Nonlinear prediction model of vehicle network traffic management based on the internet of things

Abstract

This research presents a novel nonlinear prediction model for Internet of Things (IoT) driven vehicle network traffic management. Current traffic prediction systems use linear models that do not characterize the highly nonlinear urban traffic dynamics. We integrate real-time IoT sensor data with a dual-layer long short-term memory (LSTM) neural network architecture optimised for traffic prediction. System architecture consists of three spatially separated layers: IoT sensor network for data collection, real-time data processing pipeline and the user interface for visualization. The predictive accuracy in terms of Mean Squared Error (0.0842), Mean Absolute Error (0.0623) and the R² score (0.9187) was better on average for 35 strategic urban sites at 6 months. It achieved a 92 % prediction accuracy during morning peak hours and maintained response times <200 ms for 98.5 % of predictions under any load conditions. The system resilience testing involved 99.95 % uptime with robust operation even with 15 % of the sensors failing. Challenges with extreme weather conditions and data gaps still exist; however, this research contributes to theoretical understanding of nonlinear traffic dynamics and practical applications for smart city development. While the system presented here paves the way for more intelligent, adaptive solutions to Urban Mobility to reduce congestion significantly and improve traffic management efficiency, there still exist issues regarding the acquisition of traffic data, the phenomenon of commuting behavior, and only rudimentary efforts to mathematically model passenger exposure.