Fractal Dimension in Time Series of Expressway Traffic Flow

2013 ◽  
Vol 838-841 ◽  
pp. 2088-2091
Author(s):  
Wei Zhang ◽  
Xin He
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Traffic Flow ◽  
Flow Rate ◽  
Fractal Theory ◽  
Operation Management ◽  
Flow State ◽  
Flow Data ◽  
Input Sample ◽  
Statistical Results

The correlation and Hausdroff dimensions of traffic flow and speed series are analyzed based on the Fractal theory. Using the traffic flow data of four typical expressways in China as input sample, the data statistical results indicates that both synchronized and free flow state are fractal and chaotic. In addition, traffic flow rate and speed time series are determined by different intervals and then G-P arithmetic is applied to estimate their correlation dimensions and the Hausdroff dimension. The results also illustrates that Fractal dimension could by clearly identified, which provides a useful tool for expressway operation management and facilities provision.

Runoff Fractal Dimension of Songhua River Basin in Harbin Station Based on Db4 Wavelet

2012 ◽  
Vol 550-553 ◽  
pp. 2537-2540
Author(s):  
Hai Yan Gu ◽  
Yong Wang ◽  
Lei Yu
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
River Basin ◽  
Fractal Theory ◽  
Measurement Methods ◽  
Self Similarity ◽  
Songhua River ◽  
Songhua River Basin ◽  
The Songhua River Basin ◽  
The Songhua River

The wavelet analysis and fractal theory into the analysis of hydrological time series, fluctuations in hydrological runoff sequence given the complexity of the measurement methods--- fractal dimension. The real monthly runoffs of 28 years from Songhua River basin in Harbin station are selected as research target. Wavelet transform combined with spectrum method is used to calculate the fractal dimension of runoff. Moreover, the result demonstrates that the runoff in Songhua River basin has the characteristic of self-similarity, and the complexity of runoff in the Songhua River basin in Harbin station is described quantificationally.

scholarly journals Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

2020 ◽  
Vol 11 ◽  
Author(s):  
Shahul Mujib Kamal ◽  
Mohammad Hossein Babini ◽  
Ondrej Krejcar ◽  
Hamidreza Namazi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Fractal Dimension ◽  
Daily Life ◽  
Fractal Theory ◽  
Sample Entropy ◽  
Physiological Signals ◽  
Everyday Activity ◽  
First Time

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.

scholarly journals Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Qichun Bing ◽  
Ciyun Lin ◽  
Nan Yang ◽  
Duo Mei
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Similarity Search ◽  
Prediction Method ◽  
Flow Data ◽  
Short Term ◽  
Similar Time ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Advanced Traffic Management System

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

scholarly journals A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Huiming Duan ◽  
Xinping Xiao
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Prediction Models ◽  
Transportation Systems ◽  
Flow State ◽  
Matrix Perturbation ◽  
Grey Prediction ◽  
Flow Data ◽  
Short Term ◽  
Proposed Model

Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

A FRACTAL MODEL FOR GAS APPARENT PERMEABILITY IN MICROFRACTURES OF TIGHT/SHALE RESERVOIRS

Fractals ◽  
2017 ◽  
Vol 25 (03) ◽  
pp. 1750036 ◽  
Author(s):  
SHIFANG WANG ◽  
TAO WU ◽  
XIUYING CAO ◽  
QIUSHA ZHENG ◽  
MIN AI
Keyword(s):  
Fractal Dimension ◽  
Flow Rate ◽  
Molecular Diffusion ◽  
Ambient Pressure ◽  
Fractal Theory ◽  
Fractal Model ◽  
Gas Production ◽  
Apparent Permeability ◽  
Microstructural Parameters ◽  
Shale Reservoirs

The investigation of gas transport in microfractures of tight/shale reservoirs can provide potential applications in predicting shale gas production rates. In this paper, analytical expressions for flow rate and apparent permeability are derived based on the fractal theory and the superposition of convection and molecular diffusion transfer. The proposed model relates the flow rate and apparent permeability to the microstructural parameters of tight/shale reservoirs, gas properties, the ambient pressure as well as temperature. The model predictions from the present model are compared with existing experimental data sets and are found to be consistent with existing experimental measurements. The effects of microstructural parameters of tight/shale reservoirs on apparent permeability are also investigated. The results show that apparent permeability increases with temperature, the pore area fractal dimension, the porosity as well as the maximum microfracture width and decreases with the tortuosity fractal dimension and the mean pressure.

scholarly journals Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Huiming Duan ◽  
Xinping Xiao ◽  
Lingling Pei
Keyword(s):  
Mechanical Properties ◽  
Traffic Flow ◽  
Dynamic Characteristics ◽  
Flow System ◽  
Structural Parameters ◽  
Traffic Information ◽  
Gray Model ◽  
Flow State ◽  
Flow Data ◽  
Short Term

The traffic-flow system has basic dynamic characteristics. This feature provides a theoretical basis for constructing a reasonable and effective model for the traffic-flow system. The research on short-term traffic-flow forecasting is of wide interest. Its results can be applied directly to advanced traffic information systems and traffic management, providing real-time and effective traffic information. According to the dynamic characteristics of traffic-flow data, this paper extends the mechanical properties, such as distance, acceleration, force combination, and decomposition, to the traffic-flow data vector. According to the mechanical properties of the data, this paper proposes four new models of structural parameters and component parameters, inertia nonhomogenous discrete gray models (referred to as INDGM), and analyzes the important properties of the model. This model examines the construction of the inertia nonhomogenous discrete gray model from the mechanical properties of the data, explaining the classic NDGM modeling mechanism in the meantime. Finally, this paper analyzes the traffic-flow data of Whitemud Drive in Canada and studies the relationship between the inertia model and the traffic-flow state according to the data analysis of the traffic-flow state. A simulation accuracy and prediction accuracy of up to 0.0248 and 0.0273, respectively, are obtained.

scholarly journals Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Jacqueline Nyirajana ◽  
Akinwale Oladotun Coker ◽  
Folake Olubunmi Akintayo
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Critical Density ◽  
Maximum Flow ◽  
Carbon Dioxide Production ◽  
Flow State ◽  
Fundamental Diagram ◽  
Essential Facility ◽  
Time Scheduling ◽  
Road Planning

Traffic flow study plays a key important in various functionalities of cities all over the world. The study of traffic flow is also viewed as an essential facility of the country when it wants to establish traffic operations patterns in the progress of road planning. Blockages are accountable for a sequence of harmful effects such as loss of time, scheduling difficulties, carbon dioxide production, and risks of accidents as well as external expenses. Besides, inadequate transportation facilities and increased traffic volume hamper urban development, especially in developing nations. The objective of the study was to assess the traffic flow state in two selected national roads of Kigali city. The traffic data were collected from 5 am to 8 pm on two National Roads (RN1 and RN3).  The relationship between density and flow rate was analyzed using the fundamental diagram of traffic flow. It was found that the peak hours were from 6 am to 8 am and 5 pm to 8 pm. The highest number of vehicles counted were motorcycles due to shortcuts taken to reduce travel time. The results on RN3 revealed a proportion increase of traffic flow and density in the free-flow regime from 0 to maximum flow of 3346.6 veh/h correspondent to a critical density of 114.9 veh/km. However, in the congested zone regime, there was a decrease in traffic flow and an increase in density. It was found that the curve of flow versus density tended to increase on-road RN1. This study proposed the promotion of public transport and e-commerce as strategies to mitigate the congestion. Also, further research may be carried out on all roads of Kigali city, to provide the level of congestion useful for traffic deviation accordingly.

scholarly journals Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

2021 ◽  
Vol 13 (19) ◽  
pp. 10595
Author(s):  
Yan Zheng ◽  
Chunjiao Dong ◽  
Daiyue Dong ◽  
Shengyou Wang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Traffic Flow ◽  
Temporal Correlation ◽  
Learning Model ◽  
Flow Data ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Deep Learning Model

In this paper, a fusion deep learning model considering spatial–temporal correlation is proposed to solve the problem of urban road traffic flow prediction. Firstly, this paper holds that the traffic flow of a section in the urban road network not only depends on the fluctuation of its own time series, but is also related to the traffic flow of other sections in the whole region. Therefore, a traffic flow similarity measurement method based on wavelet decomposition and dynamic time warping is proposed to screen the sections which are similar to the traffic flow state of the target section. Secondly, in order to improve the prediction accuracy, the unstable time series are reconstructed into stationary time series by differential method. Finally, taking the extracted traffic flow data of a similar section as an independent variable and the traffic flow data of target section as dependent variable, we input the above variables into the proposed CNN-LSTM fusion deep learning model for traffic flow prediction. The results show that the proposed model has a higher accuracy and stability than the other benchmark models. The MAPE can reach 92.68%, 93.39%, 85.14%, and 76.14% at a time interval of 5 min, 15 min, 30 min, and 60 min, and the other evaluation indexes are also better than the rest of the benchmark models.

The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State
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