Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

Ma, Xiaolei; Tao, Zhimin; Wang, Yinhai; Yu, Haiyang; Wang, Yunpeng

doi:10.1016/j.trc.2015.03.014

Cited by 1,713 publications

(863 citation statements)

References 51 publications

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“…Our method based on LSTM has potential for more extensive applications. Since LSTM is also capable of learning and predicting time series without seasonal patterns [22]. Figure 9 shows the real data and predictions of a time series with water in 2009.…”

Section: Discussionmentioning

confidence: 99%

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

et al. 2018

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Abstract:A satellite image time series (SITS) contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth's surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests) and human activities (for example, deforestation and urbanisation) will disturb this pattern and cause a relatively profound change on the Earth's surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM) networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1) illustrate the effectiveness and stability of the proposed approach for online disturbance detection.

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Section: Discussionmentioning

confidence: 99%

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

et al. 2018

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“…For traffic information forecasting methods, several studies used data mining techniques (e.g., linear regression [26,27], logistic regression (LR) [28], Bayesian classifier [29,30], k-nearest neighbors (kNN) [31][32][33], artificial neural network (ANN) [34][35][36][37], etc.) to analyze the historical traffic information and obtain the forecasted traffic information.…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

“…However, these regression approaches are unsuitable to analyze the non-linear distribution of data, so the accuracy of the vehicle speed prediction-based regression approach was only 73.3% for an urban road network in [27]. Furthermore, the regression approaches are the special cases of ANN without hidden layers, and the accuracy of the traffic information forecasting method based on ANN may be higher than regression approaches [34][35][36][37].…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

“…For traffic information forecasting methods based on ANN, some studies proposed and applied ANN to forecast short-term traffic information, and the experimental results showed that the accuracy of using ANN was better than using other approaches [35][36][37]. Although a previous study classified road segments into several road types for reducing ANN models [35], the diverse traffic characteristics of road segments may exist in the same type of road.…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

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Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

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Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.

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“…Discussions on the road networks that consist of many oversaturated intersections can be found in researches done by Chang and Sun [4], Di Febbraro and Sacco [5], Dotoli and Fanti [6], Ma [7,8], and Sun et al [9,10]. In the research done by Varaiya [11] and Le et al [12], the study of pressure-based signal control developed stability properties of a decentralized signal timing policy for networks with stochastic arrivals.…”

Section: Introductionmentioning

confidence: 99%

Continuous and Discrete-Time Optimal Controls for an Isolated Signalized Intersection

Tan

Shi

et al. 2017

Journal of Sensors

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A classical control problem for an isolated oversaturated intersection is revisited with a focus on the optimal control policy to minimize total delay. The difference and connection between existing continuous-time planning models and recently proposed discrete-time planning models are studied. A gradient descent algorithm is proposed to convert the optimal control plan of the continuous-time model to the plan of the discrete-time model in many cases. Analytic proof and numerical tests for the algorithm are also presented. The findings shed light on the links between two kinds of models.

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Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

Cited by 1,713 publications

References 51 publications

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

Continuous and Discrete-Time Optimal Controls for an Isolated Signalized Intersection

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