Passable: An Intelligent Traffic Light System with Integrated Incident Detection and Vehicle Alerting

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection.

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Publication Details

Journal
Sensors
Published
2025-09-16
DOI
https://doi.org/10.3390/s25185760
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Passable: An Intelligent Traffic Light System with Integrated Incident Detection and Vehicle Alerting

Amal Babour, Ohoud Alzamzami, Rawan Alghamdi, Lama Al Khuzayem et al.
Sensors
Traffic Prediction and Management Techniques
article

Passable: An Intelligent Traffic Light System with Integrated Incident Detection and Vehicle Alerting

Amal Babour, Ohoud Alzamzami, Rawan Alghamdi, Lama Al Khuzayem, Zainab Alsaggaf, Reema AlMalki
article en

Abstract

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection.

SensorsVol. 25(18)
King Abdulaziz University (SA)
Openalex Percentile: Top 73%
Traffic Prediction and Management Techniques
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Passable: An Intelligent Traffic Light System with Integrated Incident Detection and Vehicle Alerting — Amal Babour, Ohoud Alzamzami, et al. · Sensors (2025) | TGRS Research Map | TGRS