A self-driving bus scheduling optimization method

Abstract

The invention provides an automatic driving bus scheduling optimization method. The method includes: based on the original bus IC card records and GPS data, obtain valid bus IC card boarding and disembarkation transaction data and GPS trajectory data, and extract actual indicators of bus operation; integrate automatic driving features according to the actual bus operation rules, and combine the vehicle arrival time Passengers get on and off the bus, establish an automatic driving bus operation scheduling model that considers the dynamic needs of passengers; extract passenger OD and other information from the actual bus data as the input of the model, and optimize the bus operation schedule, overall passenger waiting time, and bus capacity Indexes such as traffic volume and number of passengers waiting at the station are compared with the actual state to reflect the optimization effect of autonomous driving bus dispatching. By optimizing all the vehicles currently operating on the entire bus line, real-time regulation of the arrival time of the bus at each station and the number of passengers at the station can improve the utilization efficiency of vehicles and shorten the waiting time of passengers.

Description

technical field [0001] The present invention relates to the technical field of traffic information, and more specifically relates to an automatic driving bus scheduling optimization method. Background technique [0002] Giving priority to the development of public transportation is one of the important means to solve urban traffic problems, and it will also be a long-term strategy for the development of urban transportation in my country. Restricted by road traffic conditions, the bus travel time is long and the passenger capacity is unevenly distributed, resulting in the consumption of some transport capacity resources. During the actual driving process of the bus, the traffic flow is large during the peak hours, the vehicle operation is blocked, the departure interval is unstable, and it is easy to appear the phenomenon of cross-training; at the same time, due to the insufficient response to the real-time demand of passengers, the waiting time of passengers is too long, an...

AI Technical Summary

Problems solved by technology

However, bus scheduling optimization is usually based on theoretical assumptions, and there are few studies on optimization based on measured data. The research objects are often existing traditional buses, and there is not enough research on the optimization of autonomous driving bus operation scheduling.

Images