Abstract
Estimating average travel speed (ATS) on urban streets is essential for travel planning, traffic guidance, and congestion control. Floating car data has been widely applied in estimating ATS on urban streets. The harmonic average of the floating car speed is mostly used as the true average speed in research and practice. However, the estimation accuracy of this practice is affected by the travel time distribution (TTD) and the sampling rate of floating cars, especially when facing the interrupted flow on urban streets. To address this problem, this work develops a framework for estimating ATS of urban streets based on TTD. We analyze the characteristics of TTD by using the fully sampled trajectory data observed on urban roads in Shanghai, China. It is found that the travel time of stopped and unstopped vehicles show different statistical distribution characteristics, which should be considered in estimating ATS. The focus of this method is to divide vehicles on urban roads into stopped vehicles and unstopped vehicles, and to study the distribution laws of their travel times. Based on these laws, we improve the existing ATS estimation method. In addition, this paper further discusses the sampling rate of floating cars for estimating ATS by the random sampling method. The results show that the recommended sampling rate is 5%–7%, which is slightly higher than the sampling rate of 3%–5% in the existing research. Finally, the experiment based on empirical data substantiates the superior performance of the proposed method in comparison to the harmonic average method.
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