交通需求分析和预测是城市轨道交通规划的重要依据，然而宏观交通模型的复杂性、数据质量及规划变更等问题容易导致轨道交通客流预测的不确定性。为弥补交通需求和客流预测的不确定性，以石家庄市为例，利用手机大数据从职住分布、职住平衡、出行需求、通勤圈等四个维度剖析城市特征，从城市特征刻画、现状模型校核、发展趋势研判等方面与需求预测模型优势互补，并与巴黎大区横向对标分析，借鉴巴黎大区轨道交通规划和发展的经验，从规划策略层面为城市轨道交通规划提供支撑。

Traffic demand analysis and prediction are important for urban rail transit planning. However, the complexity of macro traffic model, data quality and planning changes are easy to lead to the uncertainty of ridership prediction. With Shijiazhuang taken as an example, this study employs mobile phone big data to analyze urban characteristics based on the four dimensions of job-housing distribution, job-housing balance, travel demand, and commuting circle to compensate for the uncertainty in traffic demand and ridership predictions. The data is also used to compensate for deficiencies in the demand prediction model in the three aspects of urban feature description, current traffic model verification, and development trend evaluation. Finally, the results are compared with those of Paris Region (Région ?le-de-France); drawing on the experience of rail transit planning and development in Paris Region, the study provides a decision-making reference for urban rail transit planning.

U239.5