立足当前城镇化进程中老城改造和新区建设并举背景下居住外迁所衍生的通勤交通出行问题，从微观个体活动-出行行为源头探索居住外迁对通勤交通出行的影响规律。针对居住外迁影响下个体通勤活动链中方式、时间、起讫点、路径多维出行特征演变的复杂性，采用"多源数据采集-影响因素识别-行为机理解析-混合仿真实证"的总体技术路线展开研究。基于个体居住外迁及活动-出行多源数据的同步调查分析深入挖掘居住外迁个体通勤链中的多维出行决策影响因素，扩展运用多层贝叶斯网络建模和智能体强化学习方法构建居住外迁个体通勤链中的多维出行决策机理解析模型。结合典型城市案例，运用Multi-Agent 和ArcGIS 混合仿真预测居住外迁个体微观通勤行为演变及宏观通勤交通结构与时空分布的动态变化特征。预期研究成果可为从微观尺度预见居住外迁引发的潜在通勤交通问题提供科学的理论依据和分析方法。

Residential relocation as consequence of city reform and construction during urbanization, is responsible for some commuting travel problems. By looking at that in a more microscopic way, effect of residential relocation on commuting travel can be captured by studying relocated commuter's activity-travel behavior. This research follows a technical guideline of "Collection of multi-source data - Identification of influencing factors - Analysis of travel mechanism - Verification of mixed simulation". It aims to better capture the complex evolution of multi-dimensional commuting chain features including travel mode, time, destination and path, which is caused by residential relocation. More specifically, based on data collected by carefully organized survey about residential relocation and individuals' activity-travel behaviors, influencing factors of multi-dimensional decisions in relocated commuters' activity chains will be targeted. Analysis models about mechanism of making those decisions will be established using methods like hierarchical bayesian networks and agent-based reinforcement learning. Then, using Multi-Agent and ArcGIS as a mixed simulated method and based on case study of typical city, evolution of relocated individual's commuting behavior as well as some dynamic features of macroscopic travel mode structure and its space-time distribution can be predicted. The expected research results can help to provide some theoretic basis as well as analyzing method to forecast potential commuting travel problems cause by residential relocation in a microscopic way.