High-frequency short delays in the urban rail transit system result in a cumulative delay effect within the network, which in turn affects the daily operational organization. Most studies focus more on long-term disruption, but there is less research on high-frequency short delays, lacking detailed classification and definition. Based on the 13-week operation data of Nanjing Metro and the detailed division of short-delay frequency, this study constructed four panel-regression models and compared and explained the influencing factors from multiple perspectives. The results show that the negative binomial fixed-effects model has the best goodness of fit. The two-way fixed-effects model increased complexity but does not improve fitting performance compared with the fixed-effects model. There are no significant time fixed effects in the three types of frequency data. New routes, remote stations with low passenger volume, stations with more train trips, and stations with surrounding land use of commercial service facilities have a higher frequency of minor delays, highlighting the need to deploy adequate facilities at these stations.
China Association of Metros. Statistical Analysis of Urban Rail Transit Operation Data in 2023. Modern Urban Transit, Vol. 3, 2024, pp. 131–132.
2.
HuaW.OngG. P.Network Survivability and Recoverability in Urban Rail Transit Systems Under Disruption. IET Intelligent Transport Systems, Vol. 11, No. 10, 2017, pp. 641–648.
3.
ZuoF.Simulation Analysis on Passenger Flow Evolution Characteristics of Urban Rail Transit Transfer Stations Under Operation Incident. Southeast University, Nanjing, China, 2021.
4.
BismarkA.KathleenL.Unobserved Heterogeneity Analysis of Rail Transit Incident Delays. Transportation Research Part A: Policy and Practice, Vol. 117, 2018, pp. 39–43.
5.
YinJ.YangL.D’ArianoA.TangT.GaoZ.Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events. INFORMS Journal on Computing, Vol. 34, No. 6, 2022, pp. 3234–3258.
6.
ChaiS.YinJ.D’ArianoA.SamàM.TangT.Scheduling of Coupled Train Platoons for Metro Networks: A Passenger Demand-Oriented Approach. Transportation Research Record: Journal of the Transportation Research Board, 2023. 2677: 1671–1689.
7.
ItaniA.AboudinaA.DiabE.SrikukenthiranS.ShalabyA.Managing Unplanned Rail Disruptions: Policy Implications and Guidelines Towards an Effective Bus Bridging Strategy. Transportation Research Record Journal of the Transportation Research Board, 2019. 2673: 473–489.
8.
ChenT.LuY.FuX.SzeN.DingH.A Resampling Approach to Disaggregate Analysis of Bus-Involved Crashes Using Panel Data with Excessive Zeros. Accident Analysis and Prevention, Vol. 164, 2022, p. 106496.
9.
CaiY.XiangQ.LiY.MingX.Collision Prediction Model for Interchange Mergring Area Based on Traffic Conflict Technique. Proc., 3rd IEEE International Conference on Intelligent Transportation Engineering, Singapore, 2018.
10.
SerdarM. Z.KoM.Al-GhamdiS. G.Urban Transportation Networks Resilience: Indicators, Disturbances, and Assessment Methods. Sustainable Cities and Society, Vol. 76, 2022, p. 103452.
11.
ZhangJ.WangZ.WangS.ShaoW.ZhaoX.LiuW.Vulnerability Assessments of Weighted Urban Rail Transit Networks with Integrated Coupled Map Lattices. Reliability Engineering & System Safety, Vol. 214, 2021, p. 107707.
12.
NazninF.CurrieG.LoganD.SarviM.Application of a Random Effects Negative Binomial Model to Examine Tram-Involved Crash Frequency on Route Sections in Melbourne, Australia. Accident Analysis and Prevention, Vol. 92, 2016, pp. 15–21.
13.
WanS.DingX.LiuZ.YangK.ChenJ.The Research on Propagation Mechanism of Urban Rail Transit Fault Delay Based on Directed Graph and Fault Tree. Intelligent Computer and Applications, Vol. 11, No. 3, 2021, pp. 26–32.
14.
LouieJ.ShalabyA.HabibK.Modelling the Impact of Causal and Non-Causal Factors on Disruption Duration for Toronto’s Subway System: An Exploratory Investigation using Hazard Modelling. Accident Analysis and Prevention, Vol. 98, 2017, pp. 232–240.
15.
DingX.ZhaoY.LiuZ.HuH.The Modeling of Urban Rail Transit Emergency Delay Propagation Scope Under Network Operation Mode. Concurrency and Computation: Practice and Experience, Vol. 32, No. 23, 2019, p. e5530.
16.
CongC.LiX.YangS.ZhangQ.LuL.ShiY.Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach. International Journal of Environmental Research and Public Health, Vol. 19, 2022, p. 9052.
17.
ShalabyA.LiL.DiabE.Rail Transit Disruption Management: A Comprehensive Review of Strategies and Approaches. In Handbook of Public Transport Researc (CurrieG., ed.), Edward Elgar Publishing Ltd., Cheltenham, UK; Northampton, MA, 2021, pp. 280–313.
18.
DaiX.QiuH.SunL.A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System. Sustainability, Vol. 13, 2021, p. 9692.
19.
ZhangJ.RenG.SongJ.Resilience-Based Restoration Sequence Optimization for Metro Networks: A Case Study in China. Journal of Advanced Transportation, Vol. 2022, 2022, p. 8595356.
20.
LiuZ.LiuJ.HeX.WangF.The Spatial Connection and Network Feature of Zhongyuan Economic District Base on Intercity Traffic Flow. Economic Geography, Vol. 34, No. 7, 2014, pp. 58–66.
21.
SunL.XuJ.CuiX.Panel Data Models for Analysis and Prediction of Crash Count. Journal of Jilin University (Engineering and Technology Edition), Vol. 45, No. 6, 2015, pp. 1771–1778.
22.
DuncanD. B.Multiple Range and Multiple F Tests. Biometrics, Vol. 11, No. 1, 1955, pp. 1–42.
23.
HausmanJ.HallB.GrilichesZ.Econometric Models for Count Data with an Application to the Patents-R&D Relationship. Econometrica, Vol. 52, No. 4, 1984, pp. 909–938.
24.
ArellanoM.On the Testing of Correlated Effects with Panel Data. Journal of Econometrics, Vol. 59, No. 1, 1993, pp. 87–97.
25.
FaddyM. J.PettittA. N.Comparisons of Statistical Distributions for Cluster Sizes in a Developing Pandemic. BMC Medical Research Methodology, Vol. 22, 2022, p. 32.
FanL.LiuH.Econometrical Analysis of Factors Affecting Home Ownership Rate of Urban Resident in China. Journal of Shenyang Jianzhu University (Natural Science), Vol. 21, No. 4, 2005, pp. 329–332.
28.
BurnhamK. P.AndersonD. R.Multimodel Inference Understanding AIC and BIC in Model Selection. Sociological Methods & Research, Vol. 33, No. 33, 2004, pp. 261–304.
29.
DiabE.ShalabyA.Metro Transit System Resilience: Understanding the Impacts of Outdoor Tracks and Weather Conditions on Metro System Interruptions. International Journal of Sustainable Transportation, Vol. 14, No. 9, 2020, pp. 657–670.
30.
GuoJ.Optimization Scheme of Train Working Diagram for Upward Transfer at Xiangyulunan Station Under “Wet Track” Mode. Railway Transport and Economy, Vol. 44, No. 4, 2022, pp. 112–118.
