用加州高速数据训练AI模型，10分钟间隔预测最准
Prediction of Highway Traffic Flow Based on Artificial Intelligence Algorithms Using California Traffic Data
- 用30秒到15分钟数据训练线性回归和随机森林模型
- 10分钟间隔下R²、MAE、RMSE表现最优
- 适合交通管理与拥堵缓解的工程应用
本研究基于人工智能算法，利用2022年7月至11月期间加州高速公路78号西行段（梅尔罗斯大道至埃尔卡米诺大道，长约7.24公里）每30秒采集的交通数据，构建机器学习预测模型以应对全球交通拥堵问题。采用多元线性回归（MLR）和随机森林（RF）算法，分析了30秒至15分钟不等的数据采集间隔。通过R²、MAE和RMSE评估性能，结果表明两种模型在10分钟数据间隔时表现最佳。研究结果有望为未来交通拥堵治理与高效交通管理提供支持。
原文摘要 · Abstract (English)
The study "Prediction of Highway Traffic Flow Based on Artificial Intelligence Algorithms Using California Traffic Data" presents a machine learning-based traffic flow prediction model to address global traffic congestion issues. The research utilized 30-second interval traffic data from California Highway 78 over a five-month period from July to November 2022, analyzing a 7.24 km westbound section connecting "Melrose Dr" and "El-Camino Real" in the San Diego area. The study employed Multiple Linear Regression (MLR) and Random Forest (RF) algorithms, analyzing data collection intervals ranging from 30 seconds to 15 minutes. Using R^2, MAE, and RMSE as performance metrics, the analysis revealed that both MLR and RF models performed optimally with 10-minute data collection intervals. These findings are expected to contribute to future traffic congestion solutions and efficient traffic management.
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