การพัฒนาฟังก์ชันเดฟิซิตสำหรับระบบขนส่งสาธารณะไร้คนขับแบบโมดูล

Abstract

The development of technology in public transportation, such as autonomous vehicles and advanced communication systems, has been leveraged to create Autonomous Modular Public Transit (AMPT). AMPT is a modern and flexible public transportation system that enhances operational efficiency. However, the increased complexity in management poses challenges, particularly in determining the Minimum Fleet Size (MFS) required for AMPT to operate at maximum efficiency. To address this issue, researchers have utilized the Deficit Function, an optimization problem represented graphically, to analyze and find the minimum number of vehicles needed for efficient public transportation services and also illustrate the activities occurring during different time intervals of service, empowering system managers to work more efficiently with data and improve overall system performance. However, the traditional Deficit Function may not be directly applicable to the AMPT system due to its modular nature and the ability to change modules during service. In this research, a Developed Deficit Function has been proposed, building upon the Extended Deficit Function, to address the specific challenges of the Autonomous Modular Public Transit (AMPT) system. The AMPT system allowed flexibility to interchange modules during the service, which is the key factor in this research. The Developed Deficit Function has been designed to apply to the AMPT system called Dynamic Autonomous Road Transit or DART which is developed by TUMCREATE. In this study, the Chiang Mai University mass transit network was used in the research. The results demonstrated the effectiveness of the Developed Deficit Function. The function is adaptable to module configurations of DART in route changing was adopted to the case study and efficiently reduced the number of vehicles required during specific time periods. This outcome demonstrated that the Developed Deficit Function has significantly contributed to improving the efficiency of the AMPT system's service provision in the case study network.