ARTIFICIAL INTELLIGENCE APPLICATIONS FOR ENHANCING SAFETY IN P-HAILING SERVICES: A SYSTEMATIC LITERATURE REVIEW
DOI:
https://doi.org/10.35631/AIJBES.829002Keywords:
AI, Gig Economy, P-Hailing, SafetyAbstract
The rapid growth of p-hailing services has transformed urban transportation and last-mile delivery operations, created new economic opportunities while simultaneously raising concerns regarding rider safety, traffic risks, and operational sustainability. As p-hailing riders are frequently exposed to road hazards, demanding work conditions, and dynamic traffic environments, there is an increasing need to explore how artificial intelligence (AI) can enhance safety within this sector. However, existing studies on AI applications in transportation safety remain fragmented across different disciplines and technological domains, limiting a comprehensive understanding of their contributions to p-hailing safety. Therefore, this study aims to systematically review the current literature on AI applications for enhancing safety in p-hailing services. A Systematic Literature Review (SLR) approach was adopted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Advanced search strategies were conducted in the Scopus and Web of Science (WoS) databases using combinations of keywords related to "gig economy", "p-hailing", "safety", and "artificial intelligence". The review focused on peer-reviewed journal articles published between 2020 and 2026. Following the identification, screening, eligibility, and inclusion processes, a total of 45 articles were selected as the final dataset for analysis. The findings revealed four major themes: (1) AI-Driven Road Safety, Driver Behaviour, and Human Risk Detection; (2) AI, Machine Learning, and Digital Technologies for Transport Infrastructure Resilience; (3) Intelligent Transport Systems, Traffic Estimation, and Mobility Simulation; and (4) Shared Mobility, Equity, Sustainability, and Transport Governance. The review highlights that AI technologies are increasingly employed to support risk detection, behavioural monitoring, predictive safety assessment, traffic management, infrastructure resilience, and sustainable mobility governance. Overall, the findings demonstrate the significant potential of AI to improve safety outcomes in p-hailing services while identifying important research directions for policymakers, platform operators, and future researchers seeking to develop safer and more resilient mobility ecosystems.
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