用交通流量数据建模，避免过拟合，解释性强。
Data-driven transport modelling without overfit
- 基于交通流量数据构建可解释的模型，无需复杂问卷调查。
- 通过控制模型复杂度提升精度，防止过拟合。
- 适合城市规划者与交通政策制定者快速验证方案。
宏观交通建模旨在预测公共政策干预（如新建道路、铁路或临时封闭）后的交通流变化，是基础设施规划的关键环节。传统方法依赖对人口社会经济特征的复杂理解，需昂贵且易产生偏倚的问卷调查。以往数值优化框架虽能拟合观测流量，但可解释性差，易导致过拟合。本文提出一种数据驱动建模协议：以低成本、可靠的交通流量计数作为目标函数，具备可解释的模型权重，并可控地增加模型复杂度以提升精度。我们在多个简化及真实案例中验证该方法，并提出向多模式系统（含公共交通）推广的路径。
原文摘要 · Abstract (English)
Macroscopic transport modelling aims to predict traffic flows after proposed public policy interventions, such as a new road or railway section or a temporary road closure. As such, it is a vital step in infrastructure planning and development. Traditionally, building a transport model has relied on complex understanding of socio-economic characteristics of the population requiring expensive data collection via surveys, which are prone to biases. Previous numerical frameworks to optimize transport models to fit observed traffic flows are not easily-interpretable and can lead to overfit. We present here an alternative: a data-driven modelling protocol with objective function based on traffic counts, which can be nowadays cheaply and reliably obtained; explainable model weights; and a controlled path to increase model complexity and accuracy. We demonstrate our approach on several toy and realistic examples, and suggest ways to generalize to multimodal systems including public transport.
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