A connected-vehicle-based dynamic control model for managing the bus bunching problem with capacity constraints. Issue 6 (18th August 2017)

• Record Type: Journal Article
• Authors: Luo, Xia; Liu, Shuozhi; Jin, Peter J.; Jiang, Xiaowen; Ding, Hongfei
• Is Part Of: Transportation planning and technology. Volume 40:Issue 6(2017)
• Page Start: 722
• Page End: 740
• Publication Date: 2017-08-18
• Subjects: Connected vehicle -- dynamic control -- bus bunching -- capacity constraint -- optimal control -- validation; Transportation -- Periodicals; Transportation -- Research -- Periodicals; Local transit -- Periodicals; Transportation and state -- Periodicals; 388
• DOI: 10.1080/03081060.2017.1325147
• Languages: English
• ISSNs: 0308-1060

Abstract

ABSTRACT: This paper describes a connected-vehicle-based system architecture which can provide more precise and comprehensive information on bus movements and passenger status. Then a dynamic control method is proposed using connected vehicle data. Traditionally, the bus bunching problem has been formulated into one of two types of optimization problem. The first uses total passenger time cost as the objective function and capacity, safe headway, and other factors as constraints. Due to the large number of scenarios considered, this type of framework is inefficient for real-time implementation. The other type uses headway adherence as the objective and applies a feedback control framework to minimize headway variations. Due to the simplicity in the formulation and solution algorithms, the headway-based models are more suitable for real-time transit operations. However, the headway-based feedback control framework proposed in the literature still assumes homogeneous conditions at all bus stations, and does not consider restricting passenger loads within the capacity constraints. In this paper, a dynamic control framework is proposed to improve not only headway adherence but also maintain the stability of passenger load within bus capacity in both homogenous and heterogeneous situations at bus stations. The study provides the stability conditions for optimal control with heterogeneous bus conditions and derives optimal control strategies to minimize passenger transit cost while maintaining vehicle loading within capacity constraints. The proposed model is validated with a numerical analysis and case study based on field data collected in Chengdu, China. The results show that the proposed model performs well on high-demand bus routes.