Public transportation travel time reliability is an important issue. The experienced service reliability gap (ESRG) measures reliability based on perceived travel times. COVID-19’s impact on public transportation is still relevant. Therefore, a study investigating COVID-19’s effects on the ESRG seems necessary. This paper also compares the changes in the ESRG with changes in reliability buffer time (RBT), a measure based on nominal travel time, calculated similarly to the ESRG. Tehran’s most crowded bus rapid transit line is examined. We used automatic fare collection and vehicle location data over two time periods: the first week of December 2019 before the pandemic, and the same week of December 2020 during the pandemic (5 workdays). The data analysis relies on time series clustering and the results are interpreted using the Mann–Kendall test, time series decomposition, and passenger loading plots. While the ESRG is often greater than the RBT, their changes over time are similar. However, RBT and ESRG series may differ in that trips originated from origins experienced significant passenger load changes. Also, COVID-19 may cause the ESRG’s changes to resemble that of the RBT. Overall, it seems that the new measure of the ESRG is extremely useful when reliability and interaction with crowding are the primary focus. However, when the purpose is to assess COVID-19’s influence on reliability, it functions more like its nominal counterpart, RBT.
NesheliM. M.CederA. A.LiuT.A Robust, Tactic-Based, Real-Time Framework for Public-Transport Transfer Synchronization. Transportation Research Procedia, Vol. 9, 2015, pp. 246–268.
2.
FransenK.NeutensT.FarberS.De MaeyerP.DeruyterG.WitloxF., Identifying Public Transport Gaps Using Time-Dependent Accessibility Levels. Journal of Transport Geography, Vol. 48, 2015, pp. 176–187.
3.
AlkubatiM.KhalifaN. A.Al-barakaniH. A.An Overview of Public Transport Reliability Studies Using a Bibliometric Analysis. Ain Shams Engineering Journal, Vol. 14, No. 3, 2023, p. 101908.
4.
van OortN.Service Reliability and Urban Public Transport Design Delft University of Technology: Netherlands TRAIL Research School, Netherlands, 2011.
5.
CantwellM.CaulfieldB.O’MahonyM., Examining the Factors That Impact Public Transport Commuting Satisfaction. Journal of Public Transportation, Vol. 12, No. 2, 2009, pp. 1–21.
6.
BenezechV.CoulombelN.The Value of Service Reliability. Transportation Research Part B: Methodological, Vol. 58, 2013, pp. 1–15.
7.
LiZ.HensherD. A.Behavioural Implications of Preferences, Risk Attitudes and Beliefs in Modelling Risky Travel Choice with Travel Time Variability. Transportation, Vol. 40, 2013, pp. 505–523.
8.
WuW.LiuR.JinW.Modelling Bus Bunching and Holding Control with Vehicle Overtaking and Distributed Passenger Boarding Behaviour, Transportation Research Part B: Methodological, Vol. 104, 2017, pp. 175–197.
9.
WoodD.A Framework for Measuring Passenger-Experienced Transit Reliability using Automated Data. Master thesis. Massachusetts Institute of Technology, MA, 2015.
10.
DiabE. I.BadamiM. G.El-GeneidyA. M.Bus Transit Service Reliability and Improvement Strategies: Integrating the Perspectives of Passengers and Transit Agencies in North America. Transport Reviews, Vol. 35, No. 3, 2015, pp. 292–328.
11.
JeneliusE.Public Transport Experienced Service Reliability: Integrating Travel Time and Travel Conditions. Transportation Research Part A: Policy and Practice, Vol. 117, 2018, pp. 275–291.
12.
UnimanD. L.AttanucciJ.MishalaniR. G.WilsonN. H.Service Reliability Measurement Using Automated Fare Card Data: Application to the London Underground. Transportation Research Record: Journal of the Transportation Research Board, 2010. 2143: 92–99.
13.
ShafaatiM.SaffarzadehM.In Light of the Automated Fare Collection Data, How Did the Travel Patterns of Transit Riders in Tehran Change Following COVID-19?International Journal of Transportation Engineering, Iran, 2023.
14.
CederA.Public Transit Planning and Operation: Modeling, Practice and Behavior. CRC Press, Boca Raton, FL, 2016.
15.
LeurentF.BenezechV.CombesF.A Stochastic Model of Passenger Generalized Time Along a Transit Line. Procedia-Social and Behavioral Sciences, Vol. 54, 2012, pp. 785–797.
16.
HaghaniM.MerkertR.BehnoodA.De GruyterC.KazemzadehK.GhaderiH.ShahhoseiniZ.ThaiV.IrannezhadE.FahimniaB.How COVID-19 Transformed the Landscape of Transportation Research: An Integrative Scoping Review and Roadmap for Future Research. Transportation Letters, Vol. 16, No. 1, 2024, pp. 43–88.
17.
ShafaatiM.SaffarzadehM.In Light of the Automated Fare Collection Data, How Did the Travel Patterns of Transit Riders in Tehran Change Following COVID-19?International Journal of Transportation Engineering, 2023.
18.
HsiehH.-S.Understanding Post-COVID-19 Hierarchy of Public Transit Needs: Exploring Relationship Between Service Attributes, Satisfaction, and Loyalty. Journal of Transport & Health, Vol. 32, 2023, p. 101656.
19.
SungH.DabrundashviliN.BaekS.Mode-Specific Impacts of Social Distancing Measures on the Intra- and Inter-Urban Mobility of Public Transit in Seoul During the COVID-19 Pandemic. Sustainable Cities and Society, Vol. 98, 2023, p. 104842.
20.
BwambaleA.UzonduC.IslamM.RahmanF.BatoolZ.MukwayaP. I.WadudZ.Willingness to Pay for COVID-19 Mitigation Measures in Public Transport and Paratransit in Low-Income Countries. Transportation Research Part A: Policy and Practice, Vol. 167, 2023, p. 103561.
21.
JinS. T.KongH.WuR.SuiD. Z.Ridesourcing, the Sharing Economy, and the Future of Cities. Cities, Vol. 76, 2018, pp. 96–104.
22.
PimentaV.QuilliotA.ToussaintH.VigoD.Models and Algorithms for Reliability-Oriented Dial-a-Ride with Autonomous Electric Vehicles. European Journal of Operational Research, Vol. 257, No. 2, 2017, pp. 601–613.
23.
KucharskiR.FielbaumA.Alonso-MoraJ.CatsO.If You Are Late, Everyone Is Late: Late Passenger Arrival and Ride-Pooling Systems’ Performance. Transportmetrica A: Transport Science, Vol. 17, No. 4, 2021, pp. 1077–1100.
24.
Soza-ParraJ.RaveauS.MuñozJ. C.CatsO.The Underlying Effect of Public Transport Reliability on Users’ Satisfaction. Transportation Research Part A: Policy and Practice, Vol. 126, 2019, pp. 83–93.
25.
AlcornL. G.KarnerA.Integrating Formal and Informal Transit into One Hybrid Passenger Transport System in Lagos, Nigeria. Transportation, Vol. 48, No. 3, 2021, pp. 1361–1377.
26.
van OortN.Incorporating Service Reliability in Public Transport Design and Performance Requirements: International Survey Results and Recommendations. Research in Transportation Economics, Vol. 48, 2014, pp. 92–100.
27.
LiuJ.SchonfeldP. M.YinY.PengQ.Effects of Link Capacity Reductions on the Reliability of an Urban Rail Transit Network. Journal of Advanced Transportation, Vol. 2020, No. 1, 2020, p. 9020574.
28.
SadraniM.TirachiniA.AntoniouC.Optimization of Service Frequency and Vehicle Size for Automated Bus Systems with Crowding Externalities and Travel Time Stochasticity. Transportation Research Part C: Emerging Technologies, Vol. 143, 2022, p. 103793.
29.
TongP.YanY.WangD.QuX.Optimal Route Design of Electric Transit Networks Considering Travel Reliability. Computer-Aided Civil and Infrastructure Engineering, Vol. 36, No. 10, 2021, pp. 1229–1248.
30.
LiuY.ZhangH.JiaJ.ShiB.WangW.Understanding Urban Bus Travel Time: Statistical Analysis and a Deep Learning Prediction. International Journal of Modern Physics B, Vol. 37, No. 4, 2023, p. 2350034.
31.
FrenchC.O’MahonyM.Using Automatic Vehicle Location System Data to Assess Impacts of Weather on Bus Journey Times for Different Bus Route Types. Proc., 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, IEEE, New York, 2021, pp. 2137–2144.
32.
LiuJ.SchonfeldP.ChenJ.YinY.PengQ.Perceived Trip Time Reliability and Its Cost in a Rail Transit Network. Sustainability, Vol. 13, No. 13, 2021, p. 7504.
33.
ShafaatiM.SaffarzadehM.Does Crowding Have a More Complicated Effect on Public Transport Users with Respect to Perceived Travel Time?Transportation Research Record: Journal of the Transportation Research Board, 2024. 2678: 389–422.
34.
AlsgerA. A.MesbahM.FerreiraL.SafiH.Use of Smart Card Fare Data to Estimate Public Transport Origin–Destination Matrix. Transportation Research Record: Journal of the Transportation Research Board, 2015. 2535: 88–96.
35.
Jafari KangM.AtaeianS.AmiripourS. M.A Procedure for Public Transit OD Matrix Generation Using Smart Card Transaction Data. Public Transport, Vol. 13, 2021, pp. 81–100.
36.
SchmöckerJ.-D.FonzoneA.ShimamotoH.KurauchiF.BellM. G.Frequency-Based Transit Assignment Considering Seat Capacities. Transportation Research Part B: Methodological, Vol. 45, No. 2, 2011, pp. 392–408.
37.
HörcherD.GrahamD. J.AndersonR. J.Crowding Cost Estimation with Large Scale Smart Card and Vehicle Location Data. Transportation Research Part B: Methodological, Vol. 95, 2017, pp. 105–125.
38.
WardmanM.WhelanG.Twenty Years of Rail Crowding Valuation Studies: Evidence and Lessons from British Experience. Transport Reviews, Vol. 31, No. 3, 2011, pp. 379–398.
39.
JavedA.LeeB. S.RizzoD. M.A Benchmark Study on Time Series Clustering. Machine Learning with Applications, Vol. 1, 2020, p. 100001.
40.
WuX.KumarV.The Top Ten Algorithms in Data Mining, 2nd ed.CRC Press, Taylor and Francis group, 2009, pp. 133–143.
41.
AsadiR.ReganA.Clustering of Time Series Data with Prior Geographical Information. arXiv Preprint arXiv:2107.01310, 2021.
42.
SundarkumarG. G.BVS. B.MunigetyC. R.AroraA. S.A Time Series Clustering Based Approach for Construction of Real-World Drive Cycles. Transportation Research Part D: Transport and Environment, Vol. 97, 2021, p. 102896.
43.
Alonso de ArmiñoC.ManzanedoM. ÁHerreroÁ.Analysing the Intermeshed Patterns of Road Transportation and Macroeconomic Indicators Through Neural and Clustering Techniques. Pattern Analysis and Applications, Vol. 23, No. 3, 2020, pp. 1059–1070.
44.
HeL.AgardB.TrépanierM.A Classification of Public Transit Users with Smart Card Data Based on Time Series Distance Metrics and a Hierarchical Clustering Method. Transportmetrica A: Transport Science, Vol. 16, No. 1, 2020, pp. 56–75.
45.
AlmannaaM. H.ElhenawyM.RakhaH. A.A Novel Supervised Clustering Algorithm for Transportation System Applications. IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 1, 2019, pp. 222–232.
46.
MacQueenJ.Some Methods for Classification and Analysis of Multivariate Observations. Proc., fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, University of California Press, 1967, pp. 281–297.
47.
AliM.AlqahtaniA.JonesM. W.XieX.Clustering and Classification for Time Series Data in Visual Analytics: A Survey. IEEE Access, Vol. 7, 2019, pp. 181314–181338.
48.
PaparrizosJ.GravanoL.Fast and Accurate Time-Series Clustering. ACM Transactions on Database Systems (TODS), Vol. 42, No. 2, 2017, pp. 1–49.
49.
FaloutsosC.RanganathanM.ManolopoulosY.Fast Subsequence Matching in Time-Series Databases. ACM Sigmod Record, Vol. 23, No. 2, 1994, pp. 419–429.
50.
PetitjeanF.KetterlinA.GançarskiP.A Global Averaging Method for Dynamic Time Warping, with Applications to Clustering. Pattern Recognition, Vol. 44, No. 3, 2011, pp. 678–693.
51.
ChabchoubY.FrickerC.Classification of the Vélib Stations Using Kmeans, Dynamic Time Wraping and DBA Averaging Method. Proc., 2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Paris, France, IEEE, New York, 2014, pp. 1–5.
52.
MüllerM.Dynamic Time Warping. In Information Retrieval for Music and Motion (MüllerM., ed.), Springer Berlin, Heidelberg, 2007, pp. 69–84.
53.
RakthanmanonT.CampanaB.MueenA.BatistaG.WestoverB.ZhuQ.ZakariaJ.KeoghE.Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 7, No. 3, 2013, pp. 1–31.
54.
DaviesD. L.BouldinD. W.A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-1, No. 2, 1979, pp. 224–227.
55.
CalińskiT.HarabaszJ.A Dendrite Method for Cluster Analysis. Communications in Statistics-Theory and Methods, Vol. 3, No. 1, 1974, pp. 1–27.
56.
RousseeuwP. J.Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, Vol. 20, 1987, pp. 53–65.
57.
LiL.SuX.ZhangY.LinY.LiZ.Trend Modeling for Traffic Time Series Analysis: An Integrated Study. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 6, 2015, pp. 3430–3439.
58.
ShekharS.WilliamsB. M.Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow. Transportation Research Record: Journal of the Transportation Research Board, 2007. 2024: 116–125.
59.
AlekseevK.SeixasJ.A Multivariate Neural Forecasting Modeling for Air Transport–Preprocessed by Decomposition: A Brazilian Application. Journal of Air Transport Management, Vol. 15, No. 5, 2009, pp. 212–216.
60.
de NaillyP.CômeE.SaméA.OukhellouL.FerriereJ.Merad-BoudiaY., What Can We Learn from 9 Years of Ticketing Data at a Major Transport Hub? A Structural Time Series Decomposition. Transportmetrica A: Transport Science, Vol. 18, No. 3, 2022, pp. 1445–1469.
61.
SharmaD.KumarB.ChandS.A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test. International Journal of Intelligent Systems and Applications, Vol. 11, No. 2, 2019, pp. 70–82.
