Enhancing Transportation through Vehicle Infrastructure Interaction

Abstract

The rapid evolution of intelligent transportation systems has highlighted the importance of seamless communication between vehicles and roadway infrastructure. Enhancing transportation through vehicle–infrastructure interaction focuses on enabling real-time data exchange to improve safety, efficiency, and mobility within urban and highway networks. By integrating advanced communication technologies such as Vehicle-to-Infrastructure (V2I) protocols, traffic flow can be optimized, accident risks reduced, and environmental impact minimized through smarter routing and energy-efficient driving. This approach supports proactive traffic management, automated decision-making, and better coordination between connected vehicles and smart infrastructure. The study emphasizes the role of emerging technologies including 5G, IoT, and edge computing in achieving scalable, secure, and reliable interaction frameworks. Ultimately. vehicle–infrastructure collaboration lays the foundation for sustainable and intelligent transportation ecosystems, paving the way toward fully autonomous mobility solutions.

Introduction

Modern transportation is evolving through digital technologies and intelligent communication, moving beyond traditional, human-driven road networks. Vehicle-to-Infrastructure (V2I) communication enables continuous data exchange between vehicles and roadway elements (e.g., traffic lights, sensors), improving real-time traffic updates, adaptive management, and safety. Emerging technologies like 5G, IoT, AI, and edge computing enhance system reliability, scalability, and support smart city goals.

Literature Review:
Research on V2I has progressed from early trials to integrated frameworks combining radio technologies (IEEE 802.11p, LTE-V2X, 5G NR-V2X) and edge/cloud computing. Cellular 5G and newer standards generally outperform older protocols in reliability and latency but require careful spectrum and interference management. Edge computing reduces latency and offloads central servers but poses challenges in resource management. Standards and regulatory alignment (C-ITS) are critical for interoperability and successful deployment. Impact assessments show V2I benefits traffic flow, safety, and emissions.

Methodology:
The study designs a V2I framework by:

• Identifying problems in current systems (congestion, delays).

• Developing a system architecture with On-Board Units (OBUs) in vehicles and Roadside Units (RSUs).

• Designing communication frameworks using protocols like 5G and IEEE 802.11p.

• Collecting data via IoT sensors and processing it locally using edge computing.

• Integrating intelligent services with machine learning for traffic prediction, route optimization, and accident prevention.

Evaluation & Results:
Simulations using traffic and network simulators tested the framework against traditional systems under varied traffic conditions.

• 5G-V2X achieved very low latency (<20 ms), outperforming older protocols.

• Packet delivery reliability exceeded 95%.

• Adaptive traffic signals reduced intersection waiting times by ~30%.

• Hazard alerts lowered rear-end collision risk by over 40%.

• Fuel consumption dropped by 10–15% due to optimized traffic flow, benefiting sustainability.

Conclusion

The study on Enhancing Transportation through Vehicle–Infrastructure Interaction demonstrates the transformative potential of integrating connected vehicle technologies with intelligent roadway systems. By enabling seamless, real-time communication between vehicles and infrastructure, the framework addresses critical challenges such as congestion, road safety, and environmental sustainability. The evaluation highlighted that V2I interaction significantly improves traffic efficiency by optimizing signal control, reducing waiting times, and supporting dynamic routing. It also enhances safety through timely hazard alerts and cooperative perception, reducing the likelihood of collisions. Furthermore, the system contributes to energy conservation and emission reduction by minimizing unnecessary idling and promoting smoother traffic flow. Another key finding is the advantage of leveraging emerging technologies such as 5G, IoT, and edge computing, which collectively provide low-latency, high-reliability communication essential for safety-critical applications. While simulation and prototype testing confirm the feasibility of the approach, large-scale deployment requires careful consideration of interoperability, security, and data privacy to ensure widespread adoption. In conclusion, vehicle–infrastructure interaction offers a robust foundation for building next-generation intelligent transportation systems. It not only enhances the current mobility ecosystem but also paves the way for the integration of autonomous vehicles and smart city infrastructure, ultimately contributing to safer, greener, and more efficient urban mobility.

References

[1] Al-Sultan, S., Al-Doori, M. M., Al- Bayatti, A. H., & Zedan, H. (2014). A comprehensive survey on vehicular ad hoc networks. Journal of Network and Computer Applications, 37, 380–392. [2] Campolo, C., Molinaro, A., Araniti, G., & Berthet, A. O. (2017). 5G network slicing for vehicle-to- everything services. IEEE Wireless Communications, 24(6), 38–45. [3] Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., & Weil, T. (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys & Tutorials, 13(4), 584–616. [4] Molina-Masegosa, R., & Gozalvez, J. (2017). LTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to- everything communications. IEEE Vehicular Technology Magazine, 12(4), 30–39. [5] Papadimitratos, P., La Fortelle, A., Evenssen, K., Brignolo, R., & Cosenza, S. (2009). Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation. IEEE Communications Magazine, 47(11), 84–95. [6] Sahin, T., & Ghosh, M. (2021). Edge computing for intelligent transportation systems: A review. IEEE Transactions on Intelligent Transportation Systems, 22(8), 4971–4987.

Copyright

Copyright © 2025 NikhilKumar B, Dr. SDN Hayath Ali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.