Towards a new intelligent traffic system based on deep learning and data integration

Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.

A novel wind power prediction model using graph attention networks and bi-directional deep learning long and short term memory

Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work

Adaptive traffic lights based on traffic flow prediction using machine learning models

Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections

Artificial Neural Network and a Nonlinear Regression Model for Predicting Electrical Pole Crash

This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas

Comparative analysis of short-term demand predicting models using ARIMA and deep learning

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network

A novel hybrid deep learning approachfor tourism demand forecasting

This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models

Efficiency of recurrent neural networks for seasonal trended time series modelling

Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for modelling seasonal time series with trend. For this purpose, we are interested in the learning stability of the established systems using the mean average percentage error (MAPE) as a measure. Both simulated and real data were examined, and we have found a positive correlation between the signal period and the system input vector length for stable and relatively efficient learning. We also examined the white noise impact on the learning performance

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent