.004.
  • 30. Shaaban K, Bekkali A, Hamida E B, Kadri A. Smart tracking system for school buses using passive RFID technology to enhance child safety. Journal of Traffic and Logistics Engineering; 1(2): 191–196, https://doi.org/10.12720/jtle.1.2.191-196.
  • 31. Shinde P A, Mane Y B. Advanced vehicle monitoring and tracking system based on Raspberry Pi. 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), 2015: 1–6, https://doi.org/10.1109/ISCO.2015.7282250.
  • 32. Soczówka P, Klos M, Zochowska R, Sobota A. An analysis of the influence of travel time on access time in public transport. Scientific Journal of Silesian University of Technology. Series Transport 2021; 111: 137–149, https://doi.org/10.20858/sjsutst.2021.111.12.
  • 33. Stark J, Frühwirth J, Aschauer F. Exploring independent and active mobility in primary school children in Vienna. Journal of Transport Geography 2018; 68: 31–41, https://doi.org/10.1016/j.jtrangeo.2018.02.007.
  • 34. Sun D, El-Basyouny K, Ibrahim S, Kim A M. Are school zones effective in reducing speeds and improving safety? Canadian Journal of Civil Engineering 2018; 45(12): 1084–1092, https://doi.org/10.1139/cjce-2018-0060.
  • 35. Świderski A, Jóźwiak A, Jachimowski R. Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20(2): 292–299, https://doi.org/10.17531/ein.2018.2.16.
  • 36. Thibaud M, Chi H, Zhou W, Piramuthu S. Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: A comprehensive review. Decision Support Systems 2018; 108: 79–95, https://doi.org/10.1016/j.dss.2018.02.005.
  • 37. Transportation Research Board. The Relative Risks of School Travel: A National Perspective and Guidance for Local Community Risk Assessment – Special Report 269. Washington, DC, The National Academies Press: 2002. doi:10.17226/10409, https://doi.org/10.17226/10409.
  • 38. Wang H, Morgan C, Li D et al. Children’s fear in traffic and its association with pedestrian decisions. Journal of Safety Research 2021; 76: 56–63, https://doi.org/10.1016/j.jsr.2020.11.010.
  • 39. Westman J, Friman M, Olsson L E. What Drives Them to Drive?—Parents’ Reasons for Choosing the Car to Take Their Children to School. Frontiers in Psychology 2017, https://doi.org/10.3389/fpsyg.2017.01970.
  • 40. Yongjun Z, Xueli Z, Shuxian Z, shenghui G. Intelligent transportation system based on Internet of Things. World Automation Congress 2012, 2012: 1–3.
  • 41. Travel and Environmental Implications of School Siting. U.S. Environmental Protection Agency: 2003.
  • 42. Dzieci w wieku 0-14 lat w ruchu drogowym dla okresu od 2010 do 2019 roku. Polskie Obserwatorium Bezpieczeństwa Ruchu Drogowego: 2020.
  • 43. Global Status Report on Road Safety 2018. Geneva, World Health Organization: 2018.
  • 44. Integrated system for safe transportation of children to school(SAFEWAY2SCHOOL). [https://cordis.europa.eu/project/id/233967].
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-49b496ca-97ec-4fd8-a14e-f8872d2cdff9
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