Multistate Traffic Flow Headway Fitting
Influenced by a variety of factors, such as driving skill, traffic composition, signal control, traffic management, and traffic environment and so on, the traffic flow characteristic parameter statistics embodies multi-state characteristic which is incapable to describe precisely by using a single distribution model. In order to fit the headway characteristic parameter of multi-state traffic flow, based on the denseness of mixture Gamma distribution, the data could be fitted more accurately with the method of expectation maximization algorithm and by means of controlling the number of branches under the condition of precision beyond 95%. Take actual survey data as the example, this paper compares the fitting errors of negative exponential distribution, Erlang distribution and mixture Gamma distribution and the result shows that the fitting result based on mixture Gamma distribution is closer to actual case.
Chaohe Chen, Yong Huang and Guangfan Li
H. Ding et al., "Multistate Traffic Flow Headway Fitting", Advanced Materials Research, Vols. 243-249, pp. 4426-4429, 2011