[1] Berdica K. An introduction to road vulnerability: what has been done, is done and should be done [J]. Transport Policy, 2002, 9(2): 117-127. [2] Mattsson L G, Jenelius E. Vulnerability and resilience of transport systems|A discussion of recent research [J]. Transportation Research Part A: Policy and Practice, 2015, 81: 16-34. [3] Nagurney A, Dong J. Supernetworks: decision-making for the information age [M]. Cheltenham: Edward Elgar Publishing, 2002. [4] Wu Z X, Lam W H K. Network equilibrium for congested multi-mode networks with elastic demand [J]. Journal of Advanced Transportation, 2003, 37(3): 295-318. [5] García R, Marín A. Network equilibrium with combined modes: models and solution algorithms [J]. Transportation Research Part B: Methodological, 2005, 39(3): 223-254. [6] Si B, Zhong M, Yang X, et al. Urban transit assignment model based on augmented network with in-vehicle congestion and transfer congestion [J]. Journal of Systems Science and Systems Engineering, 2011, 20(2): 155-172. [7] Si B, Yang X, Gao Z. Multimodal traffic equilibrium model based on traveler’s combined choice within super-network [C]//2011 International Conference on Business Management and Electronic Information, 2011, 5: 74-80. [8] Qin X, Ke J, Wang X, et al. Demand management for smart transportation: a review [J]. Multimodal Transportation, 2022, 1(4): 100038. [9] Wang H. Transportation-enabled urban services: a brief discussion [J]. Multimodal Transportation, 2022, 1(2): 100007. [10] Wang K, Akar G, Cheng L, et al. Investigating tools for evaluating service and improvement opportunities on bicycle routes in Ohio, United States [J]. Multimodal Transportation, 2022, 1(4): 100040. [11] Souche-Le Corvec S. Which transport modes do people use to travel to coworking spaces (CWSs)? [J]. Multimodal Transportation, 2023, 2(2): 100078. [12] 邵虎, 卓越, 刘鹏杰, 等. 城市交通流量估计的运筹学方法 [J]. 运筹学学报, 2023, 27(2): 27-48. [13] 魏贺, 刘昊飞, 许丹丹, 等. 双层规划在城市交通领域研究与应用的系统综述 [J]. 运筹学学报, 2023, 27(2): 1-26. [14] Bell M G, Kurauchi F, Perera S, et al. Investigating transport network vulnerability by capacity weighted spectral analysis [J]. Transportation Research Part B: Methodological, 2017, 99: 251- 266. [15] Hu S R, Peeta S, Chu C H. Identification of vehicle sensor locations for link-based network traffic applications [J]. Transportation Research Part B: Methodological, 2009, 43(8-9): 873- 894. [16] Ng M W. Synergistic sensor location for link flow inference without path enumeration: a nodebased approach [J]. Transportation Research Part B: Methodological, 2012, 46(6): 781-788. [17] He S. A graphical approach to identify sensor locations for link flow inference [J]. Transportation Research Part B: Methodological, 2013, 51: 65-76. [18] Salari M, Kattan L, Lam W H, et al. Modeling the effect of sensor failure on the location of counting sensors for origin-destination (OD) estimation [J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103367. [19] Zhuo Y, Shao H, Lam W H, et al. Revisiting the traffic flow observability problem: a matrixbased model for traffic networks with or without centroid nodes [J]. Transportation Research Part B: Methodological, 2024, 190: 103099. [20] Castillo E, Calvino A, Lo H K, et al. Non-planar hole-generated networks and link flow observability based on link counters [J]. Transportation Research Part B: Methodological, 2014, 68: 239-261. [21] 傅白白, 高自友. 平衡规划问题的熵函数方法及其在混合交通流中的应用 (英文) [J]. 运筹学学报, 2007(1): 49-54. [22] 王茂然, 蔡邢菊, 吴中明, 等. 多模式交通均衡问题的一阶分裂算法 [J]. 运筹学学报, 2023, 27(2): 63-78. [23] Fan Y, Ding J, Liu H, et al. Large-scale multimodal transportation network models and algorithms|Part I: The combined mode split and traffic assignment problem [J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 164: 102832. [24] Graff L K, Flanigan K A, Qian S. Constructing a routable multimodal, multi-cost, timedependent network model with all emerging mobility options: methodology and case studies [J]. Transportation Research Part E: Logistics and Transportation Review, 2024, 192: 103757. [25] Huang H, Bucher D, Kissling J, et al. Multimodal route planning with public transport and carpooling [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(9): 3513- 3525. [26] Zhang X, Xu Y, Lai K K, et al. A traffic equilibrium model for multi-modal networks with uncertain demands [J]. Applied Sciences, 2023, 13(23): 12841. [27] Li J, Boonaert J, Doniec A, et al. Multi-models machine learning methods for traffic flow estimation from floating car data [J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103389. [28] Huang X, Ye Y, Yang X, et al. Multi-view dynamic graph convolution neural network for traffic flow prediction [J]. Expert Systems with Applications, 2023, 222: 119779. [29] Du S, Yang T, Teng F, et al. Multi-scale feature enhanced spatio-temporal learning for traffic flow forecasting [J]. Knowledge-Based Systems, 2024, 294: 111787. [30] Yu H, Wu Z, Wang S, et al. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks [J]. Sensors, 2017, 17(7): 1501. [31] Nguyen S, Pallottino S. Equilibrium traffic assignment for large scale transit networks [J]. European Journal of Operational Research, 1988, 37(2): 176-186. [32] Berge C. Balanced hypergraphs and some applications to graph theory [M]//Srivastava J N, Harary F, Rao C R, et al. (eds.) A survey of combinatorial theory, Amsterdam: North-Holland, 1973: 15-23. [33] Bondy J A, Murty U S R. Graph theory with applications [M]. London: Macmillan, 1976. |