Road Speed Prediction Scheme by Analyzing Road Environment Data

Abstract

Road speed is an important indicator of traffic congestion. Therefore, the occurrence of traffic congestion can be reduced by predicting road speed because predicted road speed can be provided to users to distribute traffic. Traffic congestion prediction techniques can provide alternative routes to users in advance to help them avoid traffic jams. In this paper, we propose a machine-learning-based road speed prediction scheme using road environment data analysis. The proposed scheme uses not only the speed data of the target road, but also the speed data of neighboring roads that can affect the speed of the target road. Furthermore, the proposed scheme can accurately predict both the average road speed and rapidly changing road speeds. The proposed scheme uses historical average speed data from the target road organized by the day of the week and hour to reflect the average traffic flow on the road. Additionally, the proposed scheme analyzes speed changes in sections where the road speed changes rapidly to reflect traffic flows. Road speeds may change rapidly as a result of unexpected events such as accidents, disasters, and construction work. The proposed scheme predicts final road speeds by applying historical road speeds and events as weights for road speed prediction. It also considers weather conditions. The proposed scheme uses long short-term memory (LSTM), which is suitable for sequential data learning, as a machine learning algorithm for speed prediction. The proposed scheme can predict road speeds in 30 min by using weather data and speed data from the target and neighboring roads as input data. We demonstrate the capabilities of the proposed scheme through various performance evaluations.

Dimensionality Reduction and Prediction of Impedance Data of Biointerface

“…Today, LSTM applications are mainly concerned with predicting upcoming events or signals [25,30,31]. In our research, we used the LSTM network, and its main use was as a forecasting tool of a signal [30,31] to study the ability to estimate upcoming changes in impedance behavior using the normalized DF signal which briefly describes the impedance changes.…”

“…Today, LSTM applications are mainly concerned with predicting upcoming events or signals [25,30,31]. In our research, we used the LSTM network, and its main use was as a forecasting tool of a signal [30,31] to study the ability to estimate upcoming changes in impedance behavior using the normalized DF signal which briefly describes the impedance changes. The prediction of DF is implemented using the DF values of previous soaking days as a training dataset and the next soaking day as a targeted value, and because we mentioned earlier that the DF signal is resampled to a larger number of samples, the targeted result or predicted DF is a series of values that represents the predicted soaking day.…”