Machine learning and IoTs for forecasting prediction of smart road traffic flow

This paper proposes to predict traffic accidents based on IoTs and deep learning to address the current problem of inaccurate traffic accident prediction. Since traditional traffic accident prediction often applies classical prediction algorithms to a small portion of data, the obtained models can only predict a small range of traffic accidents. Most accident prediction models are limited by the lack of data features, do not consider the problems of practical application scenarios, and do not incorporate regional heterogeneity, so the prediction accuracy of accident prediction models is poor. This paper analyzes and summarizes the relationship between traffic accidents and influencing factors from five aspects, such as people, vehicles, roads and environment, and proves the influence of regional heterogeneity on accidents, which paves the way for traffic accident prediction. The data and heterogeneous spatial data are preprocessed and feature selected, respectively. Logistic regression and random forest algorithm are used to train the corresponding prediction models. The results show that the prediction model combined with regional heterogeneity has better comprehensive performance than the original data.

Automated summary

This paper proposes a study on predicting traffic accidents based on IoTs and deep learning to address the problem of current inaccurate traffic accident predictions. Traditional traffic accident prediction methods often apply classical prediction algorithms to a small portion of data, resulting in models that can only predict a limited range of traffic accidents. Most accident prediction models lack data features, do not consider practical application scenarios, and do not incorporate regional heterogeneity, leading to poor prediction accuracy. This paper analyzes and summarizes the relationship between traffic accidents and influencing factors from five aspects, and proves the influence of regional heterogeneity on accidents, paving the way for traffic accident prediction. The data and heterogeneous spatial data are preprocessed and feature selected, respectively. Logistic regression and random forest algorithm are used to train the corresponding prediction models. The results show that the prediction model combined with regional heterogeneity has better comprehensive performance than the original data.