Research on Algorithms of Grey Residual Error Model in Highway Traffic Accidents Forecast

Li, Gang; Xi-yue, Huang; Liao, Shili; Han, Fengqing; Yuan, Rongdi

doi:10.1142/9789812701695_0101

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing with Japan, the ownership of country car was more than 7,000, traffic accidents up to three times in China, while the death toll was only 4611 people. In the United States across the Atlantic, the ownership of the car was 285 million, the number of car accidents killed only 42,000 people [2]. China's road traffic development is facing the bottleneck period.…”

Section: Research Backgroundmentioning

confidence: 99%

Analysis and Forecast of Traffic Accident Big Data

Chen¹

2017

ITM Web Conf.

View full text Add to dashboard Cite

Nowadays, as traffic accidents keep happening, traffic safety has become a major focus of contemporary social issues. Many factors account for traffic accidents, such as accident location, time period, driver's feelings, weather and other uncertain complex factors. As a result, the occurrence of traffic accidents is nonlinear, so it is necessary to explore the correlation between the data from many different aspects so as to avoid risks. By analyzing traffic data and graphics, R language shows how the data is related. After data preprocess, data selection by using R language Remap package remapB and remapH function, we get the locations of the accidents and the accident thermal chart, where you can find high-frequency accident locations. Besides, we employ decision tree, linear regression, random forest algorithm to model the data. According to the actual results, we can verify the correctness of the model and get the most accurate model and it can help us to predict this model with similar data in the future. The ultimate goal of data analysis is to choose the most accurate model after validating the model, analyzing the characteristics of the data and the relationship between the model and the data.

show abstract

Section: Research Backgroundmentioning

confidence: 99%

Analysis and Forecast of Traffic Accident Big Data

Chen¹

2017

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…Multi-scale analysis is the nature of the wavelet coefficients C projected onto the multi-scale space, low-frequency part of the signal [3] decomposition continue to realize the signal gradually from coarse to fine observation scale decomposition diagram as shown: Multi-scale degrees of freedom equations of motion for the damped structureis:…”

Section: Introductionmentioning

confidence: 99%

Study on Structural Damage Identification Based on Wavelet Analysis

Zhang

Liu

2011

AMR

View full text Add to dashboard Cite

This papers deals with wavelet analysis which is as a tool for the complexity of structural damage combing the multi-scale theory and pattern recognition idea. The main subject is as follows multi-scale damage information analysis and numerical simulation and model experiments which is based on wavelet analysis for structural damage identification study. Applying wavelet transform, differential eqution of structural dynamic system are decomposed and dynamic parameters are described on different scales, the dentification of structure damage location is also spatial domain information which determines in the structural damage.

show abstract

Research on Algorithms of Grey Residual Error Model in Highway Traffic Accidents Forecast

Cited by 2 publications

References 0 publications

Analysis and Forecast of Traffic Accident Big Data

Analysis and Forecast of Traffic Accident Big Data

Study on Structural Damage Identification Based on Wavelet Analysis

Contact Info

Product

Resources

About