Sensing the Road Ahead: Advanced Randomized Algorithm for Situational Awareness in Smart Transportation

Abstract

Smart transportation is a burgeoning area of study that leverages technological advancements to enhance the safety and efficacy of transportation infrastructure. The application of sensors, data analytics, and communication technologies is utilized to augment the transportation experience holistically. Situational awareness is a crucial aspect of smart transportation, encompassing the ability to detect and anticipate potential hazards and obstacles on the road ahead. Maintaining situational awareness is of utmost importance in guaranteeing secure and adequate transportation. Nevertheless, its intricate and ever-changing characteristics make transportation systems a formidable task. Modern transportation systems generate copious amounts of data with various features, exceeding conventional algorithms’ processing capabilities. In response to these challenges, the research suggests implementing an Advanced Randomized Algorithm-based Situational Awareness Model (ARA-SAM) to detect the congestion areas and route to free paths. The ARA-SAM algorithm employs a machine-learning approach. It utilizes a randomized algorithm, specifically the Hybrid Ant Colony Optimization (HACO), to forecast potential hazards and obstacles that arise on the roadway. The system is engineered to effectively manage large quantities of data and promptly adjust to evolving circumstances. ARA-SAM’s performance was evaluated through simulations utilizing real-world data. The findings indicate that ARA-SAM can precisely predict potential hazards and obstacles while maintaining a low latency rate with a delay of 23.4[Formula: see text]s, a queue length of 44.4[Formula: see text]m, a travel time of 303.8[Formula: see text]s, a density of 44.8 vehicles per km, and a vehicle count of 1937.5 vehicles per hour. The model’s efficacy in identifying lane changes, pedestrians, and vehicles, among other factors, is evidenced by the sample values. Enhancing the safety and efficiency of transportation systems can facilitate the emergence of novel advancements in the domain.