用机器学习提升引力模型，精准预测区域间出行需求
A Data-Driven Approach to Enhancing Gravity Models for Trip Demand Prediction
- 融合地理经济等多源数据，用机器学习改进传统引力模型
- R-squared提升51.48%，MAE降低63.59%，预测精度显著提高
- 适合交通规划、城市政策制定者使用，提升决策可靠性
准确预测区域间出行量对交通规划至关重要，支持资源分配与基础设施建设。尽管引力模型因简单易用而广泛使用，但难以反映现代出行行为的复杂影响因素。本研究提出一种数据驱动方法，整合田纳西州和纽约州各县的地理、经济、社会及出行数据，利用机器学习技术扩展传统模型的能力，以处理变量间的复杂交互。实验表明，机器学习增强模型显著优于传统模型：R-squared提升51.48%，说明解释力大幅增强；均方误差（MAE）降低63.59%，预测准确率明显提高；共同通勤部分（CPC）增加44.32%，预测可靠性提升。结果表明，融合多元数据与先进算法能显著改善交通模型性能，为城市规划者和政策制定者提供更可靠的预测工具。
原文摘要 · Abstract (English)
Accurate prediction of trips between zones is critical for transportation planning, as it supports resource allocation and infrastructure development across various modes of transport. Although the gravity model has been widely used due to its simplicity, it often inadequately represents the complex factors influencing modern travel behavior. This study introduces a data-driven approach to enhance the gravity model by integrating geographical, economic, social, and travel data from the counties in Tennessee and New York state. Using machine learning techniques, we extend the capabilities of the traditional model to handle more complex interactions between variables. Our experiments demonstrate that machine learning-enhanced models significantly outperform the traditional model. Our results show a 51.48% improvement in R-squared, indicating a substantial enhancement in the model's explanatory power. Also, a 63.59% reduction in Mean Absolute Error (MAE) reflects a significant increase in prediction accuracy. Furthermore, a 44.32% increase in Common Part of Commuters (CPC) demonstrates improved prediction reliability. These findings highlight the substantial benefits of integrating diverse datasets and advanced algorithms into transportation models. They provide urban planners and policymakers with more reliable forecasting and decision-making tools.
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