Abstract
Railway transportation is an important mechanical infrastructure around the globe and is becoming more and more popular with citizens because of reliability and punctuality. However, it is practically challenging and theoretically important to increase efficiency of railway operations and the utilization efficiency of the existing infrastructures. Crowdfunding Train is the train based on crowdfunding method, which caters to the real demand of passengers. Xi’an Railway Bureau drove two crowdfunding trains between Xi’an and Yulin for the first attempt in China from Oct. 7th to Oct. 8th in 2017. This article describes this event and a series of mathematical models is built for the economic simulations of crowdfunding trains, especially about incomes, costs and profits, and then takes the crowdfunding train K8188 and K8187 in China as a case analysis to utilize models.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsAbbreviations
- A :
-
The ticket rank called Hard seat
- B :
-
The ticket rank called Hard sleeper
- C :
-
The ticket rank called Soft berth
- C Q :
-
The total costs, unit: yuan
- \(C_{Q}^{F}\) :
-
The total fixed costs, unit: yuan
- \(C_{Q}^{V}\) :
-
The total variable costs, unit: yuan
- \(C_{R}^{VC}\) :
-
The variable cost by carriage in the rank R, unit: yuan per carriage
- \(C_{R}^{VT}\) :
-
The variable cost by quantity in the rank R, unit: yuan per ticket
- \(f_{i} \left( \cdots \right)\) :
-
The function for the individual decisions of passengers
- FB :
-
The feed backs for passengers who buy tickets of the crowdfunding train, unit: yuan
- i :
-
The through variable with no meaning
- I Q :
-
The total income of tickets, unit: yuan
- Int(number) :
-
The function to calculate the maximum integer below the number
- L R :
-
The ticket quota of train carriages in the rank R, unit: ticket(s) per carriage
- \(maxQ_{R}^{C}\) :
-
The maximum train carriage quantity in the rank R, unit: carriage(s)
- \(maxQ_{R}^{T}\) :
-
The maximum sold ticket quantity in the rank R, unit: ticket(s)
- Mod (number, divisor):
-
The function to calculate the remainder of the number after it is divided by the divisor, e.g. Mod(3,2) = 1, Mod(–3,2) = 1
- n P :
-
The number of passengers who may participate in the crowdfunding train x
- n R :
-
The number of ticket ranks
- P :
-
The total profit of the crowdfunding train, unit: yuan
- P R :
-
The price of the ticket in the rank R, unit: yuan per ticket
- \(Q_{R}^{C}\) :
-
The quantity of train carriages in the rank R, unit: carriage(s)
- \(Q_{R}^{T}\) :
-
The quantity of sold tickets in the rank R, unit: ticket(s)
- R :
-
The rank of tickets that passengers could participate in
- s :
-
The identification of railway stations
- \(t_{s}^{AE}\) :
-
The arrival point-in-time of the train at the Railway Station s
- \(t_{s}^{DC}\) :
-
The deadline point-in-time of checking tickets at the Railway Station s
- \(t_{s}^{DE}\) :
-
The departure point-in-time of the train at the Railway Station s
- \(t_{x}^{B}\) :
-
The beginning point-in-time of the crowdfunding train x
- \(t_{x}^{C}\) :
-
The current point-in-time of the crowdfunding train x
- \(t_{x}^{D}\) :
-
The deadline point-in-time of the crowdfunding train x
- \(t_{x}^{F}\) :
-
The first point-in-time of the crowdfunding train x when the target achieved
- W i :
-
The individual decisions of passengers whether to purchasing the ticket in the rank R or not, which equals to 1 when yes and equals to 0 for when not
- x :
-
The code of the crowdfunding train
- ∆P:
-
The changes of the total profit of the crowdfunding train, unit: yuan
- \(\Delta Q_{R}^{C}\) :
-
The changes of the quantity of train carriages in the rank R, unit: carriage(s)
- \(\Delta Q_{R}^{T}\) :
-
The changes of the quantity of sold tickets in the rank R, unit: ticket(s).
References
Mollick E (2014) The dynamics of crowdfunding: an exploratory study [J]. J Bus Ventur 29(1):1–16
Belleflamme P, Lambert T, Schwienbacher A (2014) Crowdfunding: tapping the right crowd. J Bus Ventur 29(5):585–609
Macht SA, Weatherston J (2014) The benefits of online crowdfunding for fund-seeking business ventures. Strateg Chang (UK) 23(1–2):1–14
Ozdemir V, Faris J, Srivastava S (2015) Crowdfunding 2.0: the next-generation philanthropy—a new approach for philanthropists and citizens to co-fund disruptive innovation in global health. EMBO Rep 16(3):267–271
Sisler J (2012) Crowdfunding for medical expenses. Can Med Assoc J 184(2):E123–E124
Wheat RE, Wang YW, Byrnes JE et al (2013) Raising money for scientific research through crowdfunding. Trends Ecol Evol 28(2):71–72
Gerber EM, Hui JL (2013) Crowdfunding: motivations and deterrents for participation. ACM Trans Comput Hum Interact 20(6):1–32
Belleflamme P, Omrani N, Peitz M (2015) The economics of crowdfunding platforms. Inf Econ Policy (Netherlands) 33:11–28
Calic G, Mosakowski E (2016) Kicking off social entrepreneurship: how a sustainability orientation influences crowdfunding success. J Manag Stud (USA) 53(5):738–767
Sheng B, Zhiying L, Usman K (2017) The influence of online information on investing decisions of reward-based crowdfunding. J Bus Res (Netherlands) 71:10–18
Zheng HC, Li DH, Wu J et al (2014) The role of multidimensional social capital in crowdfunding: a comparative study in China and US. Inf Manage 51(4):488–496
Alaei S, Malekian A, Mostagir M et al (2016) A dynamic model of crowdfunding. In: Ec’16: Proceedings of the 2016 ACM conference on economics and computation, p 363
Corman F, D’Ariano A, Pacciarelli D et al (2010) A tabu search algorithm for rerouting trains during rail operations. Transp Res Pt B Methodol 44(1):175–192
Meng LY, Zhou XS (2014) Simultaneous train rerouting and rescheduling on an N-track network: a model reformulation with network-based cumulative flow variables. Transp Res Pt B Methodol 67:208–234
Larsen R, Pranzo M, D’Ariano A et al (2014) Susceptibility of optimal train schedules to stochastic disturbances of process times. Flex Serv Manuf J 26(4):466–489
Corman F, D’ariano A, Marra AD et al (2017) Integrating train scheduling and delay management in real-time railway traffic control. Transp Res Pt e-Logist Transp Rev 105:213–239
Xu Y, Jia B, Ghiasi A et al (2017) Train routing and timetabling problem for heterogeneous train traffic with switchable scheduling rules. Transp Res Pt C-Emerg Technol 84:196–218
Meng LY, Zhou XS (2011) Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach [J]. Transp Res Pt B-Methodol 45(7):1080–1102
Corman F, D’Ariano A, Pacciarelli D et al (2012) Optimal inter-area coordination of train rescheduling decisions. Transp Res Pt e-Logist Transp Rev 48(1):71–88
Wang B, Rong C, Li H et al (2015) Multi-time point optimization model for empty railcar distribution. J Transp Syst Eng Informat Technol 15(5):157–163,171
D’Ariano A, Pacciarelli D, Pranzo M (2007) A branch and bound algorithm for scheduling trains in a railway network. Eur J Oper Res (Netherlands) 183(2):643–657
Pellegrini P, Marliere G, Rodriguez J (2014) Optimal train routing and scheduling for managing traffic perturbations in complex junctions. Transp Res Pt B-Methodol 59:58–80
Lu Y, Xiong K, Fan PY et al (2017) Optimal multicell coordinated beamforming for downlink high-speed railway communications. IEEE Trans Veh Technol 66(10):9603–9608
Cordeau J-F, Toth P, Vigo D (1998) A survey of optimization models for train routing and scheduling. Transport Sci 32(4):380–404
Marsden G, Reardon L (2017) Questions of governance: rethinking the study of transportation policy. Transp Res Part A-Policy Practice 101:238–251
Corman F, Meng LY (2015) A review of online dynamic models and algorithms for railway traffic management. IEEE Trans Intell Transp Syst 16(3):1274–1284
Wheat P, Wardman M (2017) Effects of timetable related service quality on rail demand. Transp Res Part A-Policy Practice 95 96–108
Canca D, Barrena E, Algaba E et al (2014) Design and analysis of demand-adapted railway timetables. J Adv Transp 48(2):119–137
Gui JW (2018) A Study on financing efficiency measurement of information technology enterprises listed in NEEQ board based on three-stage DEA model and Malmquist index. In: Proceedings of the Fifth International Forum on Decision Sciences, Xi’an :215–223
Gui JW, Wu QQ (2018) A management optimization for customizing transportation: taking crowd funding train as an example. In: Proceedings of 2018 International conference on economic management science and financial innovation, Guangzhou. pp 182–186
Acknowledgements
The authors are indebted to anonymous referees for their thoughtful comments and Vinoth Selvamani for providing editing guidance. Jiawei Gui was rewarded by the National Scholarship of China for doctoral students and appreciated that. The work described in this article was supported in part by the Strategic Planning Research Project of Ministry of Transport of China [2018-7-9] and [2018-16-9], in part by the Chang'an University Excellent Doctoral Dissertation Project of Chinese Universities Scientific Fund of China [300102239718].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gui, J., Wu, Q. (2020). An Advanced Simulation and Optimization for Railway Transportation of Passengers: Crowdfunding Train. In: Li, X., Xu, X. (eds) Proceedings of the Sixth International Forum on Decision Sciences. Uncertainty and Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-13-8229-1_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-8229-1_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8228-4
Online ISBN: 978-981-13-8229-1
eBook Packages: Business and ManagementBusiness and Management (R0)