发表机构
University of Tennessee, Knoxville; University of Calgary（田纳西大学诺克斯维尔分校; 卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究对比XGBoost与Random Forest，结合空间交叉验证等方法分析不同城市住宅价格影响因素，发现交通设施对价格的相似影响及非空间交叉验证的偏差，推荐谨慎应用机器学习方法。
AI 中文摘要
土地利用与交通基础设施是紧密关联的系统，住宅物业价格在交通规划中占据核心地位，交通投资也会影响物业价值，因此对这两个系统进行准确预测是一项相互关联的研究挑战。本研究探究了这些关联，并评估了在不同城市背景下用于住宅房地产价格建模的机器学习方法。我们使用XGBoost和Random Forest模型，评估土地利用与交通基础设施如何影响住宅价格，比较了巴基斯坦拉瓦尔品第-伊斯兰堡都会区与加拿大多伦多市的结果；还通过对比非空间交叉验证与空间交叉验证，检验了机器学习模型在空间数据上的性能，并使用SHAP值解释特征影响。尽管人口统计学与经济发展存在差异，两座城市均呈现交通基础设施及本地配套设施对价格的相似影响：临近主要城市核心区会提高售价，地铁、快速公交等高质量交通可推高价格，而常规公交站点的临近则会降低价格。非空间交叉验证会高估空间数据集的预测准确性，XGBoost的表现略优于Random Forest。我们建议在对空间依赖的土地价格数据建模时，需谨慎应用机器学习方法。
英文摘要
Land use and transportation infrastructure are tightly linked systems, and residential property prices play a central role in transportation planning. Transportation investments also influence property values, making accurate forecasting of both systems an interconnected research challenge. This study examines these interactions and evaluates machine learning methods for modeling residential real estate prices across contrasting urban contexts. We use XGBoost and Random Forest models to assess how land use and transportation infrastructure shape dwelling prices, comparing results between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We also examine the performance of machine learning models on spatial data by comparing nonspatial and spatial cross validation, and use SHAP values to interpret feature impacts. Despite differences in demographics and economic development, both cities show similar effects of transportation infrastructure and local amenities on prices. Proximity to major urban cores increases sale price, while high quality transit such as subway and bus rapid transit raises prices and conventional bus stop proximity lowers them. Nonspatial cross validation overestimates predictive accuracy for spatial datasets. XGBoost performs slightly better than Random Forest. We recommend careful application of machine learning methods when modeling spatially dependent land price data.
Journal refTransportation Research Record (2026)
