# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 1 Introduction
(2018) demonstrated that ARIMA outperformed LSTM in predicting monthly and weekly incidence of hemorrhagic fever, whereas LSTM exhibited superior performance in daily incidence prediction compared to ARIMA. [PERSON] and [PERSON] (2018) applied both models to forecast user demand for vehicle sharing and found that LSTM was more accurate in predicting the demand for each type of user individually, whereas ARIMA performed better when forecasting the overall demand across all users. The findings, advantages, and limitations of the aforementioned model comparison studies are summarized in Table 2. In conclusion, though both ARIMA and LSTM models demonstrate accurate predictions for time series data, their prediction performance varies across diverse datasets. \begin{table}
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According to the current research status, we found that:  
* There are fewer studies on in-vehicle CO\({}_{2}\) concentrations compared to studies on in-vehicle particulates. * The models for in-vehicle CO\({}_{2}\) concentration primarily rely on computational models that consider physical influencing factors, but these models still have some limitations. * Due to their similar application domains, ARIMA and LSTM are frequently employed for comparative analysis of forecasting accuracy. There exists a knowledge gap in this area, with inadequate comprehension of in-vehicle CO\({}_{2}\) concentration levels across diverse driving conditions, and there is a lack of studies utilizing data-driven time series analysis methods to build prediction models for the in-vehicle CO\({}_{2}\) concentration. Based on the aforementioned description, the cross-application of time-series forecasting models and traffic environmental science warrants in-depth investigation. It is feasible to apply ARIMA and LSTM to predict CO\({}_{2}\) concentrations in vehicles, and further comparative experiments are necessary to verify the accuracy of both models. According to the current research status, the purpose of this research is to enhance our understanding of in-vehicle CO\({}_{2}\) concentrations under various driving conditions, to establish a precise time series predictive model for the in-vehicle CO\({}_{2}\) concentration, and to compare the accuracy of these models. In order to achieve this objective, this research conducted field tests to obtain in-vehicle CO\({}_{2}\) concentration data under different driving conditions, used the statistical prediction algorithm ARIMA and the deep-learning algorithm LSTM to build a predictive model of the in-vehicle CO\({}_{2}\) concentration, compared the prediction accuracy of the two models, and we explore the application scope of the models. The findings of this research offer suggestions to traffic safety managers for selecting suitable early warning models for in-vehicle CO\({}_{2}\) concentrations and provide theoretical guidance for establishing an effective in-vehicle ventilation control system, which can help reduce the incidence of traffic accidents. Furthermore, this research can also serve as a data reference for establishing in-vehicle CO\({}_{2}\) concentration standards in the automotive industry.