精准预测油耗为进一步促进节能减排以及生态驾驶的普及提供重要支撑。以实现在城市快速路汽车不同时段下油耗精准预测为目标，在乌鲁木齐市河滩快速路上开展实车实验，采集车辆油耗、速度以及车辆运行状态时间序列。采用互信息（mutual information, MI）法、皮尔逊（Pearson）相关系数以及斯皮尔曼（Spearman）相关系数等方法对油耗影响因素进行刻画并重构。基于贝叶斯优化（Bayesian optimization, BO）、Transformer以及长短期记忆神经网络（long short-term memory neural network, LSTM）构建油耗时间序列预测串联模型，并与其他6种模型进行预测性能的对比。结果表明：BO-Transformer-LSTM模型在工作日高峰及平峰时段下油耗预测决定系数（r-square, R²）分别达到98.3%和98.36%，平均绝对误差（mean absolute error, MAE）分别为0.030 9 g/s和0.042 9 g/s。串联模型对非工作日平峰和高峰时段数据集的预测精度依旧保持在97.43%和97.89%，表明所提模型具有较好的预测精度及泛化能力，验证了不同时段下城市快速路汽车油耗预测具有较强的鲁棒性和精准度，为车辆油耗精准预测以及构建不同时段排放清单提供支撑。

Accurate fuel consumption prediction is recognized as crucial support for promoting energy conservation, emission reduction, and the adoption of eco-driving. To achieve precise fuel consumption prediction for urban expressway vehicles across different time periods. Time-series data including fuel consumption, speed, and operating status were collected through field experiments conducted on Hetan Expressway in Urumqi. A multi-dimensional analysis of fuel consumption influencing factors was performed using mutual information(MI), Pearson correlation coefficient, and Spearman correlation coefficient methods, followed by feature reconstruction. A hybrid BO-Transformer-LSTM model was developed by integrating Bayesian optimization algorithms, Transformer architecture, and long short-term memory(LSTM) neural networks. Comparative assessments were performed against six benchmark models. Experimental results demonstrate that the BO-Transformer-LSTM model has a coefficient of determination of 98.3% and 98.36% for fuel consumption prediction during peak and peak hours on weekdays, respectively, and the mean absolute error(MAE) is 0.030 9 g/s and 0.042 9 g/s. In non-working day scenarios, prediction accuracy remains sTable at 97.43% for off-peak hours and 97.89% for peak hours, indicating superior generalization capabilities. This confirms that the proposed model effectively enhances the robustness of fuel consumption prediction across various urban expressway periods. This approach provides technical support for accurate vehicle energy consumption prediction and facilitates the construction of time-specific emission inventories.