Policy and Technological Synergies in Smart City Transportation

Authors

  • Muhammad Wahyuilahi Politeknik Astra
  • Setiadi Universitas Dirgantara Marsekal Suryadarma

DOI:

https://doi.org/10.61194/sijl.v3i1.738

Keywords:

Smart Transportation Systems, Big Data, Internet of Things, Machine Learning, Urban Mobility, Policy Integration, Socio-Cultural Dynamics

Abstract

This study investigates the integration of advanced technologies, such as big data, machine learning, and the Internet of Things (IoT), within intelligent transportation systems (ITS) in smart cities. The purpose is to identify the key factors influencing the successful implementation of ITS, focusing on technological, policy, and socio-cultural dynamics. A narrative review methodology was employed, synthesizing findings from multiple studies across various regions. The results reveal that while technological advancements have improved urban mobility, the success of ITS depends on the alignment of policies, infrastructure, and public perception. Key barriers include bureaucratic inertia, fragmented governance, and regulatory inflexibility. The study suggests that adaptive regulatory frameworks, enhanced public-private partnerships, and community involvement are essential for overcoming these challenges. Future research should focus on integrating socio-political factors with technological development to create more holistic solutions. The findings underline the need for a multidisciplinary approach to ensure the sustainability and effectiveness of smart transportation systems.

References

Alam, T., Gupta, R., Ahamed, N., Ullah, A., & Almaghthwi, A. (2024). Smart mobility adoption in sustainable smart cities to establish a growing ecosystem: challenges and opportunities. Mrs Energy & Sustainability, 11(2), 304–316. https://doi.org/10.1557/s43581-024-00092-4 DOI: https://doi.org/10.1557/s43581-024-00092-4

Bresciani, C., Colorni, A., Lia, F., Luè, A., & Nocerino, R. (2016). Behavioral change and social innovation through reward: an integrated engagement system for personal mobility, urban logistics and housing efficiency. Transportation Research Procedia, 14, 353–361. https://doi.org/10.1016/j.trpro.2016.05.087 DOI: https://doi.org/10.1016/j.trpro.2016.05.087

Deja, A., Ślączka, W., Kaup, M., Szołtysek, J., Dzhuguryan, L., & Dzhuguryan, T. (2024). Supply chain management in smart city manufacturing clusters: an alternative approach to urban freight mobility with electric vehicles. Energies, 17(21), 5284. https://doi.org/10.3390/en17215284 DOI: https://doi.org/10.3390/en17215284

Dudek, T., & Kujawski, A. (2022). The concept of big data management with various transportation systems sources as a key role in smart cities development. Energies, 15(24), 9506. https://doi.org/10.3390/en15249506 DOI: https://doi.org/10.3390/en15249506

Gürel, Ö., & Serdarasan, Ş. (2024). Drone-assisted last-mile delivery under windy conditions: zero pollution solutions. Smart Cities, 7(6), 3437–3457. https://doi.org/10.3390/smartcities7060134 DOI: https://doi.org/10.3390/smartcities7060134

Mehmood, R., Meriton, R., Graham, G., Hennelly, P., & Kumar, M. (2017). Exploring the influence of big data on city transport operations: a markovian approach. International Journal of Operations & Production Management, 37(1), 75–104. https://doi.org/10.1108/ijopm-03-2015-0179 DOI: https://doi.org/10.1108/IJOPM-03-2015-0179

Mitteregger, M., Bruck, E., Soteropoulos, A., Stickler, A., Berger, M., Dangschat, J., … & Banerjee, I. (2023). Avenue21. Planning and policy considerations for an age of automated mobility. https://doi.org/10.1007/978-3-662-67004-0 DOI: https://doi.org/10.1007/978-3-662-67004-0

Moumen, I., Abouchabaka, J., & Rafalia, N. (2023). Enhancing urban mobility: integration of IoT road traffic data and artificial intelligence in smart city environment. Indonesian Journal of Electrical Engineering and Computer Science, 32(2), 985–993. https://doi.org/10.11591/ijeecs.v32.i2.pp985-993 DOI: https://doi.org/10.11591/ijeecs.v32.i2.pp985-993

Oladimeji, D., Gupta, K., Kose, N., Gundogan, K., Ge, L., & Liang, F. (2023). Smart transportation: an overview of technologies and applications. Sensors, 23(8), 3880. https://doi.org/10.3390/s23083880 DOI: https://doi.org/10.3390/s23083880

Osetrin, M. (2024). Development of pricing policy for car parking in Ukrainian cities. Systemy Logistyczne Wojsk, 61(2), 37–52. https://doi.org/10.37055/slw/203435 DOI: https://doi.org/10.37055/slw/203435

Özkaynak, B., Aras, N., Çetinkaya, İ., Ersoy, C., İncel, Ö., Koca, M., … & Yücesoy, C. (2024). Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions. Frontiers in Neuroscience, 18. https://doi.org/10.3389/fnins.2024.1279668 DOI: https://doi.org/10.3389/fnins.2024.1279668

Rashid, M., Kamruzzaman, J., Hassan, M., Imam, T., & Gordon, S. (2020). Cyberattacks detection in IoT-based smart city applications using machine learning techniques. International Journal of Environmental Research and Public Health, 17(24), 9347. https://doi.org/10.3390/ijerph17249347 DOI: https://doi.org/10.3390/ijerph17249347

Sreenivasan, A., Suresh, M., Nedungadi, P., Sreedharan, V., & Raman, R. (2023). Interpretive structural modeling: research trends, linkages to sustainable development goals, and impact of COVID-19. Sustainability, 15(5), 4195. https://doi.org/10.3390/su15054195 DOI: https://doi.org/10.3390/su15054195

Ambekar, S., Roy, D., Hiray, A., Prakash, A., & Patyal, V. (2021). Barriers to adoption of reverse logistics: a case of construction, real estate, infrastructure and project (crip) sectors. Engineering Construction & Architectural Management, 29(7), 2878-2902. https://doi.org/10.1108/ecam-02-2021-0112

Chen, X., Qiu, D., & Chen, Y. (2024). Reverse logistics in the construction industry: status quo, challenges and opportunities. Buildings, 14(6), 1850. https://doi.org/10.3390/buildings14061850

Charef, R., Morel, J., & Rakhshan, K. (2021). Barriers to implementing the circular economy in the construction industry: a critical review. Sustainability, 13(23), 12989. https://doi.org/10.3390/su132312989

Fofou, R., Jiang, Z., Gong, Q., & Yang, Y. (2022). A decision-making model for remanufacturing facility location in underdeveloped countries: a capacitated facility location problem approach. Sustainability, 14(22), 15204. https://doi.org/10.3390/su142215204

Franz, N. and Silva, C. (2022). Waste electrical and electronic equipment (weee): global and contemporary challenge to production chains and the urban environment. Gestão & Produção, 29. https://doi.org/10.1590/1806-9649-2022v29e6621

Ambekar, S., Roy, D., Hiray, A., Prakash, A., & Patyal, V. (2021). Barriers to adoption of reverse logistics: a case of construction, real estate, infrastructure and project (crip) sectors. Engineering Construction & Architectural Management, 29(7), 2878-2902. https://doi.org/10.1108/ecam-02-2021-0112 DOI: https://doi.org/10.1108/ECAM-02-2021-0112

Chen, X., Qiu, D., & Chen, Y. (2024). Reverse logistics in the construction industry: status quo, challenges and opportunities. Buildings, 14(6), 1850. https://doi.org/10.3390/buildings14061850 DOI: https://doi.org/10.3390/buildings14061850

Charef, R., Morel, J., & Rakhshan, K. (2021). Barriers to implementing the circular economy in the construction industry: a critical review. Sustainability, 13(23), 12989. https://doi.org/10.3390/su132312989 DOI: https://doi.org/10.3390/su132312989

Fofou, R., Jiang, Z., Gong, Q., & Yang, Y. (2022). A decision-making model for remanufacturing facility location in underdeveloped countries: a capacitated facility location problem approach. Sustainability, 14(22), 15204. https://doi.org/10.3390/su142215204 DOI: https://doi.org/10.3390/su142215204

Franz, N. and Silva, C. (2022). Waste electrical and electronic equipment (weee): global and contemporary challenge to production chains and the urban environment. Gestão & Produção, 29. https://doi.org/10.1590/1806-9649-2022v29e6621 DOI: https://doi.org/10.1590/1806-9649-2022v29e6621

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Published

2025-02-28

How to Cite

Wahyuilahi, M., & Setiadi. (2025). Policy and Technological Synergies in Smart City Transportation. Sinergi International Journal of Logistics, 3(1), 43–54. https://doi.org/10.61194/sijl.v3i1.738