Traffic Accident Prediction Based on Multivariable Grey Model

Abstract

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.

Exploration of the application of Grey-Markov models in the causality analysis of traffic accidents in roundabouts

“…A hybrid approach combining the theory of grey systems and Markov chains has been used to predict roundabout accidents. Time series data from 2016 to 2021 were fitted using the GM(1,1) model [ 47 ], MGM(1,4) model [ 48 ], and hybrid MGM(1,4)-Markov model, as shown in Fig 4 .…”

Implementasi Metode Grey Verhulst untuk Mendukung Kebijakan dalam Mengantisipasi Mahasiswa Dropout

“…Grey Model memiliki keunggulan tidak memerlukan data yang besar, menawarkan operasi yang mudah dan dapat menggunakan data acak untuk memberikan prediksi yang akurat. bahkan dalam penelitian [8] (Grey model hanya membutuhkan satu variable dalam memprediksi harga emas). Tetapi grey model memiliki kekurangan dalam adaptasi perubahan data [9].…”

A road traffic accidents prediction model for traffic service robot

“…Traffic accident predictions are mainly divided into macro and micro predictions. Among them, macro predictions predict the total amount of road traffic accidents or the total number of casualties of a country or a region based on various macro indicators of a country or region over the years (Cheng et al , 2019; Li et al , 2020; Omidi et al , 2019). And micro prediction refers to taking specific traffic accidents as samples and using appropriate models and methods to further achieve the purpose of predicting the severity of traffic accidents.…”

“…The prediction effect is better than the single model of RF. In general, compared to some research studies (Cheng et al , 2019; Li et al , 2020; Omidi et al , 2019), a fusion model to predict the severity of traffic accidents is proposed, that is, a micro prediction. In terms of micro prediction, through real-time monitoring of relevant elements, the application of social robots can provide more valuable and useful tips and suggestions for specific drivers based on weather conditions, their driver age and driver driving habits, road environment, traffic flow and other information.…”