Article · International Review of Automatic Control · January 31, 2020

Link Flow Estimation on an Isolated Intersection Based on Deep Learning Models

Gulnur Tolebi, N. S. Dairbekov, Daniyar Kurmankhojayev

Abstract

This paper considers the problem of short-term estimation of the link flow on an isolated intersection by means of artificial intelligence (AI). The problem is formulated as a supervised learning regression task. Several architectures of neural networks are presented to predict the link flow: recurrent neural network (RNN), convolutional neural network (CNN), and a combination of the two, with RNN treating the time dependencies and CNN extracting the spatial properties from traffic. The models are trained on synthetic data that are generated by a simulator. These models are compared with a fully-connected network. Numerical experiments are carried out on CPU and GPU. They show that deep learning models with special architecture can estimate the link flow much better than a traditional neural network on a single intersection. The obtained link flow estimates can be used in different traffic control algorithms in order to minimize the transport network congestion.

Traffic Prediction and Management TechniquesTraffic control and managementHydrological Forecasting Using AIlink flow estimationisolated intersectionsshort-term traffic forecasting

Link Flow Estimation on an Isolated Intersection Based on Deep Learning Models

Abstract

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