scholarly journals Multistep Prediction of Bus Arrival Time with the Recurrent Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhi-Ying Xie ◽  
Yuan-Rong He ◽  
Chih-Cheng Chen ◽  
Qing-Quan Li ◽  
Chia-Chun Wu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Traffic Congestion ◽  
Arrival Time ◽  
Short Term Memory ◽  
Model Simulation ◽  
Accurate Prediction ◽  
Data Set ◽  
Arrival Times ◽  
Public Transport System

Accurate predictions of bus arrival times help passengers arrange their trips easily and flexibly and improve travel efficiency. Thus, it is important to manage and schedule the arrival times of buses for the efficient deployment of buses and to ease traffic congestion, which improves the service quality of the public transport system. However, due to many variables disturbing the scheduled transportation, accurate prediction is challenging. For accurate prediction of the arrival time of a bus, this research adopted a recurrent neural network (RNN). For the prediction, the variables affecting the bus arrival time were investigated from the data set containing the route, a driver, weather, and the schedule. Then, a stacked multilayer RNN model was created with the variables that were categorized into four groups. The RNN model with a separate multi-input and spatiotemporal sequence model was applied to the data of the arrival and leaving times of a bus from all of a Shandong Linyi bus route. The result of the model simulation revealed that the convolutional long short-term memory (ConvLSTM) model showed the highest accuracy among the tested models. The propagation of error and the number of prediction steps influenced the prediction accuracy.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

Estimating normal moveout velocity using the recurrent neural network

2019 ◽  
Vol 7 (4) ◽  
pp. T819-T827
Author(s):  
Reetam Biswas ◽  
Anthony Vassiliou ◽  
Rodney Stromberg ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Mean Squared Error ◽  
Correction Term ◽  
Computation Time ◽  
Velocity Estimation ◽  
Data Set ◽  
Arrival Times ◽  
Nmo Correction ◽  
Novel Method

Machine learning (ML) has recently gained immense popularity because of its successful application in complex problems. It develops an abstract relation between the input and output. We have evaluated the application of ML to the most basic seismic processing of normal moveout (NMO) correction. The arrival times of reflection events in a common midpoint (CMP) gather follow a hyperbolic trajectory; thus, they require a correction term to flatten the CMP gather before stacking. This correction term depends on an rms velocity, also referred to as the NMO velocity. In general, NMO velocity is estimated using the semblance measures and picking the peaks in the velocity panel. This process requires a lot of human intervention and computation time. We have developed a novel method using one of the tools based on an ML- approach and applied to the NMO velocity estimation problem. We use the recurrent neural network (RNN) to estimate the NMO velocity directly from the seismic data. The input to the network is a seismic gather and corresponding precalculated NMO velocity (as prelabeled data set) to flatten the gather. We first train the network to develop a relationship between the input gathers (before NMO correction) and the corresponding NMO velocities for a few CMPs as a supervised learning process. Adam optimization algorithm is used to train the RNN. The output from the network is then compared against the correct NMO velocity. The error between the two velocities is then used to update the weight of the neurons and to minimize the mean-squared error between the two velocities. After the network is trained, it can be used to calculate the NMO velocity for the rest of the seismic gathers. We evaluate our method on a noisy data set from Poland. We used only 10% of the CMPs to train the network, and then we used the trained network to predict NMO velocity for the remaining CMP locations. The stack section obtained by using RNN-generated NMO velocities is nearly identical to that obtained by the conventional semblance method.

scholarly journals Automated Amharic Hate Speech Posts and Comments Detection Model Using Recurrent Neural Network

2020 ◽  
Author(s):  
Surafel Getachew Tesfaye ◽  
Kula Kakeba
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Performance Test ◽  
Hate Speech ◽  
Short Term Memory ◽  
Detection Methods ◽  
Batch Size ◽  
Neural Network Models ◽  
Data Set ◽  
Speech Detection

Abstract During the last few years, social activities over the internet especially on social media platforms increased drastically, but unfortunately, social networks have also become the place for hate speech proliferation by which most people’s social lives are disturbed because of hate speech posts and conflicts triggered by those posts. Studies confirm that online hate speech has different offline consequences. Even though there are a lot of researches on automated hate speech detection most of them are for other language and there is a scarcity of labeled data to apply automated analysis and detection methods on Amharic dataset. Therefore the research on automatic detection of hate speech posts attracted our attention. As a solution to those problems, this research aimed to prepare a labeled huge Amharic dataset by collecting posts and comments from selected Facebook pages of activists that participated actively. Those Facebook data sets are labeled manually as hate and free based on the guidelines given from researcher and pre-processed by applying data cleaning and normalization techniques. In this research the recurrent neural network models for automated hate speech posts detection from Amharic posts on Facebook is developed by using Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) with word n-grams for feature extraction and word2vec to represent each unique word by vector representation. The experiment conducted on those two models by using 80% of the data set for training and 10% for validation to train the model and to select the best hyper-parameters combination for automated hate speech posts detection. The remaining 10% of the dataset used for testing the model after training. As a result LSTM based RNN of Batch size 128, and learning rate 0.001 with RMSProp optimizer and 0.5 dropout achieves an accuracy of 97.9% to detect posts as hate speech or free by training with 100 epochs. Which is assured by testing the models using models performance test and inference on user-generated data.

scholarly journals Recurrent neural network-based acute concussion classifier using raw resting state EEG data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karun Thanjavur ◽  
Arif Babul ◽  
Brandon Foran ◽  
Maya Bielecki ◽  
Adam Gilchrist ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Resting State ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Health Concern ◽  
Adolescent Athletes ◽  
Individual Level ◽  
Diffuse Brain Injury ◽  
Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network

2019 ◽  
Vol 20 (9) ◽  
pp. 3283-3293 ◽  
Author(s):  
Junbiao Pang ◽  
Jing Huang ◽  
Yong Du ◽  
Haitao Yu ◽  
Qingming Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Arrival Time
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