Forecasting the pavement icing in traffic zone using long short-term memory

2021 ◽  
pp. 1-9
Author(s):  
Rao Zhongyang ◽  
Feng Chunyuan
Keyword(s):  
Traffic Safety ◽  
Short Term Memory ◽  
Freezing Point ◽  
Water Film ◽  
Model Parameters ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Pavement Temperature ◽  
Long Short Term Memory

In winter, driving is a difficult due to road icing. It is one of the most unfavorable weather conditions that endangers traffic safety. We gathered data on pavement temperature, freezing point temperature, friction coefficient, pavement water film thickness, ice content, and pavement condition using sensors. Those data are feed into Long Short-Term Memory (LSTM) to predict road icing in the city. Here the primary issue is forecasting the pavement icing of the traffic zone. Our work is an endeavor to use the deep learning method on LSTM to forecast pavement icing on Ji’nan in China. Those Sequential data of pavement icing can process and memorize by LSTM at a specific time. Finally, research results indicate that the performance of the model is very precise. With LSTM model parameters’ help, the sequential data on road icing prediction can also predict the pavement temperature.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

scholarly journals Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model

2020 ◽  
Vol 2 (3) ◽  
pp. 256-270
Author(s):  
Shakti Goel ◽  
Rahul Bajpai
Keyword(s):  
Short Term Memory ◽  
Forecasting Model ◽  
Model Parameters ◽  
Prediction Errors ◽  
Short Term ◽  
Term Memory ◽  
Corona Virus ◽  
Long Short Term Memory ◽  
Input Variables ◽  
The Impact

A Long Short Term Memory (LSTM) based sales model has been developed to forecast the global sales of hotel business of Travel Boutique Online Holidays (TBO Holidays). The LSTM model is a multivariate model; input to the model includes several independent variables in addition to a dependent variable, viz., sales from the previous step. One of the input variables, “number of active bookers per day”, is estimated for the same day as sales. This need for estimation requires the development of another LSTM model to predict the number of active bookers per day. The number of active bookers is variable, so the predicted is used as an input to the sales forecasting model. The use of a predicted variable as an input variable to another model increases the chance of uncertainty entering the system. This paper discusses the quantum of variability observed in sales predictions for various uncertainties or noise due to the estimation of the number of active bookers. For the purposes of this study, different noise distributions such as normalized, uniform, and logistic distributions are used, among others. Analyses of predictions demonstrate that the addition of uncertainty to the number of active bookers via dropouts as well as to the lagged sales variables leads to model predictions that are close to the observations. The least squared error between observations and predictions is higher for uncertainties modeled using other distributions (without dropouts) with the worst predictions being for Gumbel noise distribution. Gaussian noise added directly to the weights matrix yields the best results (minimum prediction errors). One possibility of this uncertainty could be that the global minimum of the least squared objective function with respect to the model weight matrix is not reached, and therefore, model parameters are not optimal. The two LSTM models used in series are also used to study the impact of corona virus on global sales. By introducing a new variable called the corona virus impact variable, the LSTM models can predict corona-affected sales within five percent (5%) of the actuals. The research discussed in the paper finds LSTM models to be effective tools that can be used in the travel industry as they are able to successfully model the trends in sales. These tools can be reliably used to simulate various hypothetical scenarios also.

scholarly journals Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5141
Author(s):  
Wenying Lyu ◽  
Honghai Zhang ◽  
Junqiang Wan ◽  
Lei Yang
Keyword(s):  
Time Series ◽  
Traffic Safety ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Risk Identification ◽  
Civil Aviation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Traffic safety has been thought of as a basic feature of transportation, recent developments in civil aviation have emphasized the need for risk identification and safety prediction. This study aims to increase en-route flight safety through the development of prediction models for flight conflicts. Firstly, flight conflicts time series and traffic parameters are extracted from historical ADS-B data. In the second step, a Long Short-Term Memory (LSTM) model is trained to make a one-step-ahead prediction on the flight conflict time series. The results show that the LSTM model has the greatest prediction effect (MAE 0.3901) with comparison to other models. Based on that, we add traffic parameters (volume, density, velocity) into the LSTM model as new input variables and issue a comprehensive analysis of the relative predictive power of traffic parameters. The accuracy of prediction model is validated with a mean error of less than 3%. Based on the improvements of model performance brought by traffic parameters, LSTM models with a single traffic parameter are proposed for further discussion. The results illustrate that volume is the most important factor in promoting prediction accuracy and density has an advantage of improvement in the aspect of model stability.

Neural Probabilistic Forecasting of Symbolic Sequences With Long Short-Term Memory

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Michael Hauser ◽  
Yiwei Fu ◽  
Shashi Phoha ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Probability Distributions ◽  
Series Data ◽  
Model Parameters ◽  
Short Term ◽  
Term Memory ◽  
Experimental Apparatus ◽  
Long Short Term Memory

This paper makes use of long short-term memory (LSTM) neural networks for forecasting probability distributions of time series in terms of discrete symbols that are quantized from real-valued data. The developed framework formulates the forecasting problem into a probabilistic paradigm as hΘ: X × Y → [0, 1] such that ∑y∈YhΘ(x,y)=1, where X is the finite-dimensional state space, Y is the symbol alphabet, and Θ is the set of model parameters. The proposed method is different from standard formulations (e.g., autoregressive moving average (ARMA)) of time series modeling. The main advantage of formulating the problem in the symbolic setting is that density predictions are obtained without any significantly restrictive assumptions (e.g., second-order statistics). The efficacy of the proposed method has been demonstrated by forecasting probability distributions on chaotic time series data collected from a laboratory-scale experimental apparatus. Three neural architectures are compared, each with 100 different combinations of symbol-alphabet size and forecast length, resulting in a comprehensive evaluation of their relative performances.

scholarly journals Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7333
Author(s):  
Ricardo Petri Silva ◽  
Bruno Bogaz Zarpelão ◽  
Alberto Cano ◽  
Sylvio Barbon Junior
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Wide Range ◽  
Long Short Term Memory ◽  
Adf Test

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

Auto-Detection of Human Factor Contents on Social Media Posts Using Word2vec and Long Short-Term Memory (LSTM)

2020 ◽  
pp. 1-13
Author(s):  
Chika Yinka-Banjo ◽  
Gafar Lekan Raji ◽  
Ifeanyi Precious Ohalete
Keyword(s):  
Social Media ◽  
Short Term Memory ◽  
Sequential Data ◽  
Effective Solution ◽  
Learning Models ◽  
Short Term ◽  
Test Dataset ◽  
Term Memory ◽  
Poor Academic Performance ◽  
Long Short Term Memory

The threat posed by cyberbullying to the mental health in our society cannot be overemphasized. Victims of this menace are reported to have suffered poor academic performance, depression, and suicidal thoughts. There is need to find an efficient and effective solution to this problem within the academic environment. In this research, one of the popular deep learning models—long short-term memory (LSTM)—known for its optimized performance in training sequential data was combined with Word2Vec embedding technique to create a model trained for classifying the content of social media post as containing cyberbullying content or otherwise. The result was observed to have shown improvements in its performance with respect to accuracy in the classification task with over 80% of the test dataset correctly classified as against the existing model with about 74.9% accuracy.

scholarly journals Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems

2019 ◽  
Vol 9 (17) ◽  
pp. 3470
Author(s):  
Nguyen Minh-Tuan ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Sequential Data ◽  
Short Term ◽  
Data Simulation ◽  
Assignment Problems ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Fully Connected

Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LSAP. We herein propose a new deep learning model based on the bidirectional long short-term memory (BDLSTM) structure for the LSAP. In the proposed method, the LSAP is divided into sequential sub-assignment problems, and BDLSTM extracts the features from sequential data. Simulation results indicate that the proposed BDLSTM is more memory efficient and achieves a higher accuracy than conventional techniques.

Driving Maneuvers Detection using Semi-Supervised Long Short-Term Memory and Smartphone Sensors

2021 ◽  
pp. 036119812110074
Author(s):  
Pei Li ◽  
Mohamed Abdel-Aty ◽  
Zubayer Islam
Keyword(s):  
Supervised Learning ◽  
Traffic Safety ◽  
Short Term Memory ◽  
Safety Management ◽  
Learning Algorithm ◽  
Unlabeled Data ◽  
Connected Vehicle ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Driving maneuvers detection is an important component of proactive traffic safety management and connected vehicle systems. Most of the existing studies used supervised learning concepts to train their models with labeled data. These methods achieved promising results but were limited by the heavy dependence on the labeled data. With the development of mobile sensing technologies, massive traffic-related data can be efficiently collected by mobile devices (e.g., smartphones, tablets, etc.). Considering the high costs of labeling data, this paper proposed a semi-supervised deep learning method to learn from the unlabeled data. Data from a smartphone’s accelerometer and gyroscope were collected by different drivers with a variety of smartphones, vehicles, and locations. Three long short-term memory (LSTM) models were trained with the proposed semi-supervised learning algorithm. Experimental results indicated that the proposed semi-supervised LSTM could learn from the unlabeled data and achieve outstanding results with only a small portion of the labeled data. Using much fewer labeled data, semi-supervised LSTM could achieve similar results compared with the supervised method. Moreover, the proposed method outperformed other machine learning methods (e.g., convolutional neural network, XGBoost, random forest) on precision, recall, F1-score, and area under curve. More and more traffic data will be available in the future, the proposed method is expected to make use of the undiscovered potential from the massive unlabeled data.

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