To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.
AlbrechtT., Automated timetable design for demand-oriented service on suburban railways, Public Transp1(1) (2009), 5–20.
2.
ChanS. and Miranda-MorenoL., A station-level ridership model for the metro network in Montreal, Quebec Can Civ Eng40 (2013), 254–262.
3.
ChenY.Y.LiP.P. and ZhangG.H., Real-time evaluation method of passenger flow congestion in comprehensive transportation hub, Highway Traffic Technology29(2009), 75–80.
4.
ChiericiA.CordoneR. and MajaR., The demand-dependent optimization of regular train timetables, Electr, Notes Discrete Math17 (2004), 99–104.
5.
Corman D’ArianoF. et al., Integrating train scheduling and delay management in real-time railway traffic control, Transp Res Part E105 (2017), 213–239.
6.
FruinJ.J., Pedestrian Planning and Design. Metropolitan Association of Urban Designers and Environmental Planners, New York, USA, 1971.
7.
GangY.A.TianF. and LiW.Z., Operations Research (Third Edition), Beijing, Tsinghua University Press, 2005, pp. 301–329.
8.
HassannayebiE.ZegordiS.H. and YaghiniM., Train timetabling for an urban rail transit line using a lagrangian relaxation approach, Appl Math Model40 (2016), 9892–9913.
9.
HeJ., Study on Evaluation Model of Passenger Flow Congestion Level in Urban Rail Station, Beijing, Master’s Thesis, Beijing Jiaotong University, 2014.
10.
HeJ. and HouS., Tianjin metro station reconstruction design, Urban Rapid Rail Transit17 (2004), 92–95.
11.
HeJ.F. and LiuQ., Optimization method of subway emergency evacuation route based on congestion degree, China Safety Science Journal23(2) (2013), 166–171.
12.
HensherD.A.StopherP. and BullockP., Service quality-developing a service quality index in the provision of commercial bus contracts, Transp Res Part A37(6) (2003), 499–517.
13.
HensonC., Levels of service for pedestrian, Transportation Engeering70(9) (2000), 26–30.
14.
HoogendoornS.P. and DaamenW., Pedestrian behavior at bottlenecks, Transportation Science39(2) (2005), 147–159.
15.
HorowitzA.J., Simplifications for single-route transit-ridership forecasting models, Transportation12 (1984), 261–275.
16.
HuK.C. and JenW., Passengers’ perceived service quality of city buses in Taipei: scale development and measurement, Transp Rev 26(5) (2006), 645–662.
17.
IsekiH. et al., Evaluating Connectivity Performance at Transit Transfer Facilities, Under Contract 65A0194 for Project Tool Development to Evaluate the Performance of Intermodal Connectivity (EPIC) to Improve Public Transportation, Sacramento, USA, 2007.
18.
JiangS.C.SunY.C and DuY.C., The influence of congestion degree on willingness to choose public transportation mode, Journal of Tongji University (Natural Science Edition)40(12) (2012), 1831–1835.
19.
JiangX.ZhangL. and ChenX.M., Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China, Transport Res Part C Emerg Technol44 (2014), 110–127.
20.
KarlaftisM.G. and VlahogianniE.I., Statistical methods versus neural networks in transportation research: differences, similarities and some insights, Transport Res Part C-Emerg Technol19 (2011), 387–399.
21.
KimC.KimS. and KimJ., The development and application of the service evaluation indicators of transfer facilities in the high-speed railway stations, Korean Soc Civ Eng28(4D) (2008), 533–538.
22.
KumarM., A novel integer programming approach with separable structure for scheduling job shops, Advances in Industrial Engineering and Management7(2) (2018), 30–45.
23.
LiW.ZhouJ.N., The optimize management of passenger organization in transfer station based on dynamic passenger flow analysis, Procedia – Social and Behavioral Sciences96 (2013), 1322–1328.
24.
LitmanT., Understanding Transport Demands and Elasticities: How Prices and Other Factors Affect Travel Behavior, Victoria Transport Policy Institute, 2013.
25.
LiuY.C.ZhuS.Y. and PengZ.H., Calculation model of congestion index of rail transit platform, Technology and Method36(4) (2017), 74–79.
26.
MaX.L. et al., Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transport, Res Part C-Emerg Technol54 (2015), 187–197.
27.
MiaoQ., Environmental benefit analysis of china’s green finance in waste recycling, Advances in Industrial Engineering and Management8(1) (2019), 4–6.
28.
MoriM. and TsukaguchH., A new method for evaluation of service in pedestrian facilities, Transp Res Part A21(3) (1987), 223–234.
29.
NathanailE., Measuring the quality of service for passengers on the Hellenic railways, Transp Res Part A42 (2008), 48–66.
30.
National Overall emergency Plan for Public Emergencies, Chinese People’s Government Website, 2006.
31.
National Research Council (NRC), Highway Capacity Manual, Washington, D.C, TRB, 2010.
32.
NiuH.ZhouX. and GaoR., Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: nonlinear integer programming models with linear constraints, Transp Res Part B76 (2015), 117–135.
33.
OriveA.C.SantiagoJ.I.P. and OriveA.C., Methodology based on a swot-delphy-came analysis for the evaluation of automated container terminals in ports, Advances in Industrial Engineering and Management9(1) (2020), 41–46.
34.
PelletierM.P.TrepanierM. and MorencyC., Smart card data use in public transit: a literature review, Transport Res Part C: Emerg Technol19 (2011), 557–568.
35.
SarkerS., Determination of service levels for pedestrians with European examples, Transportation Reserch Record 1405, Transportation Research Board, Washington, D.C., 1993, pp. 35–42.
36.
SenevirameE.N. and MorrallJ.E., Level of service on pedestrian facilities, 39 (1985), 109–123.
37.
SiB. et al., A multi-class transit assignment model for estimating transit passenger flows-A case study of Beijing subway network, J Adv Transport (2015).
38.
SunL. et al., Demand-driven timetable design for metro services, Transp Res Part C46 (2014), 284–299.
39.
SunY., A Study on Optimization Schemes in Subway Station by Anylogic, International Conference on Advanced Materials and Computer Science, 2016.
40.
SunY.X.LengB. and GuanW., A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system, Neurocomputing166 (2015), 109–121.
41.
TaylorB.D. et al., Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas, Transport Res Part A Policy Pract43 (2009), 60–77.
42.
Urban Rail Transit Operation Index System, 2015, 2363-T-348.
43.
VlahogianniE.I.KarlaftisM.G. and GoliasJ.C., Short-term traffific forecasting: Where we are and where we’re going, Transport Res Part C Emerg Technol43 (2014), 3–19.
44.
WangH.H., Research on Internal Passenger Traffic Congestion Mechanism of Comprehensive Passenger Hub. Beijing, Master’s thesis, Beijing Jiaotong University, 2011.
45.
WangY. et al., Passenger-demands-oriented train scheduling for an urban rail transit network, Transp Res Part C60 (2015), 1–23.
46.
WuH. and LiH., On the optimum schemes of transfer lines at Xi zhi men Station of Beijing Metro, Urban Mass Transit15(1) (2012), 88–92.
47.
XieM.L. and LiZ., Refined Evaluation of Congestion Degree of Rail Cars and Platforms Based on Big Data, Coordinated Development and Traffic Practice-Proceedings of the 2015 China Urban Transportation Planning Annual Conference and the 28th Academic Symposium, 2015.
48.
XuQ., Congest Perception and Behavioral Dynamics Modeling of Urban Rail Transit Platform Passengers, Beijing, Master’s thesis, Beijing Jiaotong University, 2014.
49.
XuY. et al., Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study, Transport Res Part E87 (2016), 130–148.
50.
YangL.GaoZ. and LiK., Passenger train scheduling on a single-track or partially double-track railway with stochastic information, Eng Optim42(11) (2010), 1003–1022.
51.
YangL. et al., Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks, Transport Res Part C77 (2017), 306–208.
52.
YaoL. et al., Adaptability analysis of service facilities in transfer subway stations, Math Probl Eng (2012), 1–12.
53.
YunT.G.LeeY.I., A study on the evaluation method of level of service in transfer walking facilities, Journal of Korean Society of Transportation28(1) (2010), 143-156.
54.
YueY. et al., Optimizing train stopping patterns and schedules for high-speed passenger rail corridors, Transp Res Part C63 (2016), 126–146.
55.
ZhangG.Y.WuY.G. and LiuY.Q., An advanced wind speed multi-step ahead forecasting approach with characteristic component analysis, J Renew Sustain Energy (2014), 6.
56.
ZhangW., Research on Congestion Degree Identification and Early Warning Control of Metro Interchange Hub, Xi’an, Master’s thesis, Chang’an University, 2014.
57.
ZhouJ.B., Study on Pedestrian Traffic Characteristics and Safety Evacuation of Integrated Traffic Interchange Junction, Xi’an, Master’s thesis, Chang’an University, 2014.
58.
ZhouW.T., Study on analysis and modeling of gate pass rate of Beijing metro station, Modern Science and Technology8(8) (2009), 83–86.
59.
ZhuY., Passenger-to-train assignment model based on automated data, Massachusetts Institute of Technology, https://dspace.mit.edu/handle/1721.1/90075.2014.
60.
ZhuY.W.KoutsopoulosH.N. and WilsonN.H.M, A probabilistic passenger-to-train assignment model based on automated data, Transp Res Part B104 (2017), 522–542.
