发表机构
Institute for Computer Science and Control, Hungarian Research Network（匈牙利研究网络计算机科学与控制研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于巴黎大都会区约7万条出行数据，用可解释机器学习方法分析15分钟城市理念与出行模式的关联，验证其核心假设并揭示空间异质性，为城市交通政策提供依据。
AI 中文摘要
15分钟城市理念倡导在短距离步行或骑行范围内获取日常服务，但其与实际观测到的出行模式之间的关联难以量化。我们利用NetMob 2025数据挑战赛的出行轨迹，结合INSEE社会人口统计数据和OpenStreetMap兴趣点（POI），在基于停留点进行分段和数据清洗后，共得到约70000个出行路段，以此研究巴黎大都会区的上述关联。我们构建了基于步行和骑行的本地服务可达性指标，考察其与出行时长、交通方式及短途汽车使用的关联。POI可达性越高，私人机动出行越少，主动出行越多，但这种关联在城市外围集聚区域明显减弱。采用可解释机器学习方法解释的梯度提升树模型，始终将出行目的、家-工作地距离、本地服务可达性、车辆拥有情况、公共交通订阅情况及社会人口背景识别为重要预测因子。对于短途出行，高POI密度与较低的汽车使用相关，而车辆拥有和驾照持有与较高的预测汽车使用相关；在服务稀疏区域，公共交通订阅与较低的预测汽车依赖相关。最后，采用可解释人工智能（XAI）方法研究特征归因如何在不同假设的变量排序下发生变化。研究结果与15分钟城市的核心假设一致，同时揭示了显著的空间和社会人口异质性，还证明了可解释机器学习方法可补充可达性指标，并为城市交通政策识别本地相关假设。
英文摘要
The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficult to quantify. We investigate this relationship in the Paris metropolitan area using mobility trajectories from the NetMob 2025 Data Challenge, enriched with INSEE sociodemographic data and OpenStreetMap points of interest (POIs), yielding approximately 70,000 trip segments after stop-based segmentation and data cleaning. We construct walking- and cycling-based indicators of local service availability and examine their associations with trip duration, transport mode, and short-trip car use. Higher POI availability is associated with less private motorized travel and more active mobility, although this relationship is substantially weaker in the outer agglomeration. Gradient-boosted tree models interpreted with explainable machine-learning methods consistently identify trip purpose, home--work distance, local service availability, vehicle ownership, public-transport subscription, and sociodemographic context as important predictors. For short trips, high POI density is associated with lower car use, while car ownership and driving-licence availability are associated with higher predicted car use; where services are sparse, public-transport subscription is associated with lower predicted car dependence. Finally, explainable AI (XAI) methods are used to examine how feature attributions change under alternative assumed variable orderings. The results are consistent with central assumptions of the 15-minute city while revealing substantial spatial and demographic heterogeneity. They also demonstrate how explainable machine-learning methods can complement accessibility indicators and identify locally relevant hypotheses for urban-mobility policy.
Comments17 pages, 16 figures. Extended report of a poster presented at the NetMob 2025 conference on 8 October 2025
