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Article: Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment

TitleStochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment
Authors
KeywordsCell transmission model
Dynamic link model
Macroscopic stochastic dynamic traffic model
Traffic flow
Issue Date2011
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trb
Citation
Transportation Research Part B: Methodological, 2011, v. 45 n. 3, p. 507-533 How to Cite?
AbstractThe paper proposes a first-order macroscopic stochastic dynamic traffic model, namely the stochastic cell transmission model (SCTM), to model traffic flow density on freeway segments with stochastic demand and supply. The SCTM consists of five operational modes corresponding to different congestion levels of the freeway segment. Each mode is formulated as a discrete time bilinear stochastic system. A set of probabilistic conditions is proposed to characterize the probability of occurrence of each mode. The overall effect of the five modes is estimated by the joint traffic density which is derived from the theory of finite mixture distribution. The SCTM captures not only the mean and standard deviation (SD) of density of the traffic flow, but also the propagation of SD over time and space. The SCTM is tested with a hypothetical freeway corridor simulation and an empirical study. The simulation results are compared against the means and SDs of traffic densities obtained from the Monte Carlo Simulation (MCS) of the modified cell transmission model (MCTM). An approximately two-miles freeway segment of Interstate 210 West (I-210W) in Los Ageles, Southern California, is chosen for the empirical study. Traffic data is obtained from the Performance Measurement System (PeMS). The stochastic parameters of the SCTM are calibrated against the flow-density empirical data of I-210W. Both the SCTM and the MCS of the MCTM are tested. A discussion of the computational efficiency and the accuracy issues of the two methods is provided based on the empirical results. Both the numerical simulation results and the empirical results confirm that the SCTM is capable of accurately estimating the means and SDs of the freeway densities as compared to the MCS. © 2010 Elsevier Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/135056
ISSN
2015 Impact Factor: 3.769
2015 SCImago Journal Rankings: 3.905
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Polytechnic UniversityA-PH65
Research Grants Council of the Hong Kong Special Administration RegionPolyU 5271/08E
Funding Information:

This research is jointly sponsored by the project funded by University Research Grant A-PH65 from the Hong Kong Polytechnic University, and the project supported by the Research Grants Council of the Hong Kong Special Administration Region under Grant Project No. PolyU 5271/08E. The authors would like to thank Dr. Gabriel Gomes, Dr. Lyudmila Mihaylova, and the two anonymous referees for their constructive comments and suggestions, which led to improvements in the study. Special thanks should also go to the Freeway Performance Measurement (PeMS) Project which provides the data in the empirical study.

References

 

DC FieldValueLanguage
dc.contributor.authorSumalee, Aen_HK
dc.contributor.authorZhong, RXen_HK
dc.contributor.authorPan, TLen_HK
dc.contributor.authorSzeto, WYen_HK
dc.date.accessioned2011-07-27T01:27:22Z-
dc.date.available2011-07-27T01:27:22Z-
dc.date.issued2011en_HK
dc.identifier.citationTransportation Research Part B: Methodological, 2011, v. 45 n. 3, p. 507-533en_HK
dc.identifier.issn0191-2615en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135056-
dc.description.abstractThe paper proposes a first-order macroscopic stochastic dynamic traffic model, namely the stochastic cell transmission model (SCTM), to model traffic flow density on freeway segments with stochastic demand and supply. The SCTM consists of five operational modes corresponding to different congestion levels of the freeway segment. Each mode is formulated as a discrete time bilinear stochastic system. A set of probabilistic conditions is proposed to characterize the probability of occurrence of each mode. The overall effect of the five modes is estimated by the joint traffic density which is derived from the theory of finite mixture distribution. The SCTM captures not only the mean and standard deviation (SD) of density of the traffic flow, but also the propagation of SD over time and space. The SCTM is tested with a hypothetical freeway corridor simulation and an empirical study. The simulation results are compared against the means and SDs of traffic densities obtained from the Monte Carlo Simulation (MCS) of the modified cell transmission model (MCTM). An approximately two-miles freeway segment of Interstate 210 West (I-210W) in Los Ageles, Southern California, is chosen for the empirical study. Traffic data is obtained from the Performance Measurement System (PeMS). The stochastic parameters of the SCTM are calibrated against the flow-density empirical data of I-210W. Both the SCTM and the MCS of the MCTM are tested. A discussion of the computational efficiency and the accuracy issues of the two methods is provided based on the empirical results. Both the numerical simulation results and the empirical results confirm that the SCTM is capable of accurately estimating the means and SDs of the freeway densities as compared to the MCS. © 2010 Elsevier Ltd.en_HK
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/trben_HK
dc.relation.ispartofTransportation Research Part B: Methodologicalen_HK
dc.subjectCell transmission modelen_HK
dc.subjectDynamic link modelen_HK
dc.subjectMacroscopic stochastic dynamic traffic modelen_HK
dc.subjectTraffic flowen_HK
dc.titleStochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignmenten_HK
dc.typeArticleen_HK
dc.identifier.emailSzeto, WY:ceszeto@hku.hken_HK
dc.identifier.authoritySzeto, WY=rp01377en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.trb.2010.09.006en_HK
dc.identifier.scopuseid_2-s2.0-79951508126en_HK
dc.identifier.hkuros187894en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79951508126&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume45en_HK
dc.identifier.issue3en_HK
dc.identifier.spage507en_HK
dc.identifier.epage533en_HK
dc.identifier.eissn1879-2367-
dc.identifier.isiWOS:000288421400004-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridSumalee, A=14326110000en_HK
dc.identifier.scopusauthoridZhong, RX=36599474200en_HK
dc.identifier.scopusauthoridPan, TL=36599154600en_HK
dc.identifier.scopusauthoridSzeto, WY=7003652508en_HK
dc.identifier.citeulike8182887-

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