Optimizing the bus operation plan Based on Deep Learning

Abstract

The bus operation plan is closely to urban resident daily life. A good bus operation plan can improve the travel efficiency and comfort at the same time. To reduce the cost of traveling with bus companies and passengers, here a bus operation model considering shuttle buses based on predicted data (PSBO) is proposed. This model contains three parts, namely the passenger flow prediction, bus travel time prediction, and bus operation optimization. In the passenger flow prediction part, we propose a novel passenger flow prediction model based on long short-term memory (LSTM) and the passenger travel frequency, called Two-stage LSTM passenger flow prediction model (TLPLP). The accuracy of the TLPLP model is verified by comparison with multiple models. This paper also chooses the LSTM model to predict the bus travel time between different stations. The predicted passenger number and bus travel time are two inputs in the bus operation optimization part of PSBO model. This model aims at minimizing the passenger travel time and the operation cost through an insert shuttle buses method. We design a quick algorithm to solve this model and verifies the effect of this model compared with the bus dispatching plan in use.