TECHNOLOGICAL AND PSYCHOSOCIAL DIMENSIONS OF AGGRESSIVE DRIVING AND ROAD RAGE: A PERSPECTIVE BASED ON INTELLIGENT TRANSPORTATION SYSTEMS, ARTIFICIAL INTELLIGENCE, AND SOCIETAL IMPACTS

Özgür Karaduman

doi:10.17780/ksujes.1830531

TR EN

AGRESİF SÜRÜŞ VE YOL ÖFKESİNİN TEKNOLOJİK VE PSİKOSOSYAL BOYUTLARI: AKILLI ULAŞIM SİSTEMLERİ, YAPAY ZEKÂ VE TOPLUMSAL ETKİLER TEMELLİ BİR PERSPEKTİF

Öz

Trafik güvenliği, yalnızca altyapı ve araçlarla değil, sürücü davranışı ve bunun Akıllı Ulaşım Sistemleri (ITS) içindeki teknolojik karşılığıyla şekillenen çok boyutlu bir alandır. Agresif sürüş ve yol öfkesi, güvenliği ve trafik akışını doğrudan etkileyen kritik olgulardır. Bu derleme; öfke, stres, sabırsızlık ve kültürel normlar gibi psikolojik ve toplumsal belirleyicileri; CAN-bus, biyometrik ve video verilerine dayalı yapay zekâ ve makine öğrenmesi yaklaşımlarını; ayrıca ADAS ve ITS uygulamalarını birlikte ele almaktadır. Davranışsal verilerin, durumsal farkındalığı artırarak yapay zekâ tabanlı risk değerlendirmesi ve erken uyarı mekanizmalarını nasıl desteklediği açıklanmaktadır. Ayrıca veri odaklı analiz ve sensör füzyonunun, geleneksel ve bağlantılı araç ortamlarında riskli sürüş örüntülerinin öngörülmesi ve azaltılmasındaki rolü tartışılmaktadır. Bulgular, insan faktörleri ile model tasarımının uyumunun güvenilirliği artırdığını ve gerçek zamanlı güvenlik işlevlerini güçlendirdiğini göstermektedir. Çalışma, akıllı davranış modelleme, etik veri kullanımı ve insan merkezli ITS tasarımı için pratik çıkarımlar sunmakta ve kültürlerarası analizler ile insan–otonomi etkileşimi gibi gelecekteki araştırma alanlarına işaret etmektedir.

Anahtar Kelimeler

TECHNOLOGICAL AND PSYCHOSOCIAL DIMENSIONS OF AGGRESSIVE DRIVING AND ROAD RAGE: A PERSPECTIVE BASED ON INTELLIGENT TRANSPORTATION SYSTEMS, ARTIFICIAL INTELLIGENCE, AND SOCIETAL IMPACTS

Abstract

Traffic safety is a multidimensional field shaped not only by infrastructure and vehicles but also by driver behavior and its technological mediation within Intelligent Transportation Systems (ITS). Among these behaviors, aggressive driving and road rage are critical phenomena that directly affect safety and traffic flow. This review examines psychological and societal determinants such as anger, stress, impatience, and cultural norms; Artificial Intelligence (AI) and Machine Learning (ML)-based detection and management approaches using CAN-bus, biometric, and video data; and Advanced Driver Assistance Systems (ADAS) and ITS applications. The study explains how behavioral evidence supports AI-based risk assessment, early-warning mechanisms, and ITS applications by enhancing situational awareness and adaptive response. It also discusses the growing role of data-driven analytics and sensor fusion in predicting and mitigating risky driving patterns in conventional and connected vehicle environments. Recent research shows that aligning human factors insights with model design enhances reliability and supports adaptive, real-time safety functions. The review provides practical implications for researchers and policymakers regarding intelligent behavior modeling, ethical data use, and human-centric ITS design, and highlights future research areas such as cross-cultural analyses, biometric-aware modeling, and human–autonomy interaction in next-generation mobility systems.

Keywords

References

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Details

Primary Language

English

Subjects

Active Sensing , Human-Computer Interaction , Autonomous Agents and Multiagent Systems , Transportation and Traffic

Journal Section

Review

Authors

Özgür Karaduman ^*
0000-0002-6569-3616
Türkiye

Publication Date

March 3, 2026

Submission Date

November 26, 2025

Acceptance Date

January 8, 2026

Published in Issue

Year 2026 Volume: 29 Number: 1

DOI

https://doi.org/10.17780/ksujes.1830531

IZ

https://izlik.org/JA33UL36JA

Cite

RIS / Bibtex

APA

Karaduman, Ö. (2026). TECHNOLOGICAL AND PSYCHOSOCIAL DIMENSIONS OF AGGRESSIVE DRIVING AND ROAD RAGE: A PERSPECTIVE BASED ON INTELLIGENT TRANSPORTATION SYSTEMS, ARTIFICIAL INTELLIGENCE, AND SOCIETAL IMPACTS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 533-553. https://doi.org/10.17780/ksujes.1830531