[1]

Jiang S, Cai C. 2022. Unraveling the dynamic impacts of COVID-19 on metro ridership: an empirical analysis of Beijing and Shanghai, China. Transport Policy 127:158−70

doi: 10.1016/j.tranpol.2022.09.002
[2]

Bucsky P. 2020. Modal share changes due to COVID-19: the case of Budapest. Transportation Research Interdisciplinary Perspectives 8:100141

doi: 10.1016/j.trip.2020.100141
[3]

Molloy J, Schatzmann T, Schoeman B, Tchervenkov C, Hintermann B, et al. 2021. Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel. Transport Policy 104:43−51

doi: 10.1016/j.tranpol.2021.01.009
[4]

Zhang Z, Han T, Yoo KH, Capecelatro J, Boehman AL, et al. 2021. Disease transmission through expiratory aerosols on an urban bus. Physics of Fluids 33(1):015116

doi: 10.1063/5.0037452
[5]

Guihaire V, Hao JK. 2008. Transit network design and scheduling: a global review. Transportation Research Part A: Policy and Practice 42(10):1251−1273

doi: 10.1016/j.tra.2008.03.011
[6]

Freling R, Wagelmans APM, Paixão JMP. 2001. Models and algorithms for single-depot vehicle scheduling. Transportation Science 35(2):165−180

doi: 10.1287/trsc.35.2.165.10135
[7]

Bunte S, Kliewer N. 2009. An overview on vehicle scheduling models. Public Transport 1(4):299−317

doi: 10.1007/s12469-010-0018-5
[8]

Bertossi AA, Carraresi P, Gallo G. 1987. On some matching problems arising in vehicle scheduling models. Networks 17(3):271−281

doi: 10.1002/net.3230170303
[9]

Liu T, Ceder AA. 2017. Deficit function related to public transport: 50 year retrospective, new developments, and prospects. Transportation Research Part B: Methodological 100:1−19

doi: 10.1016/j.trb.2017.01.015
[10]

Liu T, Ceder AA. 2017. Integrated public transport timetable synchronization and vehicle scheduling with demand assignment: a bi-objective bi-level model using deficit function approach. Transportation Research Procedia 23:341−361

doi: 10.1016/j.trpro.2017.05.020
[11]

Tang C, Ceder A, Zhao S, Ge YE. 2019. Vehicle scheduling of single-line bus service using operational strategies. IEEE Transactions on Intelligent Transportation Systems 20:149−1159

doi: 10.1109/tits.2018.2841061
[12]

Ibarra-Rojas OJ, Muñoz JC. 2016. Synchronizing different transit lines at common stops considering travel time variability along the day. Transportmetrica A: Transport Science 12(8):751−769

doi: 10.1080/23249935.2016.1174964
[13]

Shen G, Du J. 2017. Research on two-line bus interchange model based on non-equal interval. Journal of Zhejiang University Technology 45(5):587−590 (in Chinese)
[14]

Song S, Zhang W. 2018. A bus query system based on least-transfer algorithm. Computer Knowledge Technology 14(1):96−98 (in Chinese)
[15]

Lu BC, He XY, Hu S, Shu Q. 2020. Study on coordinated scheduling of multi-route flexible buses in urban periphery in off-peak period. Road Traffic Science Technology 37(5):131−139,158 (in Chinese)

doi: 10.3969/j.issn.1002-0268.2020.05.016
[16]

Daganzo CF, Pilachowski J. 2011. Reducing bunching with bus-to-bus cooperation. Transportation Research Part B: Methodological 45(1):267−277

doi: 10.1016/j.trb.2010.06.005
[17]

Chandrasekar P, Long Cheu R, Chin HC. 2002. Simulation evaluation of route-based control of bus operations. Journal of Transportation Engineering 128(6):519−527

doi: 10.1061/(ASCE)0733-947X(2002)128:6(519)
[18]

He SX, Dong J, Liang SD, Yuan PC. 2019. An approach to improve the operational stability of a bus line by adjusting bus speeds on the dedicated bus lanes. Transportation Research Part C: Emerging Technologies 107:54−69

doi: 10.1016/j.trc.2019.08.001
[19]

Li ZC, Wu F, Xu C, Li J. 2009. Simulation of hybrid genetic tabu algorithm for quasi-bus rapid transit scheduling optimization with multi-line. Journal of Computer Applications 29(1):139−142
[20]

Wu WX, Zhao HL, Zhou HJ, Chen Z. 2022. Optimization of train skip-stop operation on urban rail transit lines during peak hours. Journal of Transportation Engineering and Information 20(2):42−59 (in Chinese)

doi: 10.19961/j.cnki.1672-4747.2022.01.018
[21]

Mazloumi E, Mesbah M, Ceder A, Moridpour S, Currie G. 2012. Efficient Transit Schedule Design of timing points: a comparison of Ant Colony and Genetic Algorithms. Transportation Research Part B: Methodological 46(1):217−234

doi: 10.1016/j.trb.2011.09.010
[22]

Zheng QY, Gao LP, Zheng Y. 2025. Optimization of customized shuttle bus service area layout considering spatiotemporal accessibility. Journal of Transportation Engineering and Information 23:104−118
[23]

Deng YJ, Liu XH, Hu X, Zhang M. 2020. Reduce bus bunching with a real-time speed control algorithm considering heterogeneous roadway conditions and intersection delays. Journal of Transportation Engineering, Part A: Systems 146(7):04020048

doi: 10.1061/jtepbs.0000358
[24]

Sun A, Hickman M. 2005. The real–time stop–skipping problem. Journal of Intelligent Transportation Systems 9(2):91−109

doi: 10.1080/15472450590934642
[25]

Shan XY, Wan CX, Wang XY, Li YF, Ren JY, et al. 2022. High occupancy vehicle lane strategy considering bus priority in intelligent and connected vehicle environment. Journal of Transportation Engineering and Information 20(3):89−101 (in Chinese)

doi: 10.19961/j.cnki.1672-4747.2021.04.038
[26]

Chen X, Hellinga B, Chang C, Fu L. 2015. Optimization of headways with stop-skipping control: a case study of bus rapid transit system. Journal of Advanced Transportation 49(3):385−401

doi: 10.1002/atr.1278
[27]

Li JH, Fu H. 2025. Urban Network Partitioning with OD Directions of Traffic Sub-regions. Industrial Engineering Journal 28(5):142−151

doi: 10.3969/j.issn.1007-7375.240284
[28]

Cao Z, Jiang Y, Luo X, Yang J, Zhang Y. 2020. A direct optimization in urban transit network for small and medium-sized cities. Industrial Engineering Journal 23(6):117−123

doi: 10.3969/j.issn.1007-7375.2020.06.016
[29]

Yu Y, Ye Z, Wang C. 2015. Study of bus stop skipping scheme based on modified cellular genetic algorithm. CICTP 2015. Beijing, China. US: American Society of Civil Engineers. pp. 2397−2409 doi: 10.1061/9780784479292.222
[30]

Liu Z, Yan Y, Qu X, Zhang Y. 2013. Bus stop-skipping scheme with random travel time. Transportation Research Part C: Emerging Technologies 35:46−56

doi: 10.1016/j.trc.2013.06.004
[31]

Berrebi SJ, Watkins KE, Laval JA. 2015. A real-time bus dispatching policy to minimize passenger wait on a high frequency route. Transportation Research Part B: Methodological 81:377−389

doi: 10.1016/j.trb.2015.05.012
[32]

Newell GF. 1974. Control of pairing of vehicles on a public transportation route, two vehicles, one control point. Transportation Science 8(3):248−264

doi: 10.1287/trsc.8.3.248
[33]

Hernández D, Muñoz JC, Giesen R, Delgado F. 2015. Analysis of real-time control strategies in a corridor with multiple bus services. Transportation Research Part B: Methodological 78:83−105

doi: 10.1016/j.trb.2015.04.011
[34]

Wu W, Liu R, Jin W. 2017. Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour. Transportation Research Part B: Methodological 104:175−197

doi: 10.1016/j.trb.2017.06.019
[35]

Gavriilidou A, Cats O. 2019. Reconciling transfer synchronization and service regularity: real-time control strategies using passenger data. Transportmetrica A: Transport Science 15(2):215−243

doi: 10.1080/23249935.2018.1458757
[36]

Gkiotsalitis K, Maslekar N. 2015. Improving bus service reliability with stochastic optimization. 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015. US: IEEE. pp. 2794−2799 doi: 10.1109/ITSC.2015.449
[37]

Cortés CE, Sáez D, Milla F, Núñez A, Riquelme M. 2010. Hybrid predictive control for real-time optimization of public transport systems' operations based on evolutionary multi-objective optimization. Transportation Research Part C: Emerging Technologies 18(5):757−769

doi: 10.1016/j.trc.2009.05.016
[38]

Fu L, Liu Q, Calamai P. 2003. Real-time optimization model for dynamic scheduling of transit operations. Transportation Research Record: Journal of the Transportation Research Board 1857:48−55

doi: 10.3141/1857-06
[39]

Cats O. 2014. Regularity-driven bus operation: principles, implementation and business models. Transport Policy 36:223−230

doi: 10.1016/j.tranpol.2014.09.002
[40]

Trompet M, Anderson RJ, Graham DJ. 2009. Variability in comparable performance of urban bus operations. Transportation Research Record 2111(1):177−184

doi: 10.3141/2111-20
[41]

Randall ER, Condry BJ, Trompet M, Campus SK. 2007. International bus system benchmarking: Performance measurement development, challenges, and lessons learned. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007. Washington, D.C., USA: Transportation Research Board
[42]

Roberts MG, Heesterbeek JAP. 2003. Mathematical models in epidemiology. In Mathematical Models. Abu Dhabi: EOLSS. pp. 2−5
[43]

Kermack WO, McKendrick AG. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London Series A 115(772):700−721

doi: 10.1098/rspa.1927.0118
[44]

Auchincloss AH, Gebreab SY, Mair C, Diez Roux AV. 2012. A review of spatial methods in epidemiology, 2000−2010. Annual Review of Public Health 33(1):107−122

doi: 10.1146/annurev-publhealth-031811-124655
[45]

Moore DA, Carpenter TE. 1999. Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiologic Reviews 21(2):143−161

doi: 10.1093/oxfordjournals.epirev.a017993
[46]

Wu N, Li Y, Zhao A, Xiao C, Zhou S. 2020. Operation strategy for public transportation in Wuhan after the COVID-19 epidemic. Journal of Transportation Engineering and Information 18(3):64−73
[47]

Duan W, Fan Z, Zhang P, Guo G, Qiu X. 2015. Mathematical and computational approaches to epidemic modeling: a comprehensive review. Frontiers of Computer Science 9(5):806−826

doi: 10.1007/s11704-014-3369-2
[48]

Grassly NC, Fraser C. 2008. Mathematical models of infectious disease transmission. Nature Reviews Microbiology 6(6):477−487

doi: 10.1038/nrmicro1845
[49]

Peeri NC, Shrestha N, Rahman MS, Zaki R, Tan Z, et al. 2020. The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? International Journal of Epidemiology 49(3):717−726

doi: 10.1093/ije/dyaa033
[50]

Wu JT, Leung K, Leung GM. 2020. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet 395(10225):689−697

doi: 10.1016/S0140-6736(20)30260-9
[51]

Furuya H. 2007. Risk of transmission of airborne infection during train commute based on mathematical model. Environmental Health and Preventive Medicine 12:78−83

doi: 10.1007/BF02898153
[52]

Riley EC, Murphy G, Riley RL. 1978. Airborne spread of measles in a suburban elementary school. American Journal of Epidemiology 107(5):421−432

doi: 10.1093/oxfordjournals.aje.a112560
[53]

Gao Y, Kroon L, Schmidt M, Yang L. 2016. Rescheduling a metro line in an over-crowded situation after disruptions. Transportation Research Part B: Methodological 93:425−449

doi: 10.1016/j.trb.2016.08.011
[54]

Dong X, Li D, Yin Y, Ding S, Cao Z. 2020. Integrated optimization of train stop planning and timetabling for commuter railways with an extended adaptive large neighborhood search metaheuristic approach. Transportation Research Part C: Emerging Technologies 117:102681

doi: 10.1016/j.trc.2020.102681
[55]

Lee I, Cho SH, Kim K, Kho SY, Kim DK. 2022. Travel pattern-based bus trip origin-destination estimation using smart card data. PLoS One 17(6):e0270346

doi: 10.1371/journal.pone.0270346
[56]

Dai X, Chen XW. 2005. Analysis and processing method of IC card data of single bus line. Urban Transportation 2005(4):77−80