Adaptive signal control system aims to improve the operation efficiency of intersections, in which the platoon dispersion model is one of the key component modules. The problem of the traditional Robertson's model is that they are based on historical data, which cannot effectively capture the dynamic characteristics of traffic flow and be further applied to adaptive signal control systems. Thanks to the unprecedented availability of real‐time travel time data provided by the modern intelligent transportation systems, there is an opportunity to calibrate Robertson's model online using real‐time travel time data. This study proposes a dynamic Robertson's platoon dispersion model based on real‐time travel times. Then, based on field observations, the prediction error and computational efficiency of the traditional Robertson's model, the Gaussian model, and the proposed model are compared and analysed. The results show that the proposed model can better capture the law of dynamic dispersion for traffic flow. The reduced average mean square error of prediction can be as much as 16.86 and 7.07% compared with the traditional Robertson's model and the Gaussian model, respectively. In addition, the average computational time of the proposed model is 0.46 s, which can be applied in the dynamic predictive signal control systems.
Dynamic platoon dispersion model based on real‐time link travel time
IET Intelligent Transport Systems ; 13 , 11 ; 1694-1700
2019-11-01
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
real‐time link travel time , dynamic dispersion , vehicle‐to‐vehicle communication , traditional Robertson's model , real‐time travel times , time 0.46 s , traffic flow , dynamic characteristics , road traffic , dynamic platoon dispersion model , real‐time travel time data , Gaussian model , adaptive signal control system , average computational time , modern intelligent transportation systems , dynamic predictive signal control systems , dynamic Robertson's platoon dispersion model , road vehicles , Gaussian processes
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