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  • 1.
    Jenelius, Erik
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Kazagli, Evanthia
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Börjesson, Maria
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Förstudie av möjligheterna att knyta trängselskatten till ett trängselindex2012Report (Other academic)
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  • 2.
    Kazagli, Evanthia
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Koutsopoulos, Haris N.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Estimation of Arterial Travel Time from Automatic Number Plate Recognition Data2013In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, no 2391, p. 22-31Article in journal (Refereed)
    Abstract [en]

    Automatic vehicle identification (AVI) systems are increasingly used for the collection of traffic data in urban and freeway networks. Several methods have been proposed for the estimation of travel times from AVI data, mainly for freeways. The problem of estimation of travel times in urban networks was examined. The main difference between freeway and urban networks is that urban network AVI data are often extremely noisy. A major part of that noise is attributed to vehicles that do not traverse the monitored section directly but stop for various reasons. A mixture model was proposed to capture the underlying states of the measurements of AVI travel times in urban areas. The hypothesis was that travel times are drawn from two (or more) populations, one representing normal movement through the network and one representing vehicles that stop for whatever reason. The method was applied with AVI data (collected through a system for automatic recognition of number plates) from a number of corridors in central Stockholm, Sweden. The model was estimated as a mixture-of two lognormal distributions, and bootstrap standard errors were calculated. The results illustrate the robustness of the method and its ability to identify the underlying distribution of the latent populations consistent with the characteristics of each route, while standard methods for outlier removal fail.

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