scholarly journals Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 420
Author(s):  
Yan Yan ◽  
Bingqian Wang ◽  
Quan Z. Sheng ◽  
Adnan Mahmood ◽  
Tao Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Temporal Correlation ◽  
Data Publishing ◽  
Release Time ◽  
Discrete Wavelet ◽  
Learning Mechanisms ◽  
Location Data ◽  
Spatio Temporal

Centralized publishing of big location data can provide accurate and timely information to assist in traffic management and for facilitating people to decide travel time and route, mitigate traffic congestion, and reduce unnecessary waste. However, the spatio-temporal correlation, non-linearity, randomness, and uncertainty of big location data make it impossible to decide an optimal data publishing instance through traditional methods. This paper, accordingly, proposes a publishing interval predicting method for centralized publication of big location data based on the promising paradigm of deep learning. First, the adaptive adjusted sampling method is designed to address the challenge of finding a reasonable release time via a prediction mechanism. Second, the Maximal Overlap Discrete Wavelet Transform (MODWT) is introduced for the decomposition of time series in order to separate different features of big location data. Finally, different deep learning models are selected to construct the entire framework according to various time-domain features. Experimental analysis suggests that the proposed prediction scheme is not only feasible, but also improves the prediction accuracy in contrast to the traditional deep learning mechanisms.

scholarly journals Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

2021 ◽  
Author(s):  
Ze Ren Luo ◽  
Yang Zhou ◽  
Yu Xing Li ◽  
Liang Guo ◽  
Juan Juan Tuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Oil And Gas ◽  
Nonlinear Problems ◽  
Temporal Correlation ◽  
Learning Method ◽  
Correlation Clustering ◽  
Oil And Gas Exploration ◽  
Original Curve ◽  
Spatio Temporal ◽  
Sedimentary Microfacies

Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Jinghan Du ◽  
Haiyan Chen ◽  
Weining Zhang
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Data Recovery ◽  
Series Data ◽  
Learning Method ◽  
Content Type ◽  
Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

scholarly journals Identifying the most influential roads based on traffic correlation networks

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Shengmin Guo ◽  
Dong Zhou ◽  
Jingfang Fan ◽  
Qingfeng Tong ◽  
Tongyu Zhu ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Temporal Correlation ◽  
Correlation Network ◽  
Traffic Flows ◽  
Weighted Degree ◽  
Correlation Networks ◽  
Spatio Temporal ◽  
Smart Traffic ◽  
The Impact ◽  
Impact Distance

Abstract Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows.

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

scholarly journals Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 705 ◽  
Author(s):  
Qiaomu Zhu ◽  
Jinfu Chen ◽  
Lin Zhu ◽  
Xianzhong Duan ◽  
Yilu Liu
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Temporal Correlation ◽  
Learning Approach ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Spatio Temporal

scholarly journals Modeling Spatio-Temporal Evolution of Urban Crowd Flows

2019 ◽  
Vol 8 (12) ◽  
pp. 570 ◽  
Author(s):  
Kun Qin ◽  
Yuanquan Xu ◽  
Chaogui Kang ◽  
Stanislav Sobolevsky ◽  
Mei-Po Kwan
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Moving Objects ◽  
Morphological Changes ◽  
Morphological Evolution ◽  
Analytical Framework ◽  
Real World Data ◽  
Evolutionary Patterns ◽  
Mobility Data ◽  
Spatio Temporal

Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed.

scholarly journals A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering

2019 ◽  
Vol 8 (8) ◽  
pp. 345 ◽  
Author(s):  
Jian Dong ◽  
Bin Chen ◽  
Lingnan He ◽  
Chuan Ai ◽  
Fang Zhang ◽  
...  
Keyword(s):  
Traffic Management ◽  
Structural Characteristics ◽  
Flow Characteristics ◽  
Urban Traffic ◽  
The Public ◽  
Location Data ◽  
Points Of Interest ◽  
Spatio Temporal ◽  
Traffic Management System ◽  
Temporal Flow

With the advantages of convenient access and free parking, urban dockless shared bikes are favored by the public. However, the irregular flow of dockless shared bikes poses a challenge for the research of flow pattern. In this paper, the flow characteristics of dockless shared bikes are expounded through the analysis of the time series location data of ofo and mobike shared bikes in Beijing. Based on the analysis, a model called DestiFlow is proposed to describe the spatio-temporal flow of urban dockless shared bikes based on points of interest (POIs) clustering. The results show that the DestiFlow model can find the aggregation areas of dockless shared bikes and describe the structural characteristics of the flow network. Our model can not only predict the demand for dockless shared bikes, but also help to grasp the mobility characteristics of citizens and improve the urban traffic management system.

scholarly journals Formation of the Traffic Flow Rate under the Influence of Traffic Flow Concentration in Time at Controlled Intersections in Tyumen, Russian Federation

2021 ◽  
Vol 13 (15) ◽  
pp. 8324
Author(s):  
Viacheslav Morozov ◽  
Sergei Iarkov
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Traffic Congestion ◽  
Traffic Management ◽  
System Analysis ◽  
Motor Vehicle ◽  
Experimental Studies ◽  
Transport Systems ◽  
Experiment Planning ◽  
Traffic Light

Present experience shows that it is impossible to solve the problem of traffic congestion without intelligent transport systems. Traffic management in many cities uses the data of detectors installed at controlled intersections. Further, to assess the traffic situation, the data on the traffic flow rate and its concentration are compared. Latest scientific studies propose a transition from spatial to temporal concentration. Therefore, the purpose of this work is to establish the regularities of the influence of traffic flow concentration in time on traffic flow rate at controlled city intersections. The methodological basis of this study was a systemic approach. Theoretical and experimental studies were based on the existing provisions of system analysis, traffic flow theory, experiment planning, impulses, probabilities, and mathematical statistics. Experimental data were obtained and processed using modern equipment and software: Traficam video detectors, SPECTR traffic light controller, Traficam Data Tool, SPECTR 2.0, AutoCad 2017, and STATISTICA 10. In the course of this study, the authors analyzed the dynamics of changes in the level of motorization, the structure of the motor vehicle fleet, and the dynamics of changes in the number of controlled intersections. As a result of theoretical studies, a hypothesis was put forward that the investigated process is described by a two-factor quadratic multiplicative model. Experimental studies determined the parameters of the developed model depending on the directions of traffic flow, and confirmed its adequacy according to Fisher’s criterion with a probability of at least 0.9. The results obtained can be used to control traffic flows at controlled city intersections.

Sign in / Sign up

Export Citation Format

Share Document