Scheduling optimization method for automatic driving buses

Abstract

The invention provides a scheduling optimization method for automatic driving buses. The method comprises: acquiring getting-on and getting-off transaction data and GPS trajectory data of an effectivebus IC card based on an original bus IC record and GPS data, and extracting an actual index of bus operation; according to an actual bus operation rule, establishing an automatic driving bus operation scheduling model considering the dynamic needs of passengers by combining the automatic driving features and getting-on and getting-off situations of passengers at arrival of the bus; and extractinginformation like passenger ODs from the actual bus data to serve as the model input, comparing optimized indexes including the bus operation time table, total passenger waiting time, bus carrying capacity, waiting passenger number at bus stops, and reflecting the optimization effect of the automatic driving bus scheduling. According to the invention, all currently operated vehicles on the whole bus route are optimized and the arrival times of buses at all bus stops, and the passengers at all bus stops are regulated in real time, thereby the utilization efficiency of the bus is improved and the passenger waiting time is shortened.

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.

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