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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
31.08.2023
DOI
https://doi.org/10.7307/ptt.v35i4.181
ISSUE
2023 (Vol 35), Issue 4
LICENSE
Copyright (c) 2024 Domokos Esztergár-Kiss
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Transportation Research Challenges Based on the Analysis of EU Projects

Authors:Domokos Esztergár-Kiss

Abstract

In recent years several projects have been realised in the field of transportation, but there is a lack of systematic analysis of research challenges connected to these projects. Thus, the main aim of this paper is to provide an overview of these challenges through EU funded projects in the field of smart, green and integrated transport. Based on EU strategic documents, reports and roadmaps, 10 topics are identified playing a crucial role in transportation-related research. A systematic analysis of the projects is realised, where the projects collected from an online database in the Horizon 2020 framework programme from 2015 to 2020 are categorised into these topics. The results show that travel behaviour, big data and open data, sustainable mobility planning and smart solutions are covered by several projects which reflect the main research trends. While active and shared modes, multimodal transportation, trip optimisation and Mobility as a Service are also popular topics. Based on the results, the most underrepresented research areas are artificial intelligence and social networks. The analysis of the connections between the research topics could enable the achievement of a long-term paradigm shift in urban mobility, which is beneficial for researchers, professionals and policy makers.

Keywords:transport research, Horizon 2020, research topics, challenges

References

  1. [1] European Commission. Horizon 2020: Smart, green and integrated transport. 2020. https://trimis.ec.europa.eu/programme/horizon-2020-smart-green-and-integrated-transport [Accessed 10th Mar. 2023].
  2. [2] European Commission. European climate, infrastructure and environment executive agency. 2021. https://wayback.archive-it.org/12090/20221207155240/https://ec.europa.eu/inea/horizon-2020 [Accessed 10th Mar. 2023].
  3. [3] Suran S, Pattanaik V, Yahia SB, Draheim D. Exploratory analysis of collective intelligence projects developed within the EU-Horizon 2020 framework. Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science. 2019. Vol. 11684. DOI: 10.1007/978-3-030-28374-2_25.
  4. [4] Colombo LA, Pansera M, Owen R. The discourse of eco-innovation in the European Union: An analysis of the eco-innovation action plan and Horizon 2020. Journal of Cleaner Production. 2019;214:653-665. DOI: 10.1016/j.jclepro.2018.12.150.
  5. [5] Marzi E, Morini M, Gambarotta A. Analysis of the status of research and innovation actions on electrofuels under Horizon 2020. Energies. 2022;15(2):618. DOI: 10.3390/en15020618.
  6. [6] Saletti C, Morini M, Gambarotta A. The status of research and innovation on heating and cooling networks as smart energy systems within Horizon 2020. Energies. 2022;13(11):2835. DOI: 10.3390/en13112835.
  7. [7] European Commission. Roadmap to a single European transport area - Towards a competitive and resource efficient transport system. 2011. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0144:FIN:EN:PDF [Accessed 10th Mar. 2023].
  8. [8] European Commission. EU transport research & innovation status assessment report: An overview based on the transport research and innovation monitoring and information system (TRIMIS) database. 2019. https://data.europa.eu/doi/10.2760/6954 [Accessed 10th Mar. 2023].
  9. [9] European Commission. Strategic transport research and innovation agenda (STRIA). 2019. https://research-and-innovation.ec.europa.eu/research-area/transport/stria_en [Accessed 10th Mar. 2023].
  10. [10] Schlich R, Axhausen KW. Habitual travel behaviour: Evidence from a six-week travel diary. Transportation. 2003;30(1):13-36. DOI: 10.1023/A:1021230507071.
  11. [11] Glykeria M, Morfoulaki M, Vassilantonakis B-M, Mpoutovinas A, Kotoula KM. Travelers-led innovation in sustainable urban mobility plans. Periodica Polytechnica: Transportation Engineering. 2020;48(2):126-132. DOI: 10.3311/PPtr.11909.
  12. [12] Hickman R, Hall P, Banister D. Planning more for sustainable mobility. Journal of Transport Geography. 2013;33:210-219. DOI: 10.1016/j.jtrangeo.2013.07.004.
  13. [13] Litman T. Introduction to multi-modal transportation planning - principles and practices. 2021. https://www.vtpi.org/multimodal_planning.pdf [Accessed 10th Mar. 2023]
  14. [14] Frade I, Anabela R. Bike-sharing stations: A maximal covering location approach. Transportation Research Part A: Policy and Practice. 2015;82:216-227. DOI: 10.1016/j.tra.2015.09.014.
  15. [15] European Commission. Smart mobility and services: Expert group report. 2018. https://data.europa.eu/doi/10.2777/490085 [Accessed 10th Mar. 2023].
  16. [16] Iyer LS. AI enabled applications towards intelligent transportation. Transportation Engineering. 2021;5:100083. DOI: 10.1016/j.treng.2021.100083.
  17. [17] Kitchin R. The data revolution: A critical analysis of big data, open data and data infrastructures. New York, NY, United States: SAGE Publications Ltd; 2021.
  18. [18] Jittrapirom Pet al. Mobility as a service: A critical review of definitions. Urban Planning. 2017;2(2): 13-25. DOI: 10.17645/up.v2i2.931.
  19. [19] Anthopoulos LG, Vakali A. Urban planning and smart cities: Interrelations and reciprocities. The Future of Internet - Lecture Notes in Computer Science. 2012;7281:178-189. DOI: 10.1007/978-3-642-30241-1_16.
  20. [20] Gal-Tzur A, Grant-Muller SM, Minkov E, Nocera S. The impact of social media usage on transport policy: Issues, challenges and recommendations. Procedia - Social and Behavioral Sciences. 2014;111:937-946. DOI: 10.1016/j.sbspro.2014.01.128.
  21. [21] Le Pira M, Ignaccolo M, Inturri G, Pluchino A, Rapisarda A. Modelling stakeholder participation in transport planning. Case Studies on Transport Policy. 2016;4(3):230-238. DOI: 10.1016/j.cstp.2016.06.002.
  22. [22] Garcia-Ayllon S, Hontoria E, Munier N. The contribution of MCDM to SUMP: The case of Spanish cities during 2006–2021. International Journal of Environmental Research and Public Health. 2022;19(1):294. DOI: 10.3390/ijerph19010294.
  23. [23] Kiba-Janiak M, Witkowski J. Sustainable urban mobility plans: how do they work? Sustainability. 2019;11(17):4605. DOI: 10.3390/su11174605.
  24. [24] Detti A, et al. Federation and orchestration: A scalable solution for EU multimodal travel information services. Sustainability. 2019;11(7):1888. DOI: 10.3390/su11071888.
  25. [25] Giannakopoulou K, Paraskevopoulos A, Zaroliagis C. Multimodal dynamic journey-planning. Algorithms. 2019;12(10):213. DOI: 10.3390/a12100213.
  26. [26] Esztergár-Kiss D. Trip chaining model with classification and optimization parameters. Sustainability. 2020;12(16):6422. DOI: 10.3390/su12166422.
  27. [27] Lim C, Kim K-W, Maglio P. Smart cities with big data: reference models, challenges and considerations. Cities. 2018;82:86-99. DOI: 10.1016/j.cities.2018.04.011.
  28. [28] Zhu L, et al. Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems. 2019;20(1):383-398. DOI: 10.1109/TITS.2018.2815678.
  29. [29] Abduljabbar R, Dia H, Liyanage S, Bagloee SA. Applications of artificial intelligence in transport: An overview. Sustainability. 2019;11(1):189. DOI: 0.3390/su11010189.
  30. [30] Santoni de Sio F, Mecacci G. Four responsibility gaps with artificial intelligence: Why they matter and how to address them. Philosophy & Technology. 2021;34:1057-1084. DOI: 0.1007/s13347-021-00450-x.
  31. [31] Hopkins D. Destabilising automobility? The emergent mobilities of generation Y. Ambio. 2017;46:371-383. DOI: 10.1007/s13280-016-0841-2.
  32. [32] Dias FF, et al. A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation. 2017;44:1307-1323. DOI: 10.1007/s11116-017-9797-8.
  33. [33] Campbell AA, Cherry CR, Ryerson MS, Yang X. Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transportation Research Part C: Emerging Technologies. 2016;67:399-414. DOI: 10.1016/j.trc.2016.03.004.
  34. [34] Matyas M, Kamargianni M. The potential of mobility as a service bundles as a mobility management tool. Transportation. 2019;46:1951-1968. DOI: 10.1007/s11116-018-9913-4.
  35. [35] Butler L, Yigitcanlar T, Paz A. Barriers and risks of mobility-as-a-service (MaaS) adoption in cities: A systematic review of the literature. Cities. 2021;109:103036. DOI: 10.1016/j.cities.2020.103036.
  36. [36] Albino V, Berardi U, Dangelico RM. Smart cities: Definitions, dimensions, performance and initiatives. Journal of Urban Technology. 2015;22(1):3-21. DOI: 10.1080/10630732.2014.942092.
  37. [37] Angelakoglou K, et al. A methodological framework for the selection of key performance indicators to assess smart city solutions. Smart Cities. 2019;2(2):269-306. DOI: 10.3390/smartcities2020018.
  38. [38] Rashidi TH, Abbasi A, Maghrebi M, Hasan S, Waller TS. Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges. Transportation Research Part C: Emerging Technologies. 2017;75:197-211. DOI: 10.1016/j.trc.2016.12.008.
  39. [39] Osorio-Arjona J, García-Palomares JC. Social media and urban mobility: Using twitter to calculate home - work travel matrices. Cities. 2019;89:268-280. DOI: 10.1016/j.cities.2019.03.006.
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