Open AccessDynamic platoon dispersion model based on real‐time link travel time
Author(s) -
Yao Zhihong,
Xu Taorang,
Cheng Yang,
Qin Lingqiao,
Jiang Yangsheng,
Ran Bin
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0098
Subject(s) - platoon , intelligent transportation system , traffic flow (computer networking) , computer science , signal (programming language) , dispersion (optics) , model predictive control , real time control system , real time data , real time computing , simulation , engineering , artificial intelligence , control (management) , civil engineering , physics , computer security , optics , world wide web , programming language
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.