preprints.org > engineering > transportation science and technology > doi: 10.20944/preprints202406.1170.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 17 June 2024 / Approved: 17 June 2024 / Online: 18 June 2024 (08:25:56 CEST)
Version 2 : Received: 26 June 2024 / Approved: 26 June 2024 / Online: 27 June 2024 (08:07:26 CEST)

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a hybrid model, GraphResLSTM, which integrates Graph Convolutional Network, Residual Neural Network, and Long Short-Term Memory Network, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

OD prediction; deep learning; complex network; intelligent transportation system; spatiotemporal dependency; key road segment selection

Engineering, Transportation Science and Technology

* All users must log in before leaving a comment