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Estimating preferred departure times of road users in a large urban network
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap, Transportplanering, ekonomi och teknik. VTI.ORCID-id: 0000-0002-3738-9318
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap, Transportplanering, ekonomi och teknik.ORCID-id: 0000-0002-4531-8659
2018 (engelsk)Inngår i: Transportation, ISSN 0049-4488, E-ISSN 1572-9435, Vol. 45, nr 3, s. 767-787Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In order to reliably predict and assess effects of congestion charges and other congestion mitigating measures, a transportation model including dynamic assignment and departure time choice is important. This paper presents a transport model that incorporates departure time choice for analysis of road users’ temporal adjustments and uses a mesoscopic traffic simulation model to capture the dynamic nature of congestion. Departure time choice modelling relies heavily on car users’ preferred times of travel and without knowledge of these no meaningful conclusions can be drawn from application of the model. This paper shows how preferred times of travel can be consistently derived from field observations and conditional probabilities of departure times using a reverse engineering approach. It is also shown how aggregation of origin–destination pairs with similar preferred departure time profiles can solve the problem of negative solutions resulting from the reverse engineering equation. The method is shown to work well for large-scale applications and results are given for the network of Stockholm.

sted, utgiver, år, opplag, sider
Elsevier, 2018. Vol. 45, nr 3, s. 767-787
Emneord [en]
Transportation modelling, Departure time choice, Preferred departure times, Reverse engineering, Congestion charging, Dynamic traffic assignment
HSV kategori
Forskningsprogram
Transportvetenskap
Identifikatorer
URN: urn:nbn:se:kth:diva-200681DOI: 10.1007/s11116-016-9750-2ISI: 000430023300004Scopus ID: 2-s2.0-85000932393OAI: oai:DiVA.org:kth-200681DiVA, id: diva2:1070204
Forskningsfinansiär
Vinnova
Merknad

QC 20170201. QC 20191111

Tilgjengelig fra: 2017-01-31 Laget: 2017-01-31 Sist oppdatert: 2019-11-11bibliografisk kontrollert

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