用微数据和机器学习，精准预测城市小区域出行行为。
MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis
- 基于公开微数据与机器学习，构建合成人口出行模型。
- 在工作通勤数据上准确率高于传统方法。
- 适合城市规划者优化配送中心与交通政策。
本研究提出一种新型小区域估计框架，通过整合公开的微数据文件与机器学习方法，对代表性合成人口在小地理区域内的出行行为进行精细化建模，改进了传统的四阶段出行模型。该方法可实现高分辨率的出行生成、出行分布、出行方式选择及路径分配预测。利用美国社区调查（ACS/PUMS）工作通勤数据进行验证，结果表明该框架相比传统方法具有更高准确性。由此产生的细粒度洞察可支持针对局部问题的干预措施，助力多项政策应用，包括微配送中心的最优选址、路缘空间的有效管理，以及为弱势群体设计更具包容性的交通解决方案。
原文摘要 · Abstract (English)
This study presents a novel small-area estimation framework to enhance urban transportation planning through detailed characterization of travel behavior. Our approach improves on the four-step travel model by employing publicly available microdata files and machine learning methods to predict travel behavior for a representative, synthetic population at small geographic areas. This approach enables high-resolution estimation of trip generation, trip distribution, mode choice, and route assignment. Validation using ACS/PUMS work-commute datasets demonstrates that our framework achieves higher accuracy compared to conventional approaches. The resulting granular insights enable the tailoring of interventions to address localized situations and support a range of policy applications and targeted interventions, including the optimal placement of micro-fulfillment centers, effective curb-space management, and the design of more inclusive transportation solutions particularly for vulnerable communities.
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