Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

scholarly journals Jamming Prediction for Radar Signals Using Machine Learning Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Gyeong-Hoon Lee ◽  
Jeil Jo ◽  
Cheong Hee Park
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Training Data ◽  
Electronic Warfare ◽  
New Type ◽  
Long Short Term Memory ◽  
Radar Signals ◽  
Feature Values ◽  
Deep Learning Model

Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long short-term memory (LSTM) which takes the PDW list as input. Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs. Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library. The prediction performance and time complexity of two proposed methods are compared. In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.

Deep Learning Model for Forecasting Financial Sales Based on Long Short-Term Memory Networks

2019 ◽  
pp. 498-506
Author(s):  
Pablo F. Ordoñez-Ordoñez ◽  
Martha C. Suntaxi Sarango ◽  
Cristian Narváez ◽  
Maria del Cisne Ruilova Sánchez ◽  
Mario Enrique Cueva-Hurtado
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Deep Learning Model

scholarly journals DeepDigest: prediction of protein proteolytic digestion with deep learning

2020 ◽  
Author(s):  
Jinghan Yang ◽  
Zhiqiang Gao ◽  
Xiuhan Ren ◽  
Jie Sheng ◽  
Ping Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Shotgun Proteomics ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Cleavage Sites ◽  
Proteomics Research ◽  
Long Short Term Memory ◽  
Deep Learning Model ◽  
Subsequent Identification

ABSTRACTIn shotgun proteomics, it is essential to accurately determine the proteolytic products of each protein in the sample for subsequent identification and quantification, because these proteolytic products are usually taken as the surrogates of their parent proteins in the further data analysis. However, systematical studies about the commonly used proteases in proteomics research are insufficient, and there is a lack of easy-to-use tools to predict the digestibilities of these proteolytic products. Here, we propose a novel sequence-based deep learning model – DeepDigest, which integrates convolutional neural networks and long-short term memory networks for digestibility prediction of peptides. DeepDigest can predict the proteolytic cleavage sites for eight popular proteases including trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN and LysargiNase. Compared with traditional machine learning algorithms, DeepDigest showed superior performance for all the eight proteases on a variety of datasets. Besides, some interesting characteristics of different proteases were revealed and discussed.

scholarly journals Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

2021 ◽  
Author(s):  
Mahdi Yousefzadeh Aghdam ◽  
Seyed Reza Kamel ◽  
Seyed Javad Mahdavi Chabok ◽  
maryam khairabadi
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Short Term Memory ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Learning Machine ◽  
Deep Learning Model

Abstract Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.

Sign in / Sign up

Export Citation Format

Share Document