Accurate prediction of food delivery times significantly impacts customer satisfaction, operational efficiency, and profitability in food delivery services. However, existing studies primarily utilize static historical data and often overlook dynamic, real-time contextual factors crucial for precise prediction, particularly in densely populated Indian cities. This research addresses these gaps by integrating real-time contextual variables such as traffic density, weather conditions, local events, and geospatial data (restaurant and delivery location coordinates) into predictive models. We systematically compare various machine learning algorithms, including Linear Regression, Decision Trees, Bagging, Random Forest, XGBoost, and LightGBM, on a comprehensive food delivery dataset specific to Indian urban contexts. Rigorous data preprocessing and feature selection significantly enhanced model performance. Experimental results demonstrate that the LightGBM model achieves superior predictive accuracy, with an R2 score of 0.76 and Mean Squared Error (MSE) of 20.59, outperforming traditional baseline approaches. Our study thus provides actionable insights for improving logistics strategies in complex urban environments. The complete methodology and code are publicly available for reproducibility and further research.

翻译：准确预测外卖送达时间对提升顾客满意度、运营效率和盈利能力具有显著影响。然而，现有研究主要利用静态历史数据，往往忽视了动态实时情境因素对精确预测的关键作用，这在人口密集的印度城市中尤为突出。本研究通过将实时情境变量——如交通密度、天气状况、本地事件以及地理空间数据（餐厅与配送地点的坐标）——整合到预测模型中，以弥补上述不足。我们针对印度城市环境特有的综合外卖数据集，系统比较了多种机器学习算法，包括线性回归、决策树、Bagging、随机森林、XGBoost 和 LightGBM。严格的数据预处理和特征选择显著提升了模型性能。实验结果表明，LightGBM 模型取得了最优的预测精度，其 R2 分数为 0.76，均方误差（MSE）为 20.59，优于传统的基线方法。因此，本研究为改进复杂城市环境下的物流策略提供了可行的见解。完整的方法论和代码已公开，以确保可复现性并促进进一步研究。