用实时数据提升印度城市送餐时间预测准确率
Food Delivery Time Prediction in Indian Cities Using Machine Learning Models
- 融合交通、天气等实时信息构建预测模型
- LightGBM模型预测准确率达R2 0.76，MSE为20.59
- 适合物流优化与智能调度系统研发者参考
精准预测送餐时间对提升客户满意度、运营效率和盈利能力至关重要。现有研究多依赖静态历史数据，常忽略动态实时因素，尤其在人口密集的印度城市中。本研究通过整合交通密度、天气状况、本地活动及地理空间数据（餐厅与配送位置坐标）等实时上下文变量，弥补这一不足。在针对印度城市场景的完整送餐数据集上，系统比较了线性回归、决策树、集成学习、随机森林、XGBoost和LightGBM等多种机器学习算法。经过严格的预处理与特征选择，模型性能显著提升。实验结果表明，LightGBM模型表现最优，达到R2 = 0.76，均方误差（MSE）为20.59，显著优于传统基线方法。研究为复杂城市环境下的物流策略优化提供了可操作洞见。完整方法与代码已公开，支持复现与进一步研究。
原文摘要 · Abstract (English)
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.
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