Zoican, S.; Zoican, R.; Galatchi, D.; Vochin, M. Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting. Appl. Sci.2024, 14, 412.
Zoican, S.; Zoican, R.; Galatchi, D.; Vochin, M. Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting. Appl. Sci. 2024, 14, 412.
Zoican, S.; Zoican, R.; Galatchi, D.; Vochin, M. Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting. Appl. Sci.2024, 14, 412.
Zoican, S.; Zoican, R.; Galatchi, D.; Vochin, M. Graph-Based Neural Networks’ Framework Using Microcontrollers for Energy-Efficient Traffic Forecasting. Appl. Sci. 2024, 14, 412.
Abstract
The paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. The method minimizes the communication network (between vehicles and the database servers) load and represents a reasonable trade-off between communication network load and forecasting accuracy. Traffic prediction leads to the choice of less congested routes and therefore to the reduction of energy consumption. The traffic is forecasting using a LTSM neural network with a regression layer. The inputs of the neural network are sequences - obtained from graph that represent the road network - at specific moments of time that are read from traffic sensors or the outputs of neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. This general framework is based on Contiki IoT operating system that ensure support for wireless communication and efficient implementation of processes in a resource constrained system and it is particularized to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and the other case in which the traffic sensors are read when their values changes are detected. A comparison between the cases is made and the influence of filtering is evaluated. The obtained accuracy is very good, very close to the accuracy obtained in infinite precision simulation, and the computation time is low enough and the system can work in real time.
Keywords
graph-based neural network, traffic forecasting, Internet of Things, Contiki operating system
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
