Grey Systems Theory Time Series Prediction applied to Road Traffic Safety in Germany

René, None Sylvain; Becker, Ulrik; Manz, Hansjörg

doi:10.1016/j.ifacol.2016.07.039

Cited by 20 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These goals are difficult to realize by probability statistic method and fuzzy sets method. GSA has been widely used [2][3][4][5][6][7][8][9]. There are many forms of extension [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Determination of Time Delay in Grey Prediction Model with Time Delay

Li¹,

Liao²,

Dong³

2019

ISI

View full text Add to dashboard Cite

This paper attempts to determine the time delay between the sequence of system behaviors and that of influencing factors on system behaviors. Firstly, a new concept was coined called the representative subsequence (RS), and the RSs of influencing factors were selected. Next, a new geometric similarity grey relational grade (GRG) model was set up to compute the time delay of each influencing factor RS relative to the sequence of system behaviors. On this basis, the time-delay corresponding to the maximum GRG was taken as the desired time-delay. The verification on actual data shows that the concept and extraction rule of the RS are feasible, and our geometric similarity GRG model outperforms the conventional model in predicting time delay. The research findings lay the basis for grey prediction modelling of time sequences and optimizing the prediction effect of grey prediction models.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Determination of Time Delay in Grey Prediction Model with Time Delay

Li¹,

Liao²,

Dong³

2019

ISI

View full text Add to dashboard Cite

show abstract

“…Ma et al [21] used a combination of the grey and fuzzy theory to create three different sets of composite road safety indicators by considering judgment attitudes: one related to regional roads, one related to urban roads and one related to highways. René et al [22] applied the grey system with time series to predict the expected number of accidents and relayed results to target the improvement that should be performed and measures that should be conducted. Liu et al [23] used grey relational analysis to calculate the weights for the purpose of constructing a fuzzy road safety indicator.…”

Section: Grey Relational Analysismentioning

confidence: 99%

Construction of Road Safety Composite Indicator Using Grey Relational Analysis

Grdinić-Rakonjac

Antić

Pešić

et al. 2021

PROMET

View full text Add to dashboard Cite

Aggregation of different variables into one road safety performance index is a popular concept in evaluating road safety and comparing the performance of territories/entities. This paper presents the development of a novel and innovative weighting methodology using grey relational analysis. Based on the proposed model, ten hierarchical road safety indicators were selected in terms of a two-layered model with three categories related to behaviour, safety and system. Grey weights are assigned to the categorized indicators in each layer, and the grey road safety composite indicator for each entity (21 selected territories) is calculated by the weighted sum approach. With relatively high weights, this systematic methodology can serve the policy makers in targeting the risk domains where improvements are needed. The results clearly illustrate effectiveness in addressing a large number of indicators with hierarchical structures.

show abstract

“…In addition, based on the GM(1,1) model, the improved and combined grey models were proposed in references [22] to predict traffic accidents and got more effective results than traditional algorithms. Additionally, in René S's work [23], in order to predict the development of road traffic accidents, the grey system theory was applied to predict traffic accident in Germany.…”

Section: Related Workmentioning

confidence: 99%

Traffic Accident Prediction Based on Multivariable Grey Model

Zhao

Liu

2020

Information

View full text Add to dashboard Cite

Owing to frequent traffic accidents and casualties nowadays, the ability to predict the number of traffic accidents in a period is significant for the transportation department to make decisions scientifically. However, owing to many variables affecting traffic accidents in the road traffic system, there are two critical challenges in traffic accident prediction. The first issue is how to evaluate the weight of each variable’s impact on the accident. The second issue is how to model the prediction process for multiple interrelated variables. Aiming to solve these two problems, we propose effective solutions to deal with traffic accident prediction. Firstly, for the first issue, we exploit the grey correlation analysis to measure the correlation of factors to accident occurrence. Then, for the second issue, we select the main factors by correlation analysis to establish a multivariable grey model—MGM(1,N) for prediction process modeling. Further, we explore the collinearity between variables and better optimize the predictive model. The experimental results show that our approach achieves best performance than four general-purpose comparative algorithms in traffic accident prediction task.

show abstract

Grey Systems Theory Time Series Prediction applied to Road Traffic Safety in Germany

Cited by 20 publications

References 8 publications

Adaptive Determination of Time Delay in Grey Prediction Model with Time Delay

Adaptive Determination of Time Delay in Grey Prediction Model with Time Delay

Construction of Road Safety Composite Indicator Using Grey Relational Analysis

Traffic Accident Prediction Based on Multivariable Grey Model

Contact Info

Product

Resources

About