Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms

Technologies

Didier López-Mancilla¹  Esteban Tlelo-Cuautle²  Oscar Roberto López-Bonilla³  Everardo Inzunza-González³  Alfonso Navarro-Espinoza³  Enrique Efrén García-Guerrero³  Carlos Hernández-Mejía⁴ 

[1] Centro Universitario de Los Lagos, Universidad de Guadalajara (UDG), Lagos de Moreno C.P. 47460, Mexico;Department of Electronics, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla C.P. 72840, Mexico;Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carret. Transpeninsular Ensenada—Tijuana No. 3917, Fracc. Playitas, Ensenada C.P. 22860, Mexico;Instituto Tecnológico Superior de Misantla, Doctorado en Ciencias de la Ingeniería, Km 1.8 Carretera Lomas del Cojolite, Misantla C.P. 93821, Mexico;

关键词: intelligent transportation system;    smart city;    traffic flow prediction;    traffic light;    machine-learning;    deep-learning;   

DOI  :  10.3390/technologies10010005

来源: DOAJ

【 摘 要 】

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.

【 授权许可】

Unknown