Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
Source abstract
Abstract
Traffic congestion represents an urban challenge that authorities are trying to solve through various means. Current traffic management systems do not solve these challenges, which is why the research presents a new proposal for a traffic optimization system. The proposed solution integrates small-sized equipment (ESP32 equipped with accelerometers, gyroscopes, and cameras), cloud-based AI services (Azure Content Safety), and a multi-parametric analytical framework for real-time navigation. The system uses the Traffic Optimization Algorithm (TOA) proposed by the authors to calculate the Global Route Quality Indicator (GRQIk). It associates each route with a value based on which the degree of optimality is estimated. GRQIk is calculated based on the distance traveled, traffic delays, estimated travel time, road safety, and the individual’s sensitivity. Real-time data are collected using ESP32, with a pothole detection threshold set at 0.8 rad/s. Through the TomTom API, four alternative routes are identified. The performance evaluation showed that GRQIk differentiates route quality, with scores ranging from 26.40% for optimal routes to 100% for the least favorable ones. In addition, Azure’s Content Safety API achieved 100% accuracy in identifying violent incidents and accidents. The limitations of the research concern the small number of images available to test the Content Safety service. The research establishes new approaches for future developments in the field of smart transportation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Data Management and Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Automatic Control, Computers, and Electronics, Faculty of Mechanical and Electrical Engineering, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania
Department of Business Administration, Faculty of Economic Sciences, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania
Funding
Petroleum-Gas University of Ploiesti, Romania
References (69)
Show source-provided references
1. Rosca, C.-M., Rădulescu, G., and Stancu, A. (2025). Artificial Intelligence of Things Infrastructure for Quality Control in Cast Manufacturing Environments Shedding Light on Industry Changes. Appl. Sci., 15.10.3390/app15042068 ↗
2. Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives
3. Fouladgar, M., Parchami, M., Elmasri, R., and Ghaderi, A. (2017, January 14–19). Scalable deep traffic flow neural networks for urban traffic congestion prediction. Proceedings of the International Joint Conference on Neural Networks, Anchorage, AK, USA.10.1109/IJCNN.2017.7966128 ↗
4. Anjaneyulu, M., and Kubendiran, M. (2022). Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique. Sustainability, 15.10.3390/su15010074 ↗
5. Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., and Altowaijri, S.M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19.10.3390/s19092206 ↗
6. Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies10.1155/2022/4687319 ↗
7. Afrin, T., and Yodo, N. (2020). A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System. Sustainability, 12.10.3390/su12114660 ↗
8. Integration of Public Transportation in Smart Transportation System (Smart Transportation System) in Jakarta | Konfrontasi: Jurnal Kultural, Ekonomi dan Perubahan Sosial10.33258/konfrontasi2.v9i2.223 ↗
9. Multimodal Transport For Smart Mobility in Emerging Cities: Case of Doha10.29117/cic.2023.0120 ↗
10. Yang, X.-S. (2025). Convergence Catalysts: Exploring the Fusion of Embedded Systems, IoT, and Artificial Intelligence. Engineering Applications of AI and Swarm Intelligence, Springer Nature.10.1007/978-981-97-5979-8 ↗
11. Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm
12. Conceptual Design of Smart Network Adaptive Traffic Light in Creating Low-Carbon City10.30880/ijie.2023.15.06.011 ↗
13. Liu, L., Lian, M., Lu, C., Zhang, S., Liu, R., and Xiong, N.N. (2022). TCSA: A Traffic Congestion Situation Assessment Scheme Based on Multi-Index Fuzzy Comprehensive Evaluation in 5G-IoV. Electronics, 11.10.3390/electronics11071032 ↗
14. Intelligent Traffic Congestion Control Using Black Widow Optimization with Hybrid Deep Learning on Smart City Environment10.2478/jsiot-2023-0010 ↗
15. Impact factors to reduce carbon emissions. Evidences from Latin America10.1108/K-05-2022-0712 ↗
16. A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network
17. Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion10.1155/2019/2915369 ↗
18. Dynamic optimal route search algorithm for car navigation systems with preferences by dynamic programming10.1002/tee.20601 ↗
19. Multi-agent deep reinforcement learning for large-scale traffic signal control10.1109/TITS.2019.2901791 ↗
20. Gregurić, M., Vujić, M., Alexopoulos, C., and Miletić, M. (2020). Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data. Appl. Sci., 10.10.3390/app10114011 ↗
21. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning
22. Wang, S., Xie, X., Huang, K., Zeng, J., and Cai, Z. (2019). Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data. Entropy, 21.10.3390/e21080744 ↗
23. Rosca, C.-M., and Stancu, A. (2024). Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0. Appl. Sci., 14.10.3390/app142310835 ↗
24. Machine Learning Approach to Short-Term Traffic Congestion Prediction in a Connected Environment10.1177/0361198118795010 ↗
25. Application of Autoformer to Short-Term Traffic Flow Prediction
26. Cooperative deep Q-learning with Q-value transfer for multi-intersection signal control10.1109/ACCESS.2019.2907618 ↗
27. Collaborative Traffic Signal Automation Using Deep Q-Learning10.1109/ACCESS.2023.3331317 ↗
28. Evaluating Reinforcement Learning State Representations for Adaptive Traffic Signal Control10.5383/JTTM.01.01.003 ↗
29. Traffic Signal Control System Using Deep Reinforcement Learning With Emphasis on Reinforcing Successful Experiences10.1109/ACCESS.2022.3225431 ↗
30. A Review of Deep Reinforcement Learning for Traffic Signal Control
31. Rosca, C.-M., and Stancu, A. (2024). Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis. Appl. Sci., 14.10.3390/app142210169 ↗
32. Traffic Flow Breakdown Prediction using Machine Learning Approaches10.1177/0361198120934480 ↗
33. A Comprehensive Analysis of Reward Function for Adaptive Traffic Signal Control10.17977/um018v4i22021p85-96 ↗
34. Schumacher, M., Adriano, C.M., and Giese, H. (2023, January 2–4). Challenges in Reward Design for Reinforcement Learning-based Traffic Signal Control: An Investigation using a CO2 Emission Objective. Proceedings of the SUMO Conference, Berlin, Germany.10.52825/scp.v4i.222 ↗
35. Bouktif, S., Cheniki, A., and Ouni, A. (2021). Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning. Sensors, 21.10.3390/s21072302 ↗
36. Cao, K., Wang, L., Zhang, S., Duan, L., Jiang, G., Sfarra, S., Zhang, H., and Jung, H. (2024). Optimization Control of Adaptive Traffic Signal with Deep Reinforcement Learning. Electronics, 13.10.3390/electronics13010198 ↗
37. New Algorithm to Prevent Online Test Fraud Based on Cognitive Services and Input Devices Events
38. Deep Reinforcement Learning Aided Packet-Routing for Aeronautical Ad-Hoc Networks Formed by Passenger Planes10.1109/TVT.2021.3074015 ↗
39. The energy transition in Europe—A solution for net zero carbon?10.1007/s11356-022-20730-z ↗
40. Zheng, X., Huang, W., Wang, S., Zhang, J., and Zhang, H. (2022). Research on Energy-Saving Routing Technology Based on Deep Reinforcement Learning. Electronics, 11.10.3390/electronics11132035 ↗
41. Zhuang, C.X., and Chen, C. (2022). Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm. Front. Neurorobotics, 16.10.3389/fnbot.2022.1049343 ↗
42. A Deep Reinforcement Learning Approach for Global Routing10.1115/1.4045044 ↗
43. Chrpa, L., Vallati, M., and Parkinson, S. (2019, January 8–9). Exploiting automated planning for efficient centralized vehicle routing and mitigating congestion in urban road networks. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, Cyprus.10.1145/3297280.3297539 ↗
44. Švadlenka, M., Chrpa, L., and Vallati, M. (2023, January 14–16). Improving the Scalability of Automated Planning-based Vehicle Routing via Smart Routes Identification. Proceedings of the 8th International Conference on Models and Technologies for Intelligent Transportation Systems, Nice, France.10.1109/MT-ITS56129.2023.10241639 ↗
45. A hybrid path planning method for an unmanned cruise ship in water quality sampling10.1109/ACCESS.2019.2925894 ↗
46. Gao, Z., Zhang, J., Yan, S., Xiao, Y., Simeonidou, D., and Ji, Y. (2019, January 3–7). Deep Reinforcement Learning for BBU Placement and Routing in C-RAN. Proceedings of the Optical Fiber Communication Conference, San Diego, CA, USA.10.1364/OFC.2019.W2A.22 ↗
47. A Deep Learning-Based Routing Approach for Wireless Mesh Backbone Networks10.1109/ACCESS.2023.3277431 ↗
48. Rosca, C.-M., Stancu, A., Neculaiu, C.-F., and Gortoescu, I.-A. (2024). Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities. Appl. Sci., 14.10.3390/app14198861 ↗
49. Ali, A., Ayub, N., Shiraz, M., Ullah, N., Gani, A., and Qureshi, M.A. (2021). Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey. Sustainability, 13.10.3390/su132313068 ↗
50. Silas, A.I., Jonathan, F.M., and Ekong, O.E. (2021, January 12–14). Traffic Management System for Prioritizing Emergency Over Time and Density. Proceedings of the Accra Bespoke Multidisciplinary Innovations Conference, Accra, Ghana.
51. Real-time vehicle counting in complex scene for traffic flow estimation using multi-level convolutional neural network
52. Qi, L., Wang, F., Xu, X., Dou, W., Zhang, X., Khosravi, M.R., and Zhou, X. (2022). Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation. Proceedings of the 11th International Conference on Computer Engineering and Networks, Lecture Notes in Electrical Engineering, Hechi, China, Springer.10.1007/978-981-16-6554-7_78 ↗
53. Rosca, C.-M., Stancu, A., and Iovanovici, E.M. (2025). The New Paradigm of Deepfake Detection at the Text Level. Appl. Sci., 15.10.3390/app15052560 ↗
54. Open Urban mmWave Radar and Camera Vehicle Classification Dataset for Traffic Monitoring10.1109/ACCESS.2024.3397013 ↗
55. Traffic monitoring using an adaptive sensor power scheduling algorithm10.1007/s42452-019-1494-0 ↗
56. Mihaila, M., Blanari, I., Goanta, V., and Barsanescu, P.D. (2022, January 9–10). Finite element analysis of a modified weigh-in-motion sensor. Proceedings of the 10th International Conference on Advanced Concepts in Mechanical Engineering, Iasi, Romania.10.1088/1757-899X/1262/1/012052 ↗
57. Ma, R., Zhang, Z., Dong, Y., and Pan, Y. (2020). Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck. Sensors, 20.10.3390/s20185051 ↗
58. Roșca, C.-M., and Cărbureanu, M. A Comparative Analysis of Sorting Algorithms for Large-Scale Data: Performance Metrics and Language Efficiency. Proceedings of the Emerging Trends and Technologies on Intelligent Systems. ETTIS 2024. Lecture Notes in Networks and Systems, Noida, India.10.1007/978-981-97-5703-9_8 ↗
59. Analyzing differences of highway lane-changing behavior using vehicle trajectory data10.1016/j.physa.2023.128980 ↗
60. Ship visual trajectory exploitation via an ensemble instance segmentation framework10.1016/j.oceaneng.2024.119368 ↗
61. Design and evaluation of a new intersection model to minimize congestions using VISSIM software10.1515/eng-2020-0019 ↗
62. A Study of Roundabout Sustainability using Traffic Simulation -A Case Study at Ayer Hitam Signalised Intersection
63. A Cyber-Physical System Modelling Framework for an Intelligent Urban Traffic System
64. Grigoropoulos, G., Hosseini, S.A., Keler, A., Kaths, H., Spangler, M., Busch, F., and Bogenberger, K. (2021). Traffic Simulation Analysis of Bicycle Highways in Urban Areas. Sustainability, 13.10.3390/su13031016 ↗
65. Švadlenka, M., and Chrpa, L. (2023, January 14–17). Towards a Framework for Intelligent Urban Traffic Routing. Proceedings of the 36th International FLAIRS Conference, Clearwater Beach, FL, USA.10.32473/flairs.36.133321 ↗
66. Mostafi, S., Alghamdi, T., and Elgazzar, K. (2022, January 18–23). RegTraffic: A Regression based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization. Proceedings of the International Joint Conference on Neural Networks, Padua, Italy.10.1109/IJCNN55064.2022.9892185 ↗
67. TomTom (2025, February 20). Calculate Route, Routing API, TomTom Developer Portal. Available online: https://developer.tomtom.com/routing-api/documentation/tomtom-maps/calculate-route.
68. TomTom (2025, February 20). API Request for Routing. Available online: https://api.tomtom.com/routing/1/calculateRoute/{waypointsParam}/json?key={apiKey}&traffic=true&maxAlternatives=3.
69. Rosca, C.-M., Stancu, A., and Tănase, M.R. (2025). A Comparative Study of Azure Custom Vision Versus Google Vision API Integrated into AI Custom Models Using Object Classification for Residential Waste. Appl. Sci., 15.10.3390/app15073869 ↗