Abstract
The Mixed distribution model of time headway plays an important role in Intelligent Transportation System. A more accurate improved model based on a new traffic state classification method and correlation coefficient method was established in this study. Upon analyzing traffic data from three unban roads in Nanjing, China from December 8, 2017 to April 24, 2018 in clear days free of fogs and haze, it was found that 1) the improved model dwarfs the other models, for it boasts higher goodness-of-fit and it represents the only one to successfully pass the Chi-square test. 2) the improved model shows better performance in application because the relative errors between the field traffic rate and calculated traffic rate obtained from the improved model are the smallest. 3) the goodness-of-fit of the improved model is affected by traffic state, and the fitting precision of the improved model under four states is the best. Thus, it is hard to tell performance of which model established under two states or three-state model is better. 4) correlation coefficients between time headway and the absolute value of relative speed make the improved Mixed model more fitted and precise. The improved model proposed in this study is a more accurate time headway distribution model.
Highlights
Time headway is a fundamental measure in both traffic flow theory and transportation applications [1]
The main objective of this study is to propose a more accurate time headway distribution model based on a new traffic state classification method combining both the macroscopic traffic flow information and microscopic traffic flow information
The Mixed distribution model, which is the original model of the improved model, and the classical models that are used in the process of establishing the improved model, are utilized for comparative analysis to verify the accuracy of the improved model
Summary
Time headway is a fundamental measure in both traffic flow theory and transportation applications [1]. It is usually defined as the time between two successive vehicles as they pass the same common feature (e.g., front/rear bump) of both vehicles [2]. Various distribution models have been proposed to describe the probability of a particular value or value range of time headway [2]. Traffic rate can be predicted based on the relationship between traffic rate and time headway by using time headway distribution according to probability theory [4], [5]. Traffic managers can smooth traffic stream, reduce traffic congestion, design traffic signal timing, and evaluate passenger satisfaction [5]–[8]
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