Performance Evaluation of ARIMA, Autoformer, and Symmetric LSTNFCL Models for Traffic Accident Emergency Prediction

Abstract

The prediction of pre-hospital medical emergencies based on historical timing data holds significant potential for enhancing individual safety. In this study, we constructed ARIMA (Autoregressive Integrated Moving Average Model), Autoformer, and the structurally symmetric deep learning model LSTNFCL (Long-Short-Term Network with Conv2Former, CBAM (Convolutional Block Attention Module), and LSTM) using the time-period symmetric pre-hospital traffic accident medical emergency calls in Chengdu from 1 January 2022 to 31 December 2023. We systematically evaluated the prediction efficiency of the three models on pre-hospital traffic accident medical emergency call demand. The experiments show the following: The MAE (mean absolute error) of Autoformer is 0.849 and the RMSE (root mean square error) is 0.922, but its inference takes longer; LSTNFCL achieves competitive performance with an MAE of 1.681 and an RMSE of 3.301 under a lightweight architecture by integrating LSTM (Long Short-Term Memory), Conv2Former, and CBAM modules. It demonstrates notably superior computational efficiency and reasoning time compared to ARIMA. Furthermore, it surpasses Autoformer in terms of efficiency and reasoning time. Ablation experiments demonstrate that the Conv2Former module reduces the MSE (mean squared error) by 21%, while the CBAM module further optimizes the MAPE (mean absolute percentage error) to 0.891%. This study provides a prediction scheme that balances accuracy and real-time performance for pre-hospital emergency systems, and the results show that Autoformer is suitable for offline high-precision scenarios, while LSTNFCL is more suitable for real-time prediction needs with resource constraints. Focusing on the public safety, this paper offers an effective early prediction method for the pre-hospital emergency medical system.

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