Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

Traffic Flow Prediction with Heterogenous Data Using a Hybrid CNN-LSTM Model

Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast performance.On the other hand,the availability of spatiotemporal data is limited.This research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.

Digital Development Rights in Developing Countries:Where the Governance Rules for Cross-Border Data Flows

The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.

Functionality Semantics of Predicate Data Flow Diagram

SOZL (structured methodology + object-oriented methodology + Z language) is a language that attempts to integrate structured method, object-oriented method and formal method. The core of this language is predicate data flow diagram (PDFD). In order to eliminate the ambiguity of predicate data flow diagrams and their associated textual specifications, a formalization of the syntax and semantics of predicate data flow diagrams is necessary. In this paper we use Z notation to define an abstract syntax and the related structural constraints for the PDFD notation, and provide it with an axiomatic semantics based on the concept of data availability and functionality of predicate operation. Finally, an example is given to establish functionality consistent decomposition on hierarchical PDFD (HPDFD).

A New Synthetical Knowledge Representation Model and Its Application in Data Flow Diagram

A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of syntax and semantic rules suitable for a semantic network. Based on the model,the paper introduces a formal method defining data flow diagrams (DFD) and also simply explains how to use the method.

Determination of Curie Point Depth and Heat Flow Using Airborne Magnetic Data over the Kom-Ombo and Nuqra Basins, Southern Eastern Desert, Egypt

The Kom-Ombo and Nuqra basins in southern Egypt have recently been discovered as potential hydrocarbon basins. The lack of information about the geothermal gradient and heat flow in the study area gives importance to studying the heat flow and the geothermal gradient. Several studies were carried out to investigate the geothermal analyses of the northwestern desert, as well as the west and east of the Nile River, using density, compressive wave velocity, and bottom hole temperature (BHT) measured from deep oil wells. This research relies on spectral analysis of airborne magnetic survey data in the Kom-Ombo and Nuqra basins in order to estimate the geothermal gradient based on calculating the depth to the bottom of the magnetic source that caused the occurrence of these magnetic deviations. This depth is equal to the CPD, at which the material loses its magnetic polarisation. This method is fast and gives satisfactory results. Usually, it can be applied as a reconnaissance technique for geothermal exploration targets due to the abundance of magnetic data. The depth of the top (Zt) and centroid (Z0) of the magnetic source bodies was calculated for the 32 windows representing the study area using spectral analysis of airborne magnetic data. The curie-isotherm depth, geothermal gradient, and heat flow maps were constructed for the study area. The results showed that the CPD in the study area ranges from 13 km to 20 km. The heat flow map values range from 69 to 109 mW/m2, with an average of about 80 mW/m2. The calculated heat flow values in the assigned areas (A, B, C, and D) of the study area are considered to have high heat flow values, reaching 109 mW/m2. On the other hand, the heat flow values in the other parts range from 70 to 85 mW/m2. Since heat flow plays an essential role in the maturation of organic matter, it is recommended that hydrocarbon accumulations be located in places with high heat flow values, while deep drilling of hydrocarbon wells is recommended in places with low to moderate heat flow values.

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

Stochastic Analysis and Modeling of Velocity Observations in Turbulent Flows

Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.

Fatigue Safety Assessment of Concrete Continuous Rigid Frame Bridge Based on Rain Flow Counting Method and Health Monitoring Data

The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects,this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge,which combines long-term in-site monitoring data collected by the health monitoring system(HMS)and fatigue theory.In the paper,the authors mainly carry out the research work in the following aspects:First of all,a long-span continuous rigid frame bridge installed with HMS is used as an example,and a large amount of health monitoring data have been acquired,which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles;next,for calculating the cumulative fatigue damage of the bridge structure,fatigue stress spectrum got by rain flow counting method,S-N curves and damage criteria are used for fatigue damage analysis.Moreover,it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles.The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress.The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well,and also can help bridge engineers do the assessment of future service duration.

DFDVis:A Visual Analytics System for Understanding the Semantics of Data Flow Diagram

Data flow diagram(DFD),as a special kind of data,is a design artifact in both requirement analysis and structured analysis in software development.However,rigorous analysis of DFD requires a formal semantics.Formal representation of DFD and its formal semantics will help to reduce inconsistencies and confusion.The logical structure of DFD can be described using formalism of Calculus of Communicating System(CCS).With a finite number of states based on CCS,state space methods will help a lot in analysis and verification of the behavior of the systems.But the number of states of even a relatively small system is often very great that is called state explosion.In this paper,we present a visual system which combines Formal methods and visualization techniques so as to help the researchers to understand and analyze the system described by the DFD regardless of the problem of state explosion.

INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION

Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.

Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet

A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.

Traffic flow prediction based on BILSTM model and data denoising scheme

Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.

A Testing Method for Web Services Composition Based on Data-Flow

This paper proposes a method of data-flow testing for Web services composition. Firstly, to facilitate data flow analysis and constraints collecting, the existing model representation of business process execution language （BPEL） is modified in company with the analysis of data dependency and an exact representation of dead path elimination （DPE） is proposed, which over-comes the difficulties brought to dataflow analysis. Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language （WSDL） documents and the def-use annotated control flow graph is created. Based on this model, data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph, and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated, then testers can design the test cases according to the collected constraints for each path selected.

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data： An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

A multi-source data fusion modeling method for debris flow prevention engineering

The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.

Study on the Deconvolution Method and Processing Flow of Airgun Source Data

With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source signals. To some extent,deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking,in order to remove the airgun source wavelet signal and obtain the Green's functions between the airgun source and stations,we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions,etc. We use the Binchuan( in Yunnan Province,China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency;time domain iterative deconvolution is better in terms of the signal-to-noise ratio( SNR),and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally,we propose a general processing flow for the airgun source data to extract the Green 's functions between the airgun source and stations.

Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

A New Automated Method and Sample Data Flow for Analysis of Volatile Nitrosamines in Human Urine

Volatile nitrosamines (VNAs) are a group of compounds classified as probable (group 2A) and possible (group 2B) carcinogens in humans. Along with certain foods and contaminated drinking water, VNAs are detected at high levels in tobacco products and in both mainstream and side-stream smoke. Our laboratory monitors six urinary VNAs—N-nitrosodimethylamine (NDMA), N-nitrosomethylethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosopiperidine (NPIP), N-nitrosopyrrolidine (NPYR), and N-nitrosomorpholine (NMOR)—using isotope dilution GC-MS/ MS (QQQ) for large population studies such as the National Health and Nutrition Examination Survey (NHANES). In this paper, we report for the first time a new automated sample preparation method to more efficiently quantitate these VNAs. Automation is done using Hamilton STARTM and Caliper StaccatoTM workstations. This new automated method reduces sample preparation time from 4 hours to 2.5 hours while maintaining precision (inter-run CV < 10%) and accuracy (85% - 111%). More importantly this method increases sample throughput while maintaining a low limit of detection (<10 pg/mL) for all analytes. A streamlined sample data flow was created in parallel to the automated method, in which samples can be tracked from receiving to final LIMs output with minimal human intervention, further minimizing human error in the sample preparation process. This new automated method and the sample data flow are currently applied in bio-monitoring of VNAs in the US non-institutionalized population NHANES 2013-2014 cycle.