International Journal of Transportation Engineering and Traffic System
Volume 11, Issue 1 (2025)
Table of contents
-
Hydraulic Traffic Reduction Mechanism
Abstract: AbstractUrbanization and the surge in vehicle ownership have led to escalating traffic congestion, particularlyin densely populated metropolitan regions. Conventional traffic management systems, which depend onfixed lane structures and pre-set signal timings, often struggle to accommodate the increasing andunpredictable nature of traffic flow. This challenge necessitates the development of adaptive andintelligent traffic management solutions that can dynamically adjust to real-time conditions. A novelapproach to mitigating traffic congestion is the Hydraulic Traffic …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 11, Issue 1, 2025 · pp. 18–23 Read article
-
Real-Time Traffic Management System Utilizing Machine Learning Techniques
Abstract: AbstractThe rapid increase in the number of vehicles on roads has created significant challenges forconventional traffic management systems, which struggle to efficiently regulate traffic flow undervarying conditions. Traditional traffic signals operate on fixed timing cycles, failing to adapt tofluctuating vehicle densities across different lanes. This inefficiency results in unnecessary delays,increased congestion, longer travel times, and excessive fuel consumption. Additionally, fixed-timingsignals do not account for dynamic traffic patterns, leading to bottlenecks …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 11, Issue 1, 2025 · pp. 24–31 Read article
-
AI-Powered Traffic Management: Enhancing Urban Mobility and Efficiency
Abstract: AbstractUrban traffic congestion is a persistent challenge, resulting in economic setbacks, extended traveldurations, excessive fuel consumption, and environmental harm. The rapid urbanization and increasingnumber of vehicles have intensified this issue, highlighting the need for smarter and more efficienttraffic management solutions. With advancements in artificial intelligence (AI), innovative trafficcontrol strategies can be implemented to optimize urban mobility and alleviate congestion. This paperexplores AI-driven traffic management solutions, focusing on machine learning-based adaptive …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 11, Issue 1, 2025 · pp. 32–36 Read article
-
Leveraging Artificial Intelligence for Traffic Flow Optimization: A Case Study Approach
Abstract: AbstractTraffic congestion poses a significant challenge in urban environments, contributing to economiclosses, increased fuel consumption, environmental pollution, and diminished quality of life. Traditionaltraffic management approaches, such as pre-timed traffic signals and manual interventions, often failto adapt dynamically to fluctuating traffic conditions. This study explores the integration of artificialintelligence (AI) in traffic management to optimize traffic flow and reduce congestion. By leveragingAI-driven predictive models, deep reinforcement learning, and real-time traffic data, …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 11, Issue 1, 2025 · pp. 37–42 Read article