[1] BRESSON G, DARGAY J, MADRE J L, et al. The main determinant of the demand for public transport: A comparative analysis of England and France using shrinkage estimators[J]. Transportation Research Part A, 2003, 37(7): 605-627.
[2] SCHIMEK P. Dynamic estimates of fare elasticity for US public transit[J]. Transportation Research Record, 2015 (2538): 96-101.
[3] WANG Z J, LI X H, CHEN F. Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data[J]. Transportation Research Part A, 2015(77): 213-224.
[4] 孟斌, 黄松, 尹芹. 北京市居民地铁出行出发时间弹性时空分布特征研究[J]. 地球信息科学学报, 2019, 21 (1): 107-117. [MENG B, HUANG S. YI Q. Spatial and temporal distribution characteristics of residents' depart times elasticity in Beijing[J]. Journal of Geo- Information Science, 2019, 21(1): 107-117.]
[5] 刘家玮. 北京地铁分时定价对乘客出行行为影响研究 [D]. 北京: 北京交通大学, 2018. [LIU J W. Research on the effect of time-dependent pricing of Beijing subway on passenger travel behaviors[D]. Beijing: Beijing Jiaotong University, 2018.]
[6] CURRIE G. Quick and effective solution to rail overcrowding: Free early bird ticket experience in Melbourne, Australia[J]. Transportation Research Record, 2010(2146): 35-42.
[7] 邹庆茹, 赵鹏, 姚向明. 基于售检票数据的城市轨道交通乘客分类[J]. 交通运输系统工程与信息, 2018, 18(1): 223-230. [ZOU Q R, ZHAO P, YAO X M. Passenger classification for urbanrail transit by mining smart card data[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(1): 223-230. ]
[8] PEER S, KNOCKAERT J, VERHOEF E T. Train commuters' scheduling preferences: Evidence from a large- scale peak avoidance experiment[J]. Transportation Research Part B, 2016(83): 314-333. |