The Role of Artificial Intelligence in Enhancing Urban Traffic Crisis Management

Urban transportation systems are increasingly vulnerable to crises and natural disasters, necessitating the development of intelligent and resilient management strategies. This paper investigates the transformative role of Artificial Intelligence (AI) and its subfields in enhancing traffic management during emergency scenarios. Drawing on existing research, we synthesize key methodologies, including the application of Deep Learning models for real-time traffic flow prediction [۲, ۹] and Reinforcement Learning for dynamic traffic signal control in crisis situations [۵]. Furthermore, the integration of IoT and Big Data is shown to be crucial for real-time surveillance and predictive analytics, enabling proactive incident detection and resource allocation [۶, ۸, ۱۳]. The paper specifically highlights the use of AI-driven emergency route planning in response to urban challenges like flooding, with a particular focus on a case study of Bangkok [۴, ۱۵]. We also examine the role of Computer Vision in post-disaster damage assessment [۱۲]. The synthesized findings demonstrate that AI and its data-driven approaches are essential for building robust urban resilience, transforming how cities respond to and recover from crises [۱۰, ۱۴]. This review provides a comprehensive overview of how these technologies can lead to safer and more efficient metropolitan environments [۱, ۷, ۱۱].

کلیدواژه ها:

Artificial Intelligence (AI) ، Traffic Management ، Crisis Management ، Urban Resilience ، Deep Learning ، Smart Cities