车厢内的人群密集拥挤是轨道交通通勤区别于道路自驾出行的重要影响因素。解释和预测轨道交通用户综合考虑出行时间、时刻延误、车厢拥挤和票价因素进行早高峰通勤决策的行为进而制定可行的需求管理措施，需要理解和把握通勤者在家、在车厢内还是在工作地点的边际时间效用变化规律。以往研究大都假定通勤者具有常数化的边际时间效用，但这一假设与实际情况不大符合。项目以分析轨道交通用户的时间使用决策为切入点，通过通勤时刻选择偏好调查和可控行为实验，验证通勤者时间效用偏好的时变特性，重点揭示车厢拥挤感知负效用的时变函数规律。在此基础上，考虑内生的车厢拥挤效应，构建轨道交通走廊高峰通勤均衡模型和居住选址均衡模型，在系统效率和出行公平性两方面进行福利经济分析。最后利用北京轨道交通一卡通刷卡数据进行实证研究，校正模型参数，发现并解释典型走廊通勤居民的高峰出行和居住选址规律，为实际轨道交通系统的票价制定与运营管理提供理论依据。

In-vehicle crowding is one of important factors impacting the commuting in rail transit systems, which is distinct from the travel by auto. Morning peak-period commuters generally choose their respective preferred trains to the destination with the balance of travel time, schedule delay, in-vehicle crowding and fare. It requires understanding and characterizing the variation of their marginal time utilities at home, in the vehicle or at work, for the purpose of explaining and forecasting commuting decision-makings of peak-period transit commuters and then designing feasible demand management policies. Existing studies on rail transit commuting problems almost assume commuters have the constant marginal time utility preferences, which are not consistent with some empirical evidences. With the analysis of rail transit commuters' time-of-use decisions as the starting point, this project plans to conduct departure-time choice preference surveys and controlled behavioral experiments. Our objective is to verify the variation of commuters' time utility preferences, especially to reveal the function forms of time-varying disutility from in-vehicle crowding. On this basis, we plan to develop peak-period commuting equilibrium models and residential location equilibrium models with endogenous in-vehicle crowding, and then do welfare economic analyses on system efficiency and travel equity. Besides, this project plans to conduct empirical studies in Beijing using rail transit smart card data to calibrate model parameters. The models and findings in this project can explore and explain peak-period commuting and residential location patterns on rail transit corridors, and accordingly provide a theoretical basis for the practical pricing and operational management of rail transit systems.