This paper proposes a methodology for online prediction of travel time reliability based on real-world measurements in light of the probabilistic character of traffic breakdown. A discrete time Markov chain is developed to represent the flow breakdown event. Through the calibrated transition probability matrix of the Markov chain, the probability of flow breakdown or recovery along a given facility can be estimated. Travel time reliability that captures the probability of flow breakdown and the conditional expected delay associated with occurrence of breakdown can therefore be predicted. Because both the mean and the variance of travel time are represented as functions of current traffic conditions and calibrated for each road section on the basis of field data, travel time and its reliability could be obtained offline by analyzing historical data and computed online when real-time measurements are available.
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References
1.
BovyP. H., and SteinE.Route Choice: Wayfinding in Transport Networks.Kluwer, Norwell, Mass.,1990.
2.
NolandR. B., and PolakJ. W.Travel Time Variability: A Review of Theoretical and Empirical Issues.Transport Reviews, Vol. 22, No. 1, 2002, pp. 39–54.
3.
NicholsonA., SchmockerJ., BellM., and IidaY.Assessing Transportation Reliability: Malevolence and User Knowledge. In The Network Reliability of Transport: Proceedings of the 1st International Symposium on Transportation Network Reliability (BellM., and IidaY., eds.), Elsevier, Amsterdam, Netherlands,2003.
4.
ChenA., YangH., LoH. K., and TangW.Capacity Reliability for a Road Network: An Assessment Methodology and Numerical Results.Transportation Research Part B, Vol. 36, 2002, pp. 225–252.
5.
Al-DeekH., and EmamE. B.New Methodology for Estimating Reliability in Transportation Networks with Degraded Link Capacities.Journal of Intelligent Transportation Systems, Vol. 10, No. 3, 2006, pp. 117–129.
6.
ClarkS., and WatlingD.Modelling Network Travel Time Reliability Under Stochastic Demand.Transportation Research Part B, Vol. 39, No. 2, 2005, pp. 119–140.
7.
ChenC., SkabardonisA., and VaraiyaP.Travel-Time Reliability as a Measure of Service. In Transportation Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 74–79.
8.
PersaudB., YagarS., and BrownleeR.Exploration of the Breakdown Phenomenon in Freeway Traffic. In Transportation Research Record 1634, TRB, National Research Council, Washington, D.C., 1998, pp. 64–69.
9.
ElefteriadouL., RoessR. P., and McShaneW. R.Probabilistic Nature of Breakdown at Freeway Merge Junctions. In Transportation Research Record 1484, TRB, National Research Council, Washington, D.C., 1995, pp. 80–89.
10.
KernerB. S.Theory of Breakdown Phenomenon at Highway Bottlenecks. In Transportation Research Record: Journal of the Transportation Research Board, No. 1710, TRB, National Research Council, Washington, D.C., 2000, pp. 136–144.
11.
EvansJ. L., ElefteriadouL., and GautamN.Probability of Breakdown at Freeway Merges Using Markov Chains.Transportation Research Part B, Vol. 35, 2001, pp. 237–254.
12.
BrilonW., GeistefeldtJ., and ReglerM.Reliability of Freeway Traffic Flow: A Stochastic Concept of Capacity.Proc., 16th International Symposium on Transportation and Traffic Theory: Transportation and Traffic Theory. Flow, Dynamics and Human Interaction. Elsevier Ltd., Oxford, United Kingdom, 2005, pp. 125–144.
KernerB. S.The Physics of Traffic.Springer, Heidelberg, Germany, 2004.
15.
SchönhofM., and HelbingD.Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling.Transportation Science, Vol. 41, No. 2, 2007, pp. 135–166.
16.
LawlessJ. F.Statistical Models and Methods for Lifetime Data.Wiley, New York, 2003.
17.
BanksJ.Two-Capacity Phenomenon at Freeway Bottlenecks: A Basis for Ramp Metering? In Transportation Research Record 1320, TRB, National Research Council, Washington, D.C., 1991, pp. 83–90.
18.
CassidyM., and BertiniR.Some Traffic Features at Freeway Bottlenecks.Transportation Research Part B, Vol. 33, No. 1, 1999, pp. 25–42.
19.
ChungK., RudjanakanoknadJ., and CassidyM. J.Relation Between Traffic Density and Capacity Drop at Three Freeway Bottlenecks.Transportation Research Part B, Vol. 41, No. 1, 2007, pp. 82–95.
20.
PayneH. J.Discontinuity in Equilibrium Freeway Traffic Flow. In Transportation Research Record 971, TRB, National Research Council, Washington, D.C., 1984, pp. 140–146.
21.
KulkarniV. G.Modeling and Analysis of Stochastic Systems.Chapman & Hall, London, 1995.
22.
Bureau of Public Roads.Traffic Assignment Manual.U.S. Department of Commerce, 1964.
23.
GerloughD. L., and HuberM. J.Special Report 165: Traffic Flow Theory: A Monograph.TRB, National Research Council, Washington, D.C., 1976, pp. 63–66.
24.
WaltersA. A.The Theory and Measurement of Private and Social Cost of Highway Congestion.Econometrica, Vol. 29, No. 4, 1961, pp. 676–699.
25.
LiR., RoseG., and SarviM.Using Automatic Vehicle Identification Data to Gain Insight into Travel Time Variability and Its Causes. In Transportation Research Record: Journal of the Transportation Research Board, No. 1945, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 24–32.
26.
MahmassaniH. S., and ZhouX.Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework. In Advances in Control, Communication Networks, and Transportation Systems, In Honor of Pravin Varaiya (AbedE., ed.), Birkhauser, Boston, Mass.,2005.
