This paper develops a framework to test Bottom-up segmentation and Wavelet transform capability to distinguish on-peak from off-peak periods given the time series of the travel time. The proposed techniques are tested on the times series of travel time obtained from 15 working days of Bluetooth data on Brisbane’s busiest urban corridor. This study shows that the peak period can be systematically determined from either Bottom-up segmentation or WT on the time series of travel times. The Bottom-up segmentation technique estimated a mean peak period over the 15 working days of 106 min, compared to 99 min with Wavelet transformation. Further investigation is warranted should a recommendation be made as to which technique can more reliably estimate peak period.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Systematic Traffic Peak Period Identification Using Bottom-Up Segmentation and Wavelet Transformation


    Additional title:

    Int. J. ITS Res.


    Contributors:


    Publication date :

    2020-05-01


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic Composition during the Morning Peak Period

    Tom Cherrett / Mike McDonald | DOAJ | 2002

    Free access

    Traffic Pattern Identification Using Wavelet Transforms

    National Research Council (U.S.) | British Library Conference Proceedings | 2005


    Peak-period traffic congestion : options for current programs

    Remak, Roberta / Rosenbloom, Sandra | TIBKAT | 1976


    Traffic Peak Period Detection from an Image Processing View

    Jianli Xiao / Hang Li / Xiang Wang et al. | DOAJ | 2018

    Free access

    Impacts of Incentive-Based Intervention on Peak Period Traffic

    Kumar, Vivek / Bhat, Chandra R. / Pendyala, Ram M. et al. | Transportation Research Record | 2016