Enabling internet of things in road traffic forecasting with deep learning models

Abstract

Integration of the latest technological advancements such as Internet of Things (IoT) and Computational Intelligence (CI) techniques is an active research area for various industrial applications. The rapid urbanization and exponential growth of vehicles has led to crowded traffic in cities. The deployment of IoT infrastructures for building smart and intelligent traffic management system greatly improves the quality and comfort of city dwellers. This work aims at building a cost effective IoT enabled traffic forecasting system using deep learning techniques. The case study experimentation is done in a real time traffic environment. The main contributions of this work include: (i) deploying road side sensor station built with ultrasonic sensor and Arduino Uno controller for obtaining traffic flow data (ii) building an IoT cloud system based on open source Thingspeak cloud platform for monitoring real time traffic (iii) performing short term traffic forecast using Recurrent Neural Network (RNN) models such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of the prediction model is compared with the traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) and Convolutional Neural Network (CNN). The results show good performance metrics with RMSE of 5.8, 7.9, 10.2 for LSTM model and 6.7, 8.6, 10.9 for GRU model for three different scenarios such as whole day, morning congested hour and evening congested hour datasets.