International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
AI-Driven Adaptive Lane Control Priyanka Balkrishna Sapte1 ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper delivers a thorough analysis of AI-
understanding of dynamic traffic strategies and inform future research and practical applications for better urban mobility.
driven dynamic lane management systems, with an emphasis on Dynamic Lane Reversal (DLR) and Dynamic Lane Assignment (DLA). It integrates insights from various academic studies to assess the strengths and limitations of these approaches in improving traffic flow and road safety. The paper first underscores the relevance of dynamic lane strategies in addressing urban traffic issues and enhancing transport systems. It then explores different methodologies used in prior studies, ranging from traditional rule-based frameworks to advanced machine learning and deep learning models. Through systematic evaluation of empirical evidence and theoretical insights, the paper presents key outcomes related to lane reversal and usage control methods. It also examines the impact of these techniques on throughput, travel time, pollution, and driver experience. The findings offer guidance for urban planners, engineers, and policy developers, while also identifying research opportunities and calling for multidisciplinary efforts and practical testing of AI-based traffic solutions.
II. Workflow of AI-Based Dynamic Lane Management To improve urban mobility and ease traffic congestion, dynamic lane management systems employ a combination of technologies, primarily powered by the Internet of Things (IoT) and Artificial Intelligence (AI). This integrated approach connects sensors, communication networks, and data processing tools to enable real-time decision-making and more efficient traffic systems. Step 1: Strategically placed IoT devices—such as cameras, radar, and lidar—constantly gather traffic-related data including vehicle counts, speed, and congestion levels. These devices communicate with infrastructure through Vehicle-toInfrastructure (V2I) systems, exchanging data such as position, speed, and direction. This continuous flow of data enables the system to monitor traffic conditions and detect potential bottlenecks.
Key Words: AI-driven, dynamic lane management, Dynamic Lane Reversal (DLR), Dynamic Lane Assignment (DLA), traffic flow, road safety, machine learning, urban traffic, travel time, policy development.
Step 2: A centralized control unit processes the incoming data via communication technologies like cellular networks or Dedicated Short Range Communication (DSRC). Based on the analysed data, appropriate algorithms are selected—such as Dynamic Lane Switching, Reversal, or Usage Control—to manage traffic accordingly.
1.INTRODUCTION Urban traffic congestion is a widespread problem that leads to fuel wastage, delays, higher emissions, and slower emergency responses. To address this, artificial intelligence is being integrated into dynamic lane management systems as a promising way to handle varying traffic conditions effectively.
Step 3: The system then transmits updated lane assignments and signal changes to electronic signage and traffic lights on the roads. For instance, in case of an accident, it can reroute traffic in real time by reallocating nearby lanes to maintain flow. Simultaneously, it offers route suggestions to individual vehicles using real-time conditions and lane layouts, which connected vehicles can access and respond to, resulting in smarter travel choices and improved traffic efficiency.
These systems, empowered by IoT and real-time analytics, continuously adjust traffic lanes and signals based on current traffic patterns. By using sensors and smart algorithms, they help optimize flow and increase safety. The growing adoption of AI-powered autonomous vehicles has further enabled realtime, automated lane adjustments for more efficient traffic handling. Traditional traffic control methods, which rely on static lane assignments, often fall short in adapting to changing traffic volumes. In contrast, intelligent transportation systems use dynamic lane grouping, which reassigns lanes based on current needs, providing a more adaptable traffic solution.
This sequence outlines the foundational steps of implementing AI-powered Dynamic Lane Management (DLM). The following sections will explore the specific strategies and their effects on traffic congestion. III. Strategy 1 – Dynamic Lane Reversal:
This paper focuses on the combination of AI-based lane assignment and signal optimization at intersections. Through a comprehensive literature review, it aims to enhance
© 2025, IRJET
|
Impact Factor value: 8.315
Dynamic Lane Reversal (DLR) is a proactive traffic management method used to reduce congestion by
|
ISO 9001:2008 Certified Journal
|
Page 1466