AI to Predict and Determine Incoming Traffic Based on Vehicle Speed

Abstract

Traffic jam has emerged as one of the most immovable problems of the urban mobility in the recent urban history, which have consequences on the time of travel, fuel economy, road safety and the environmental sustainability. Conventional traffic management is founded on conventional sensing methods such as inductive loop detectors, cameras or radar which are usually susceptible to processing real-time loads of data to extremely large volumes. The recent advances of artificial intelligence (AI) considered in particular machine learning (ML) and deep learning (DL) provide the modern opportunities of dynamically predicting traffic conditions, provided that a series of input features are present, such as vehicle speed, vehicle density, GPS trace, sensor fusion data. The present paper has established a comprehensive framework which is used to predict and establish the forthcoming traffic situations based primarily on the change in vehicle speed. With the help of the recurring neural network (RNNs), long short-term memory (LSTM) models, and reinforcement learning, we design such a predictive model capable of revealing the patterns of congestion, the peak hours, and the aberrant circumstances such as sudden keepup of a slowing down through accidents.This framework consists of five fundamental steps: (i) traffic sensor and vehicular ad hoc network (VANET) data acquisition and preprocessing, (ii) feature engineering involving attention to speed patterns, acceleration-deceleration ratios, and vehicle flow density, (iii) model training and validation, based on past traffic data (i.e., historical traffic data), (iv) real-time prediction of congestion and estimated time of arrival (ETA), and (v) decision support system-based adaptive traffic signal control and rerouting. Simulation and real world experimental findings indicate accuracy to prediction to almost 92% in different traffic conditions. Moreover, our system offers almost real-time performance of the performance so as to provide a low delay rate between senses and prediction. The computational complexity, the problem of scalability, and the problems of integration of smart cities are also examined in the paper. The results highlight the importance of speed-based AI-based prediction models in changing traffic management systems. In contrast to density-based methods that demand massive hardware implementation, speed-based inference offers lightweight and scalable solutions to both developed and developing territories. The findings indicate that the given model will be able to contribute to the current urban mobility and decrease traffic congestion, not to mention the future intelligent transportation systems (ITS).