# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 1 Introduction  
The intelligent transport system (ITS) is currently being developed for the road transport network, and the latest information and communication technology are being worked with. Many ITS applications, such as traffic control, emergency detection, parking guidance, etc., have extensively incorporated deep learning, and they showed significant performance improvement [1; 2; 3]. This study reports a novel design of an intelligent traffic forecasting system using deep learning, applied alongside ITS for efficient driving and urban planning. Many studies have used deep learning algorithms for traffic prediction. Some research studies have considered exploiting neural networks for better analysis of the spatio-temporal correlation of traffic data. The authors of [4] propose a short-term traffic forecast model using a two-dimensional long short-term memory (LSTM) network. In [5], the authors use a convolutional neural network (CNN) by converting traffic data from multiple road sections into 2D images. In [6; 7], the authors adopt Google DeepMind's WaveNet network and a hierarchical temporal memory model, respectively. Research has been conducted for predicting traffic behavior in unusual traffic conditions. In [8], the authors propose a traffic prediction method based on multi-task learning. They classify traffic data based on traffic situations and construct situation-aware prediction models. The authors of [9] suggest a pattern sensitive network with adversarial training. They train a joint distribution model to improve prediction accuracy under unusual traffic conditions. In [10], the authors adopt a regularized loss function to balance the predictions of light and heavy traffic conditions. Some studies have addressed more interesting factors affecting traffic behavior. In [11], the authors introduce a rainfall factor into the deep belief network (DBN) and LSTM to predict urban traffic flow. The authors of [12] combine the flow-based and weather-based traffic model with a DBN, and theauthors of [13] propose a long-term speed prediction method based on LSTM. They analyze the correlation between the historical parameters of drivers, vehicles, roads, and traffic. Research on other data-driven prediction models is still ongoing. In [14], the authors introduce an iterative \(k\)-nearest neighbor predictor for long-term traffic forecasting. The authors of [15; 16] propose \(k\)-nearest neighbor traffic prediction models that adopt advanced similarity measures and dynamic parameter tuning, respectively, and the authors of [17] propose a short-term traffic prediction method based on an adaptive multi-kernel support vector machine. Other researches continue to improve time-series based prediction [18]. Authors of [19] proposed a traffic prediction scheme using both historical and real-time data. They identified traffic situation with bayesian network and fused auto-regressive integrated moving average and periodical moving average model with neural networks. Authors of [20]proposed a long-term traffic forecasting model based on functional nonparametric regression. They adopted an autocorrelation analysis and a functional principal component analysis for better accuracy. This study proposes a new traffic forecasting model using a multi-module deep neural network (DNN) framework, called the time and weather aware DNN (TW-DNN). This system is developed based on our previous design [21], to generate more reliable and complete long-range traffic data. The framework contains two blocks of neural network modules. The first block extracts various features from the input, and the second block fuses the features to obtain a traffic prediction output. Large amounts of data such as weather forecast data, road network information, and traffic speed and flow data are collected to train the system. The experiments are conducted focusing on multiple traffic situations, including rush hours, holidays, and heavy rains, and the performance is compared with that of different but equally capable systems.