Departure moment optimization method based on demand response type bus

Abstract

The invention discloses a departure moment optimization method based on a demand response type bus, which carries out optimization on a departure moment of the demand response type bus for the demandresponse type bus of which service objects have differences in spatial and temporal distribution. The optimization method aims at a maximum weight combination value of a loading rate and a passenger average satisfaction level of the demand response type bus, considers constraint conditions of a demand time interval, a departure moment time interval, a demand type, passenger waiting time, vehicle capacity and the like, and establishes a demand response type bus departure moment optimization model to carry out optimization. According to the invention, the departure moment, the demand response and a driving path are integrated in one uniform optimization model and synchronous optimization is carried out, so that service timeliness and the operation level of the demand response type bus are improved.

Description

technical field [0001] The invention belongs to the technical field of bus departure time optimization, in particular to a method for optimizing the departure time of a demand-response bus. Background technique [0002] Prioritizing the development of public transport is an important method to increase the proportion of urban public transport modes and alleviate traffic congestion. The traditional bus travel mode has many problems such as long walking distance to the station and long waiting time for the bus. In this context, a high-quality public transport mode - "demand-responsive public transport" application was born. At present, the research on demand-responsive public transport mainly focuses on the optimization of driving routes. In order to accelerate the pace of successful popularization of demand-responsive public transport in China, the research on optimizing the bus departure time should be paid attention to. The existing departure time optimization research ob...

AI Technical Summary

Problems solved by technology

However, the division of time period length is related to the number of departures in this time period: if the distribution of passengers in adjacent time periods is too different, there will be a mismatch between supply and demand at the split point period at the end of the previous period and the beginning of the next period

It can be seen that in some cases, when using the method of dividing time periods to optimize the departure time, there will be certain blind spots

In addition, the service objects of demand-responsive public transport have strong fluctuations in the distribution of time and space, emphasizing the timeliness of service, so it is not appropriate to use the above method to study the optimization of its departure time

Images