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In this paper, the average travel time over a link is considered as a random variable following an identical probability distribution as the arrival process. A new estimation method of the average travel time uses a cross-correlation analysis of traffic flow measurement data. This method requires only traffic flow information, which is available from the measurements of single loop detectors upstream and downstream from one link. Different from the existing maximum cross-correlation analysis method, the proposed method considers average travel time as a random variable, with its mean value estimated from all significant cross-correlation coefficients rather than from only the maximum cross-correlation coefficient. Therefore, the inherent variability of average travel time among different vehicles can be considered. Moreover, different from the existing optimization method, the proposed method uses the statistical t-test of the significant cross-correlation coefficients to determine automatically and adaptively the fitting range of the probability density function of the average travel time. Thus, it avoids using the approximated car length factor and has no need to predetermine the range of the average travel time as required by the optimization method. Details of the average travel time estimation procedures are presented, and the effectiveness of the proposed method is demonstrated through both simulation study and a case study of real traffic data.

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