Detrended cross-correlation analysis of urban traffic congestion and NO 2 concentrations in Chengdu

Shi, Kai; Di, Baofeng; Zhang, Kaishan; Feng, Chenglian; Svirchev, Laurence

doi:10.1016/j.trd.2016.12.012

Cited by 52 publications

(25 citation statements)

References 30 publications

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“…DCCA is used to analyze the correlation between two non-stationary time series, and has been widely used in finance, atmosphere, physiology, and other fields [23,24,25,26]. In this paper, the electrostatic gait signals of hemiplegic patients and healthy control subjects are analyzed with the DCCA method, and the logarithmic curve of the cross-correlation fluctuation function and the scale of the electrostatic gait signals are obtained, as shown in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…The Detrended Cross-Correlation Analysis (DCCA) method proposed by Podobnik and Stanley [22] was first used to calculate the cross-correlation between two non-stationary time series. It has been widely used in financial [23,24], atmospheric [25], physiological, and other fields [26]. However, the single scaling index in the DCCA results still has similar inadequacies to DFA; that is, only a single parameter is used to describe the sequence characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

Tian

Wang

et al. 2019

Sensors

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Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

Tian

Wang

et al. 2019

Sensors

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“…e dataset is collected by users' mobile phones and includes the information of order ID, driver ID, start time, end time, start location, and end location. e dataset describes the city of Chengdu, a super city with high traffic volume, emission, and energy consumption [20]. e date range of the dataset is from November 2, 2016, to November 30, 2016, without November 10, 2016. ere is also an obvious data vacancy on November 8.…”

Section: Case Studymentioning

confidence: 99%

GPS Data in Urban Online Car-Hailing: Simulation on Optimization and Prediction in Reducing Void Cruising Distance

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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Ride-hailing, as a popular shared-transportation method, has been operated in many areas all over the world. Researchers conducted various researches based on global cases. They argued on whether car-hailing is an effective travel mode for emission reduction and drew different conclusions. The detailed emission performance of the ride-hailing system depends on the cases. Therefore, there is an urgent demand to reduce the overall picking up distance during the dispatch. In this study, we try to satisfy this demand by proposing an optimization method combined with a prediction model to minimize the global void cruising distance when solving the dispatch problem. We use Didi ride-hailing data on one day for simulation and found that our method can reduce the picking up distance by 7.51% compared with the baseline greedy algorithm. The proposed algorithm additionally makes the average waiting time of passengers more than 4 minutes shorter. The statistical results also show that the performance of our method is stable. Almost the metric in all cases can be kept in a low interval. What is more, we did a day-to-day comparison. We found that, despite the different spatial-temporal distribution of orders and drivers on different day conditions, there are little differences in the performance of the method. We also provide temporal analysis on the changing pattern of void cruising distance and quantity of orders on weekdays and weekends. Our findings show that our method can averagely reduce more void cruising distance when ride-hailing is active compared with the traditional greedy algorithm. The result also shows that the method can stably reduce void cruising distance by about 4000 to 5000 m per order across one day. We believe that our findings can improve deeper insight into the mechanism of the ride-hailing system and contribute to further studies.

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“…It is subjected to solar radiation and its precursors which are nitrogen oxides (NOx), volatile organic compounds (VOCs), and carbon monoxide (CO) [12][13][14][15][16][17][18][19][20][21]. Nitrogen oxide and carbon monoxide are mainly from vehicle exhaust emissions and fossil fuel burning [22][23][24]. Concentration distributions of volatile organic compounds are associated with biomass burning, fossil fuel burning, process emission, solvent source, and traffic emission [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Ground-Level Ozone from 2015 to 2018 in BTH Area, China

et al. 2020

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With the ground-level ozone pollution problem increasingly prominent in recent years in China, it is particularly important in basic researches on ozone contamination characteristics. In this study, 13 cities in Beijing-Tianjin-Hebei (BTH) area were examined to determine the characteristics of surface ozone (O3) from 2015 to 2018. Due to the photochemical oxidation of ozone precursors (such as nitrogen oxides and carbon monoxide) along with the presence of sunlight and characteristics of local emission sources, the O3 and oxidant (OX) concentrations showed obvious seasonal variation and daily variation. It implicated that the O3 concentrations reached the maximum during summer. The concentrations of O3 were higher at daytime than those measured at nighttime. The ozone weekend effect was estimated by the difference and deviation, which exhibited that the difference between weekday and weekend were related to the concentrations of ozone precursors and PM, vehicle emissions, and solar radiation. Moreover, the O3 concentrations decreased with the increase of other air pollutants by correlation analysis. The ozone pollution was easily formed at light and moderate polluted periods when compared to other air quality levels.

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Detrended cross-correlation analysis of urban traffic congestion and NO 2 concentrations in Chengdu

Cited by 52 publications

References 30 publications

Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

GPS Data in Urban Online Car-Hailing: Simulation on Optimization and Prediction in Reducing Void Cruising Distance

Characteristics of Ground-Level Ozone from 2015 to 2018 in BTH Area, China

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