A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services

Chaofei Song; Runfeng Chang; Zibo Zhang; Anying Liu; Ruowen Li; Shenghan Zhou

doi:10.1109/DSIT55514.2022.9943914

A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services

Chaofei Song
, Runfeng Chang
, Zibo Zhang
, Anying Liu
, Ruowen Li
, Shenghan Zhou^*

^*Corresponding author for this work

School of Reliability and Systems Engineering

Beihang University
North China University of Technology
Beijing Institute of Economics and Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The purpose of this paper is to explore the performance of graph neural network (GNN) based short-term traffic supply-demand gap prediction for online car-hailing. In recent years, with the rapid development of smart cities, the technology as well as the scale of online car-hailing has grown rapidly, and it is necessary to analyze the travel characteristics of online car-hailing, and secondly, there are quite few studies on the supply-demand gap prediction of online car-hailing. Based on the dataset of online car-hailing operation, we analyzed the travel characteristics of online car-hailing from several dimensions, such as traffic congestion, weather, air quality, and temperature. In response to the current road traffic congestion and the reasonable allocation of online car-hailing, this paper proposes an online car-hailing supply-demand gap prediction model based on graph convolutional neural network and long and short-term memory neural network (GCN-LSTM), with mean absolute error (MAE) and root mean squared error (RMSE) as the evaluation index, and analyzes the performance of the model through simulation. The results show that the MAE and RMSE of the proposed method is only 12.3 and 26.4, respectively, which performs better than LightGBM, LSTM and other models on this dataset. Therefore, the constructed model for predicting the supply-demand gap of online car-hailing has a high-quality prediction performance.

Original language	English
Title of host publication	2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665498685
DOIs	https://doi.org/10.1109/DSIT55514.2022.9943914
State	Published - 2022
Event	5th International Conference on Data Science and Information Technology, DSIT 2022 - Shanghai, China Duration: 22 Jul 2022 → 24 Jul 2022

Publication series

Name	2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings

Conference

Conference	5th International Conference on Data Science and Information Technology, DSIT 2022
Country/Territory	China
City	Shanghai
Period	22/07/22 → 24/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

GCN-LSTM model
online car-hailing
short-term traffic supply-demand gap prediction

Access to Document

10.1109/DSIT55514.2022.9943914

Cite this

Song, C., Chang, R., Zhang, Z., Liu, A., Li, R., & Zhou, S. (2022). A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services. In 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSIT55514.2022.9943914

Song, Chaofei ; Chang, Runfeng ; Zhang, Zibo et al. / A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services. 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings).

@inproceedings{f3284a5687084c3a8e793864fa5e9ec7,

title = "A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services",

abstract = "The purpose of this paper is to explore the performance of graph neural network (GNN) based short-term traffic supply-demand gap prediction for online car-hailing. In recent years, with the rapid development of smart cities, the technology as well as the scale of online car-hailing has grown rapidly, and it is necessary to analyze the travel characteristics of online car-hailing, and secondly, there are quite few studies on the supply-demand gap prediction of online car-hailing. Based on the dataset of online car-hailing operation, we analyzed the travel characteristics of online car-hailing from several dimensions, such as traffic congestion, weather, air quality, and temperature. In response to the current road traffic congestion and the reasonable allocation of online car-hailing, this paper proposes an online car-hailing supply-demand gap prediction model based on graph convolutional neural network and long and short-term memory neural network (GCN-LSTM), with mean absolute error (MAE) and root mean squared error (RMSE) as the evaluation index, and analyzes the performance of the model through simulation. The results show that the MAE and RMSE of the proposed method is only 12.3 and 26.4, respectively, which performs better than LightGBM, LSTM and other models on this dataset. Therefore, the constructed model for predicting the supply-demand gap of online car-hailing has a high-quality prediction performance.",

keywords = "GCN-LSTM model, online car-hailing, short-term traffic supply-demand gap prediction",

author = "Chaofei Song and Runfeng Chang and Zibo Zhang and Anying Liu and Ruowen Li and Shenghan Zhou",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Data Science and Information Technology, DSIT 2022 ; Conference date: 22-07-2022 Through 24-07-2022",

year = "2022",

doi = "10.1109/DSIT55514.2022.9943914",

language = "英语",

series = "2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings",

address = "美国",

}

Song, C, Chang, R, Zhang, Z, Liu, A, Li, R & Zhou, S 2022, A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services. in 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 5th International Conference on Data Science and Information Technology, DSIT 2022, Shanghai, China, 22/07/22. https://doi.org/10.1109/DSIT55514.2022.9943914

A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services. / Song, Chaofei; Chang, Runfeng; Zhang, Zibo et al.
2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services

AU - Song, Chaofei

AU - Chang, Runfeng

AU - Zhang, Zibo

AU - Liu, Anying

AU - Li, Ruowen

AU - Zhou, Shenghan

PY - 2022

Y1 - 2022

N2 - The purpose of this paper is to explore the performance of graph neural network (GNN) based short-term traffic supply-demand gap prediction for online car-hailing. In recent years, with the rapid development of smart cities, the technology as well as the scale of online car-hailing has grown rapidly, and it is necessary to analyze the travel characteristics of online car-hailing, and secondly, there are quite few studies on the supply-demand gap prediction of online car-hailing. Based on the dataset of online car-hailing operation, we analyzed the travel characteristics of online car-hailing from several dimensions, such as traffic congestion, weather, air quality, and temperature. In response to the current road traffic congestion and the reasonable allocation of online car-hailing, this paper proposes an online car-hailing supply-demand gap prediction model based on graph convolutional neural network and long and short-term memory neural network (GCN-LSTM), with mean absolute error (MAE) and root mean squared error (RMSE) as the evaluation index, and analyzes the performance of the model through simulation. The results show that the MAE and RMSE of the proposed method is only 12.3 and 26.4, respectively, which performs better than LightGBM, LSTM and other models on this dataset. Therefore, the constructed model for predicting the supply-demand gap of online car-hailing has a high-quality prediction performance.

AB - The purpose of this paper is to explore the performance of graph neural network (GNN) based short-term traffic supply-demand gap prediction for online car-hailing. In recent years, with the rapid development of smart cities, the technology as well as the scale of online car-hailing has grown rapidly, and it is necessary to analyze the travel characteristics of online car-hailing, and secondly, there are quite few studies on the supply-demand gap prediction of online car-hailing. Based on the dataset of online car-hailing operation, we analyzed the travel characteristics of online car-hailing from several dimensions, such as traffic congestion, weather, air quality, and temperature. In response to the current road traffic congestion and the reasonable allocation of online car-hailing, this paper proposes an online car-hailing supply-demand gap prediction model based on graph convolutional neural network and long and short-term memory neural network (GCN-LSTM), with mean absolute error (MAE) and root mean squared error (RMSE) as the evaluation index, and analyzes the performance of the model through simulation. The results show that the MAE and RMSE of the proposed method is only 12.3 and 26.4, respectively, which performs better than LightGBM, LSTM and other models on this dataset. Therefore, the constructed model for predicting the supply-demand gap of online car-hailing has a high-quality prediction performance.

KW - GCN-LSTM model

KW - online car-hailing

KW - short-term traffic supply-demand gap prediction

UR - https://www.scopus.com/pages/publications/85143119944

U2 - 10.1109/DSIT55514.2022.9943914

DO - 10.1109/DSIT55514.2022.9943914

M3 - 会议稿件

AN - SCOPUS:85143119944

T3 - 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings

BT - 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Data Science and Information Technology, DSIT 2022

Y2 - 22 July 2022 through 24 July 2022

ER -

Song C, Chang R, Zhang Z, Liu A, Li R, Zhou S. A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services. In 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings). doi: 10.1109/DSIT55514.2022.9943914

A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this