scholarly journals A Methodology for Signal Timing Estimation Based on Low Frequency Floating Car Data: Analysis of Needed Sample Sizes and Influencing Factors

2016 ◽  
Vol 15 ◽  
pp. 220-232 ◽  
Author(s):  
Steffen Axer ◽  
Bernhard Friedrich
Keyword(s):  
Data Analysis ◽  
Influencing Factors ◽  
Low Frequency ◽  
Signal Timing ◽  
Sample Sizes ◽  
Floating Car Data ◽  
Timing Estimation

scholarly journals Signal timing estimation based on low frequency floating car data

2017 ◽  
Vol 25 ◽  
pp. 1645-1661 ◽  
Author(s):  
Steffen Axer ◽  
Bernhard Friedrich
Keyword(s):  
Low Frequency ◽  
Signal Timing ◽  
Floating Car Data ◽  
Timing Estimation

Low Frequency DAS Data Study with Integrated Data Analysis for Monitoring Hydraulic Fracture

2020 ◽  
Author(s):  
M. Ichikawa ◽  
M. Kato ◽  
S. Uchida ◽  
K. Tamura ◽  
A. Kato ◽  
...  
Keyword(s):  
Data Analysis ◽  
Hydraulic Fracture ◽  
Low Frequency ◽  
Integrated Data Analysis

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

scholarly journals Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

2017 ◽  
Vol 6 (8) ◽  
pp. 253 ◽  
Author(s):  
Chaoyang Shi ◽  
Bi Chen ◽  
Qingquan Li
Keyword(s):  
Travel Time ◽  
Road Networks ◽  
Low Frequency ◽  
Urban Road ◽  
Floating Car Data

Identification of influencing factors for suicidal ideation and suicide attempts among adolescents: 11-year national data analysis for 788,411 participants

2020 ◽  
Vol 291 ◽  
pp. 113228
Author(s):  
Seung Chan Jeong ◽  
Joo Young Kim ◽  
Min Hwan Choi ◽  
Ju Suk Lee ◽  
Jun Hwa Lee ◽  
...  
Keyword(s):  
Data Analysis ◽  
Suicidal Ideation ◽  
Influencing Factors ◽  
Suicide Attempts ◽  
National Data

scholarly journals An Approach for Reconstruction of Realistic Economic Data Based on Frequency Characteristics between IMFs

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Du ◽  
Hao Gong ◽  
Suyue Han ◽  
Peng Zheng ◽  
Bin Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Low Frequency ◽  
Test Method ◽  
Series Data ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Economic Data ◽  
Index Method

Reconstruction of realistic economic data often causes social economists to analyze the underlying driving factors in time-series data or to study volatility. The intrinsic complexity of time-series data interests and attracts social economists. This paper proposes the bilateral permutation entropy (BPE) index method to solve the problem based on partly ensemble empirical mode decomposition (PEEMD), which was proposed as a novel data analysis method for nonlinear and nonstationary time series compared with the T-test method. First, PEEMD is extended to the case of gold price analysis in this paper for decomposition into several independent intrinsic mode functions (IMFs), from high to low frequency. Second, IMFs comprise three parts, including a high-frequency part, low-frequency part, and the whole trend based on a fine-to-coarse reconstruction by the BPE index method and the T-test method. Then, this paper conducts a correlation analysis on the basis of the reconstructed data and the related affected macroeconomic factors, including global gold production, world crude oil prices, and world inflation. Finally, the BPE index method is evidently a vitally significant technique for time-series data analysis in terms of reconstructed IMFs to obtain realistic data.

Non-parametric estimation of route travel time distributions from low-frequency floating car data

2015 ◽  
Vol 58 ◽  
pp. 343-362 ◽  
Author(s):  
Mahmood Rahmani ◽  
Erik Jenelius ◽  
Haris N. Koutsopoulos
Keyword(s):  
Travel Time ◽  
Low Frequency ◽  
Parametric Estimation ◽  
Floating Car Data ◽  
Non Parametric Estimation ◽  
Non Parametric

scholarly journals VEHICLE TRAJECTORY BASED CONTROL DELAY ESTIMATION AT INTERSECTIONS USING LOW-FREQUENCY FLOATING CAR SAMPLING DATA

Transport ◽  
2020 ◽  
Vol 35 (5) ◽  
pp. 523-532 ◽  
Author(s):  
Hua Wang ◽  
Changlong Gu
Keyword(s):  
Probability Model ◽  
Estimation Method ◽  
Low Frequency ◽  
Reference Method ◽  
Geometric Probability ◽  
Delay Estimation ◽  
Peak Period ◽  
Control Delay ◽  
Floating Car Data ◽  
Vehicle Trajectory

Control delay is an important parameter that is used in the optimization of traffic signal timings and the estimation of the level of service at signalized intersection. However, it is also a parameter that is very difficult to estimate. In recent years, floating car data has emerged as an important data source for traffic state monitoring as a result of high accuracy, wide coverage and availability regardless of meteorological conditions, but has done little for control delay estimation. This article proposes a vehicle trajectory based control delay estimation method using low-frequency floating car data. Considering the sparseness and randomness of low-frequency floating car data, we use historical data to capture the deceleration and acceleration patterns. Combined with the low-frequency samples, the spatial and temporal ranges where a vehicle starts to decelerate and stop accelerating are calculated. These are used together with the control delay probability distribution function obtained based on the geometric probability model, to calculate the expected value of the control delay for each vehicle. The proposed method and a reference method are compared with the truth. The results show that the proposed method has a root mean square error of 11.8 s compared to 13.7 s for the reference method for the peak period. The corresponding values for the off-peak period are 9.3 s and 12.5 s. In addition to better accuracy, the mean and standard deviation statistics show that the proposed method outperforms the reference method and is therefore, more reliable. This successful estimation of control delay from sparse data paves the way for a more widespread use of floating car data for monitoring the state of intersections in road networks.

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