Article Sidebar

Download
PDF
Statistic
Read Counter : 0 Download : 0

Main Article Content

Abstract

The carpooling system is an automated system that eases travelers’ misery and helps them find cars quickly. One application that will be turbocharged is carpooling, where solo drivers to work can ask other passengers in our application for a ride. It provides the car user with an easy-to-use platform between the car owner and the car user. The existing carpooling schemes aim to reduce carbon footprint and environmental impact by matching passengers and drivers along the way. However, they are generally severely deficient in features, including inefficient static route planning, low-quality ride-matching algorithms, and a lack of high-quality user trust mechanisms. Our proposed system can resolve these problems by operating algorithmically (e.g., using A-Star, which dynamically adjusts to optimize routes based on the real environment) and by leveraging advanced machine learning models, such as clustering and recommendation systems, to improve the precision of ride matching. We also enhance the trust feature by providing comprehensive profiles of drivers with verified contact details, vehicle condition reports, user ratings, and reviews, thereby offering an efficient, dependable, and secure carpooling experience.

Keywords

Carpooling Intelligent transportation Carpooling efficiency Environmental impact User trust mechanisms

Article Details

How to Cite
Madhuri Vikas Mane, Kumar, D., & Kamal Agarwal. (2026). Enhancing operational and infrastructure integration in shared mobility. Future Technology, 5(3), 118–127. Retrieved from https://fupubco.com/futech/article/view/784
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. Abutaleb, M., Kiani, M., & Shahriari, S. (2020). Feasibility of carpooling in emerging markets: Barriers and opportunities. Sustainable Cities and Society, 55, 102034. https://doi.org/10.1016/j.scs.2020.102034
  2. Almeida, R., & Silva, D. (2020). Enhancing carpooling efficiency: A multi-agent system approach. Computers, Environment and Urban Systems, 78, 101377. https://doi.org/10.1016/j.compenvurbsys.2019.101377
  3. Anthopoulos, L. G., &Tzimos, D. N. (2021). Carpooling platforms as smart city projects: A bibliometric analysis and systematic literature review. Sustainability, 13(19), 10680. https://doi.org/10.3390/su131910680
  4. Bachmann, J., Smith, M., & Johnson, T. (2018). Psychological and sociological factors influencing the adoption of ride-sharing services. Transport Policy, 69, 30–39. https://doi.org/10.1016/j.tranpol.2018.06.003
  5. Banister, D. (2020). The role of carpooling in sustainable urban mobility. Urban Studies, 57(13), 2736–2753. https://doi.org/10.1177/0042098020907646
  6. Beed, R. S., Sarkar, S., Roy, A., Biswas, S. D., & Biswas, S. (2020). A hybrid multi-objective carpool route optimization technique using genetic algorithm and A* algorithm. arXiv Preprint. https://arxiv.org/abs/2007.05781
  7. Castells, M., & Hall, P. (2020). The geography of carpooling: A global perspective. Urban Studies, 57(2), 487–502. https://doi.org/10.1177/0042098020915497
  8. Ciasullo, M. V., Mazzei, A., & D’Angelo, G. (2018). Sustainable commuting and carpooling: A critical review. Sustainability, 10(10), 3461. https://doi.org/10.3390/su10103461
  9. Diab, E., &Elhenawy, M. (2019). An evaluation of smart carpooling platforms: A case study of Cairo, Egypt. International Journal of Sustainable Transportation, 13(4), 276–287. https://doi.org/10.1080/15568318.2018.1539759
  10. Do, T. N., & Jung, J. H. (2018). Socioeconomic benefits of colleague-based carpooling. Transportation Research Part D: Transport and Environment, 61, 123–136. https://doi.org/10.1016/j.trd.2018.04.013
  11. Fox, T., & Gaffney, L. (2020). Smart carpooling: An overview of technology integration for sustainable urban transport. Journal of Transport Geography, 83, 102657. https://doi.org/10.1016/j.jtrangeo.2020.102657
  12. Fregnan, M., & Teixeira, E. (2017). Analysis of the economic benefits of shared mobility in urban areas. International Journal of Transport Economics, 44(1), 91–103. https://doi.org/10.19272/201701401009
  13. Greenberg, A., & Polak, J. (2021). Carpooling as an alternative to traditional commuting: A behavioral approach. Transportation Research Part F: Traffic Psychology and Behaviour, 81, 115–127. https://doi.org/10.1016/j.trf.2021.05.010
  14. Gurumurthy, R., Sinha, R., & Ramachandran, S. (2019). Optimization models for urban carpooling: A comparison of methods. Transportation Research Part C: Emerging Technologies, 110, 147–163. https://doi.org/10.1016/j.trc.2019.01.011
  15. Haliem, M., Mani, G., Aggarwal, V., & Bhargava, B. (2020). A distributed model-free ride-sharing approach for joint matching, pricing, and dispatching using deep reinforcement learning. arXiv Preprint. https://arxiv.org/abs/2010.01755
  16. Haroon, W., Khan, M. A., Ilyas, Z., Almujibah, H. R., Zubair, M. U., Ashfaq, M., & Hamza, M. (2024). Analyzing young adult travelers’ perception and impacts of carpooling on traffic sustainability. Sustainability, 16(14), 6098. https://doi.org/10.3390/su16146098
  17. Hill, J., & Lin, H. (2021). The economic and environmental impact of carpooling in dense urban environments. Environmental Economics and Policy Studies, 23(5), 775–790. https://doi.org/10.1007/s10018-021-00322-x
  18. Kang, W., Wang, Q., Cheng, L., & Ning, M. (2024). Examining commuters’ intention to use app-based carpooling: Insights from the Technology Acceptance Model. Sustainability, 16(14), 5894. https://doi.org/10.3390/su16145894
  19. Kuncoro, A., & Ramli, S. (2020). Carpooling system design: A user-centered approach for optimizing mobility in smart cities. Smart Cities, 3(1), 10–25. https://doi.org/10.3390/smartcities3010002
  20. Liu, X., Yan, X., Liu, F., Wang, R., & Leng, Y. (2019). A trip-specific model for fuel saving estimation and subsidy policy making of carpooling based on empirical data. Transportation Research Part D: Transport and Environment, 70, 193–207. https://doi.org/10.1016/j.trd.2019.03.009
  21. Lopez, A., & Sánchez, M. (2021). Investigating the sustainability of carpooling in urban areas. Journal of Urban Planning and Development, 147(2), 03121005. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000618
  22. Mallus, M., Xie, X., & Cheng, W. (2017). Economic impact and social benefits of carpooling: A review of current literature and future directions. Transportation Research Part A: Policy and Practice, 96, 76–90. https://doi.org/10.1016/j.tra.2017.01.001
  23. Mitropoulos, L., Kortsari, A., &Ayfantopoulou, G. (2021). Factors affecting drivers to participate in a carpooling to public transport service. Sustainability, 13(9), 9129. https://doi.org/10.3390/su13091229
  24. Minton, E. A., & Wei, H. (2020). Environmental perceptions and sustainable carpooling. Journal of Sustainable Mobility, 10(2), 39–50. https://doi.org/10.1080/20528356.2020.1735021
  25. Molina, G., Sharma, S., & Lisi, L. (2020). Big data and its role in predicting carpooling adoption. Transportation Research Part B: Methodological, 137, 1–14. https://doi.org/10.1016/j.trb.2020.03.008
  26. Moraga, G., & Chouinard, H. (2021). A survey on the adoption and barriers of carpooling services. Transport Reviews, 41(2), 222–245. https://doi.org/10.1080/01441647.2020.1847539
  27. Papoutsis, P., Fennia, S., Bridon, C., & Duong, T. (2021). Relaxing door-to-door matching reduces passenger waiting times: A workflow for the analysis of driver GPS traces in a stochastic carpooling service. arXiv Preprint. https://arxiv.org/abs/2102.06381
  28. Rodriguez, J., & Castro, M. (2021). Cost-benefit analysis of carpooling incentives in urban settings. Journal of Transport Economics and Policy, 55(3), 276–294. https://doi.org/10.1080/07362220.2021.1911272
  29. Tafreshian, M., Shakouri, M., & Ghaffari, A. (2020). Peer-to-peer ride-sharing platforms: Design, implementation, and future challenges. Transportation Research Part C: Emerging Technologies, 119, 102739. https://doi.org/10.1016/j.trc.2020.102739
  30. Vlahos, K., &Korinthios, S. (2018). Leveraging blockchain for carpooling: Opportunities and challenges. Computers, Environment and Urban Systems, 70, 38–45. https://doi.org/10.1016/j.compenvurbsys.2018.02.002
  31. Wang, X., & Hu, P. (2021). The impact of shared mobility systems on urban traffic flow. Journal of Transportation Engineering, Part B: Pavements, 147(3), 05021012. https://doi.org/10.1061/JTEPBS.0000466
  32. Yamada, M., & Nakamura, S. (2020). The role of artificial intelligence in optimizing carpool routes. AI and Transportation, 5(3), 100–112. https://doi.org/10.1016/j.aiat.2020.05.001
  33. Zhan, X., & Ma, W. (2021). Blockchain-based carpooling: A case study of Shanghai. Sustainability, 13(5), 1225. https://doi.org/10.3390/su13051225
  34. Zhang, J., & Zuo, Z. (2022). A review of carpooling: Theory, methods, and applications. Sustainable Cities and Society, 69, 102855. https://doi.org/10.1016/j.scs.2021.102855
  35. Zhang, Z., & Liu, L. (2021). Application of machine learning algorithms to optimize carpool matching. Journal of Intelligent Transportation Systems, 25(4), 421–434. https://doi.org/10.1080/15472450.2021.1955829