Work

Prediction of bus arrival time based on improved long short-term memory network model

Year: 2024

Type: conference-paper

Abstract: Enhancing the precision of predicting bus arrival times can improve passenger travel efficiency and bus service quality, and save bus operating costs. Long Short Term Memory Network (LSTM) models are diffusely applied to time series analysis of bus travel time prediction tasks, but they may have shortcomings in spatial feature research. To address this issue, a prediction model mixing Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM) comed up, which uses CNN to draw spatial features from the data, then uses LSTM networks to learn time dependencies in time series data, and introduces Attention mechanism to enhance the performance of LSTM models. Finally, the effectiveness of the prediction model is verified using bus GPS data. The test results express that the model is provided with low error.

Source: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL)

DOI: 10.1117/12.3050640

Authors: Jiale Liu, Duo Mei

Institution: Bohai University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: bronze

OpenAlex ID: W4402921809