The research suggests that future transportation policy should prioritize the implementation of adaptive traffic control systems to reduce travel and waiting times, as well as emissions. Real-time, AI

The gap

Consensus across the literature

Supporting evidence — 4 representative gaps

• Scenario based traffic optimization in Egypt performance gains through simulation modeling (2026) · doi

  The research suggests that future transportation policy should prioritize the implementation of adaptive traffic control systems to reduce travel and waiting times, as well as emissions. Real-time, AI-driven traffic light systems should be developed to dynamically respond to traffic conditions in congested areas, thereby enabling more efficient traffic management. Egypt should also invest in innovative country initiatives, particularly in deploying ITS for traffic monitoring, predictive analytics for congestion management, and real-time rerouting via user navigation apps. These policy directions align with Egypt's urban development goals and can be tailored for similar cities facing traffic congestion challenges. Future research could expand on this work by applying the simulation framework across different intersections and integrating socio-economic variables to achieve more inclusive policy outcomes.

  Keywords: traffic policy future systems real time management egypt congestion suggests transportation prioritize implementation adaptive control
• Density based traffic monitoring system (2026) · doi

  Future work includes integrating advanced AI models for more accurate traffic prediction and real-time decision- making. The system can also be enhanced with smart city infrastructure and emergency vehicle prioritization for improved efficiency. © 2026 The Author(s). Published by IJCOPE Journal. Website: https://ijcope.org/ 24 International Journal of Creative and Open Research in Engineering and Management ISSN: 3108-1754 (Online) Volume 02 Issue 04 April-2026 | Impact Factor: 3.5 CHAPTER 8 APPENDIX 8.1 Density Based Traffic Monitoring System through IOT ✓ ✓ ✓ ✓ ✓ https://ieeexplore.ieee.org/document/8359445 https://link.springer.com/chapter/10.1007/978-3-030-03131-2_34 https://www.sciencedirect.com/science/article/pii/S1877050920309169 https://www.irjet.net/archives/V7/i5/IRJET-V7I51234.pdf https://www.researchgate.net/publication/343456789_IoT_Based_Smart_Traffic_Management_Syste CHAPTER 9 REFERNCES AND BIBLIOGRAPHY [1] S. Upadhye, S. Neelakandan, K. Thangaraj, D. V. Babu, N. Arulkumar and K. Qureshi, "Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles," in Journal of Mobile Multimedia, vol. 19, no. 2, pp. 477-496, March 2023, doi: 10.13052/jmm1550-4646.1926. [2] S. Rafiq and M. A. Khanum (2021) Review on Minimization of Ambulance Response Time Using Image Processing and Critical path Mapping Based on Traffic Control, Vol. 02, Iss. 02, S. No. 002, pp. 1 7, 2021. https://doi.org/10.54060/JIEEE/002.02.002 [3] Patel, R., Mange, S., Mulik, S. et al. AI based emergency vehicle priority system. CCF Trans. Pervasive Comp. Interact. 4, 285–297 (2022). https://doi.org/10.1007/s42486-022-00093-7 [4] KHERRAKI, Amine; EL OUAZZANI, Rajae. Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring. IAES International Journal of Artificial Intelligence (IJ-AI), [S.l.], v. 11, n. 1, p. 110-120, mar. 2022. ISSN 2252-8938. Available at: <https://ijai.iaescore.com/index.php/IJAI/article/view/21104>, doi:http://doi.org/10.11591/ijai.v11.i1.pp110- 120. [5] Sunil M, V Yashaswini Naidu, Vignesh R, Vishwas P, Amitha S, “ SMART TRAFFIC MANAGEMENT FOR AMBULANCE, ” Volume:04/Issue:12/December-2022 Impact Factor- 6.752 www.irjmets.com [6] Usaid, M., Muhammad Asif, Tabarka Rajab, Munaf Rashid, & Syeda Iqra Hassan. (2022). Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios. Sir Syed University Research Journal of Engineering & Technology, 12(1), 92–97.

  Keywords: https traffic journal time system based real smart emergency vehicle management chapter monitoring using ambulance
• Smart Transportation Systems: Enhancing Traffic Flow and Reducing Urban Congestion through Intelligent Solutions (2026) · doi

  GRJNST, Volume: 04 - Issue 3 (2026) / ISSN P: 2790-7643 Article ID: 2082 https://doi.org/10.53762/grjnst.04.03.03 G. 2082 Page 27 The study advised that urban officials and policymakers should focus on the development of Smart Transportation Systems by investing in the advanced digital infrastructure and intelligent traffic management technologies. To enhance traffic coordination in congested regions, governments ought to increase the use of real-time traffic monitoring systems and control mechanisms that can adaptively change signal timing to enhance traffic coordination. It is also suggested that models of artificial intelligence and machine learning should be integrated into traffic management systems to improve predictive abilities and help to make proactive decisions. To ensure a smooth communication between vehicles and infrastructure, as well as control systems, transportation agencies are advised to enhance IoT-based data integration systems. In addition, capacity-building programs and technical training should be provided to transportation professionals to effectively manage and operate intelligent systems. Awareness campaigns should be encouraged as well to ensure that the people adopt smart mobility solutions and to ensure that the technology-driven transportation systems are accepted by the people.

  Keywords: systems traffic transportation enhance ensure grjnst advised smart infrastructure intelligent management coordination control well people
• Smart Transportation Systems using AI and IOT (2026) · doi

  Smart Transportation system is one the best example, if we use AI and IoT in city transportation. I used different technologies for its operation, such as: React.js for front-end, Spring Boot and MySQL for back-end, and we used IoT devices. And, by using the Smart Transportation system, we are capable of managing traffic flow and increase the security. Based on my findings I am confident that smart traffic system can improve the traffic flow and for fast response in emergency situations it can be very useful and efficient in routing the vehicles. The use of smart traffic systems can contribute to the betterment of the environment. Wasting less fuel, traffic jams are avoided as the routes can be determined. K. Data Accuracy: A. Key Contributions The system was very accurate in traffic prediction, route finding, analysis and vehicles tracking using the AI Module. Data Accuracy was very high. M. Enhanced Urban Mobility: The Smart Transportation system provides ease to people in their day-to-day mobility in city, by suggesting them how to reach and when to reach. It also provides traffic and updated information regarding transport. It also provides the navigation system. The © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution. https://ijsmt.org/ , Email: [email protected] 8 International Journal of Science, Strategic Management and Technology Volume 02 Issue 05 May-2026 | ISSN: 3108-1762 (Online) | Impact Factor: 3.8 An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases Smart Transportation system can help to enhance the urban mobility. likely to be a serious barrier in developing areas or rural places. traffic analysis and real based N. Intelligent Traffic Management: The transportation systems can work efficiently when traffic signal the management

  Keywords: traffic system smart transportation mobility provides management city used using flow based vehicles systems accuracy