This paper demonstrates the capabilities of wavelet transform (WT) for analyzing importantudfeatures related to bottleneck activations and traffic oscillations in congested trafficudin a systematic manner. In particular, the analysis of loop detector data from a freewayudshows that the use of wavelet-based energy can effectively identify the location of anudactive bottleneck, the arrival time of the resulting queue at each upstream sensor location,udand the start and end of a transition during the onset of a queue. Vehicle trajectories wereudalso analyzed using WT and our analysis shows that the wavelet-based energies of individualudvehicles can effectively detect the origins of deceleration waves and shed light on possibleudtriggers (e.g., lane-changing). The spatiotemporal propagations of oscillationsudidentified by tracing wavelet-based energy peaks from vehicle to vehicle enable analysisudof oscillation amplitude, duration and intensity.
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