A data-driven hybrid control framework to improve transit performance. (October 2019)

• Record Type: Journal Article
• Authors: Wang, Wensi; Liu, Jiaming; Yao, Baozhen; Jiang, Yonglei; Wang, Yunpeng; Yu, Bin
• Is Part Of: Transportation research. Volume 107(2019)
• Page Start: 387
• Page End: 410
• Publication Date: 2019-10
• Subjects: Data-driven hybrid control -- Transit performance -- Machine learning -- Random forest model; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.08.017
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: This paper presents a data-driven hybrid control (DDHC) framework. DDHC framework composed of data-driven control module, performance module, and optimization module. Control strategies including acceleration strategy and deceleration strategy. The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. Abstract: This paper presents a data-driven hybrid control (DDHC) framework that can arrange adaptive control strategies for vehicles to effectively improve the transit performance of the public transport system. The framework depicts a powerful combination of a data-driven control method that is used to imitate the control behaviour of dispatchers and a mathematical optimization method. Three components comprise the DDHC framework: a data-driven control module, a performance module, and an optimization module. The data-driven control module contains a random forest model which is adopted to justify whether to intervene in the operation of a bus line, and if so, which vehicles should be controlled and what type of control strategy should be taken – an acceleration strategy or deceleration strategy. The performance module including vehicle operation state models is used to describe the system evolution. The last component optimizes the specific control actions – which type of acceleration or deceleration strategy should be adopted – by minimizing total passenger travel time. The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. The results show that the DDHC framework with reasonable parameters can suit the needs of real-time control in complex traffic environments.