Scholarlink

<p>This paper introduces <b>ESC.AI (Enhanced Smart Commuting with Artificial Intelligence)</b>, an intelligent and integrated safety framework designed to improve health monitoring, environmental awareness, behavioral detection, driver supervision, and route optimization in school bus transportation systems. The proposed framework combines multimodal sensing, edge-based artificial intelligence, adaptive routing, and secure data management to enable proactive risk detection and real-time decision-making during transit. Although school buses remain one of the safest modes of transportation for students, recent national statistics continue to highlight persistent risks related to health emergencies, behavioral incidents, and environmental hazards. According to data from the National Safety Council (NSC) and the National Highway Traffic Safety Administration (NHTSA), school bus–related crashes resulted in 104 fatalities in the United States in 2022, representing a 3.7% decrease from 2021. Between 2013 and 2022, approximately 71% of fatalities involved occupants of other vehicles, 16% were pedestrians, and only 5% were school bus passengers. Injury statistics show a similar pattern, emphasizing the need for safety solutions that protect both students and surrounding road users. ESC.AI addresses these challenges through a unified platform that integrates Internet of Things (IoT) sensors for physiological and environmental monitoring, computer vision–based behavioral analysis, driver monitoring, and intelligent routing. Edge–cloud computing is employed to ensure low-latency responses, while blockchain-based mechanisms are used selectively to enhance data integrity, traceability, and access control for sensitive safety records. Together, these components form a cohesive and scalable framework aimed at improving transparency, responsiveness, and reliability in school transportation systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced smart commuting with artificial intelligence for intelligent health and safety monitoring in school buses

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

查看全文
  • Hanin Hossam,
  • Rofida Tamer,
  • Mariam Mohsen,
  • Omnia Ahmed,
  • Mennatallah Alaa,
  • Joumana Mourad,
  • Rana Adel,
  • Amira Hatem,
  • Sameh Sherif

摘要

This paper introduces ESC.AI (Enhanced Smart Commuting with Artificial Intelligence), an intelligent and integrated safety framework designed to improve health monitoring, environmental awareness, behavioral detection, driver supervision, and route optimization in school bus transportation systems. The proposed framework combines multimodal sensing, edge-based artificial intelligence, adaptive routing, and secure data management to enable proactive risk detection and real-time decision-making during transit. Although school buses remain one of the safest modes of transportation for students, recent national statistics continue to highlight persistent risks related to health emergencies, behavioral incidents, and environmental hazards. According to data from the National Safety Council (NSC) and the National Highway Traffic Safety Administration (NHTSA), school bus–related crashes resulted in 104 fatalities in the United States in 2022, representing a 3.7% decrease from 2021. Between 2013 and 2022, approximately 71% of fatalities involved occupants of other vehicles, 16% were pedestrians, and only 5% were school bus passengers. Injury statistics show a similar pattern, emphasizing the need for safety solutions that protect both students and surrounding road users. ESC.AI addresses these challenges through a unified platform that integrates Internet of Things (IoT) sensors for physiological and environmental monitoring, computer vision–based behavioral analysis, driver monitoring, and intelligent routing. Edge–cloud computing is employed to ensure low-latency responses, while blockchain-based mechanisms are used selectively to enhance data integrity, traceability, and access control for sensitive safety records. Together, these components form a cohesive and scalable framework aimed at improving transparency, responsiveness, and reliability in school transportation systems.