Design and Development of a Novel Sensor Assisted Vehicle Count Prediction Using Modulated Deep Learning Principles

In the realm of traffic management and urban planning, accurate prediction of vehicle counts is crucial for optimizing transportation systems and ensuring efficient traffic flow. Traditional prediction models often rely on historical data and mathematical algorithms, which may lack the ability to adapt to dynamic real-world conditions. This paper proposes a novel approach that integrates sensor data with Explainable Artificial Intelligence (XAI) principles to enhance the accuracy and interpretability of vehicle count predictions. The proposed system utilizes a network of sensors deployed across key traffic points to collect real-time data on vehicle movements, including speed, volume, and direction. This sensor data is then fed into a machine learning model, specifically designed to predict vehicle counts based on various environmental and contextual factors. Unlike conventional black-box models, the proposed system leverages XAI techniques to provide transparent insights into the decision-making process, enabling stakeholders to understand and trust the predictions. Key components of the XAI-based prediction model include feature importance analysis, model-agnostic interpretability methods, and interactive visualization tools. By identifying the most influential features and explaining the rationale behind predictions, the system empowers transportation planners and policymakers to make informed decisions regarding traffic management strategies and infrastructure investments. By enhancing both accuracy and interpretability, the proposed system paves the way for more effective traffic management strategies and ultimately improves the overall quality of urban mobility.