Evidence map›Paper›PMID 41864994›Full record

ArticleScientific reports2026

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

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

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hanin HossamSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Rofida TamerSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Mariam MohsenSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Omnia AhmedSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Mennatallah AlaaSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Joumana MouradSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Rana AdelSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Amira HatemSchool of Computer Science and Information Technology, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Sameh SherifSchool of Electronics, Communication and Computer Engineering, Egypt-Japan University of Science and Technology (E-JUST), New Borg El Arab, Egypt. sameh.sherif@aucegypt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Accidents, TrafficArtificial IntelligenceMotor VehiclesSafetyTransportationHumansIntelligent SystemsSchoolsUnited States

Identifiers

PMID41864994
PMCPMC13009530

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.