scholarly journals Implementation of Deep Learning Predictor (LSTM) Algorithm for Human Mobility Prediction

2020 ◽  
Vol 14 (18) ◽  
pp. 132
Author(s):  
Ida Nurhaida ◽  
Handrie Noprisson ◽  
Vina Ayumi ◽  
Hong Wei ◽  
Erwin Dwika Putra ◽  
...  
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
Mobility Prediction ◽  
Sequential Data ◽  
Short Term ◽  
Large Scale Dataset ◽  
History Of ◽  
Long Short Term Memory ◽  
Trajectory Identification

The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of<em> </em>human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result,<em> </em>the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of <em>epoch</em> and <em>batch_size</em> is 100 and 20 respectively.

scholarly journals CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY

2020 ◽  
Vol V-4-2020 ◽  
pp. 87-94
Author(s):  
S. Miyazawa ◽  
X. Song ◽  
R. Jiang ◽  
Z. Fan ◽  
R. Shibasaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Human Mobility ◽  
Background Information ◽  
Mobility Prediction ◽  
Short Term ◽  
Term Memory ◽  
Mobility Data ◽  
Long Short Term Memory

Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.

scholarly journals Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

scholarly journals Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life

2019 ◽  
Vol 10 (1) ◽  
pp. 69 ◽  
Author(s):  
Peyman Sheikholharam Mashhadi ◽  
Sławomir Nowaczyk ◽  
Sepideh Pashami
Keyword(s):  
Short Term Memory ◽  
Remaining Useful Life ◽  
Short Term ◽  
Diagnostic Models ◽  
History Of ◽  
Useful Life ◽  
Long Short Term Memory ◽  
Low Performance ◽  
Limited Sensing

Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers.

scholarly journals Social Risk Early Warning of Environmental Damage of Large-Scale Construction Projects in China Based on Network Governance and LSTM Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Junmin Fang ◽  
Dechun Huang ◽  
Jingrong Xu
Keyword(s):  
Early Warning ◽  
Construction Projects ◽  
Large Scale ◽  
Short Term Memory ◽  
Network Governance ◽  
Scale Construction ◽  
Environmental Damage ◽  
Social Risk ◽  
Short Term ◽  
Long Short Term Memory

With the improvement of citizens’ risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk media, and big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed， and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China.

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%.

The performance of LSTM models from basin to continental scales

2020 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Günter Klambauer ◽  
Grey Nearing ◽  
Sepp Hochreiter
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Regional Scale ◽  
Data Driven ◽  
Hydrological Models ◽  
Short Term ◽  
Term Memory ◽  
Basin Scale ◽  
Long Short Term Memory

&lt;p&gt;Simulation accuracy among traditional hydrological models usually degrades significantly when going from single basin to regional scale. Hydrological models perform best when calibrated for specific basins, and do worse when a regional calibration scheme is used.&amp;#160;&lt;/p&gt;&lt;p&gt;One reason for this is that these models do not (have to) learn hydrological processes from data. Rather, they have a predefined model structure and only a handful of parameters adapt to specific basins. This often yields less-than-optimal parameter values when the loss is not determined by a single basin, but by many through regional calibration.&lt;/p&gt;&lt;p&gt;The opposite is true for data driven approaches where models tend to get better with more and diverse training data. We examine whether this holds true when modeling rainfall-runoff processes with deep learning, or if, like their process-based counterparts, data-driven hydrological models degrade when going from basin to regional scale.&lt;/p&gt;&lt;p&gt;Recently, Kratzert et al. (2018) showed that the Long Short-Term Memory network (LSTM), a special type of recurrent neural network, achieves comparable performance to the SAC-SMA at basin scale. In follow up work Kratzert et al. (2019a) trained a single LSTM for hundreds of basins in the continental US, which outperformed a set of hydrological models significantly, even compared to basin-calibrated hydrological models. On average, a single LSTM is even better in out-of-sample predictions (ungauged) compared to the SAC-SMA in-sample (gauged) or US National Water Model (Kratzert et al. 2019b).&lt;/p&gt;&lt;p&gt;LSTM-based approaches usually involve tuning a large number of hyperparameters, such as the number of neurons, number of layers, and learning rate, that are critical for the predictive performance. Therefore, large-scale hyperparameter search has to be performed to obtain a proficient LSTM network.&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;However, in the abovementioned studies, hyperparameter optimization was not conducted at large scale and e.g. in Kratzert et al. (2018) the same network hyperparameters were used in all basins, instead of tuning hyperparameters for each basin separately. It is yet unclear whether LSTMs follow the same trend of traditional hydrological models to degrade performance from basin to regional scale.&amp;#160;&lt;/p&gt;&lt;p&gt;In the current study, we performed a computational expensive, basin-specific hyperparameter search to explore how site-specific LSTMs differ in performance compared to regionally calibrated LSTMs. We compared our results to the mHM and VIC models, once calibrated per-basin and once using an MPR regionalization scheme. These benchmark models were calibrated individual research groups, to eliminate bias in our study. We analyse whether differences in basin-specific vs regional model performance can be linked to basin attributes or data set characteristics.&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall&amp;#8211;runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005&amp;#8211;6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.&amp;#160;&lt;/p&gt;&lt;p&gt;Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089&amp;#8211;5110, https://doi.org/10.5194/hess-23-5089-2019, 2019a.&amp;#160;&lt;/p&gt;&lt;p&gt;Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., &amp; Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019b.&lt;/p&gt;

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.

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