Research on Bus Passenger Traffic Forecasting Model based on GPS and IC Card Data

DOI

10.2991/icmeit-19.2019.4How to use a DOI?

Keywords

GPS data; IC card data; RBF neural network; Bus passenger flow; Prediction.

Abstract

In order to accurately predict the bus passenger traffic volume and optimize bus dispatching, the paper combines the prediction model of RBF neural network to propose the deep integration of GPS and IC card data to realize the forecast of bus passenger traffic. Based on the GPS data and IC card data of Chongqing bus, the paper introduces the characteristics of GPS data and IC card data, and uses GPS data and IC data fusion analysis to obtain passenger OD distribution, passenger time segment distribution and passenger flow statistics. Then, using the RBF neural network model, the obtained three sets of data are predicted. Finally, the site of the 462 line in the main business district of Chongqing is used as the data source for verification. The results show that the RBF neural network prediction model based on GPS and IC card data sources can accurately predict the bus traffic and meet the requirements of optimizing bus scheduling.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jie Deng
AU  - Huawei Nie
AU  - Chaojun Chen
PY  - 2019/04
DA  - 2019/04
TI  - Research on Bus Passenger Traffic Forecasting Model based on GPS and IC Card Data
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 18
EP  - 27
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.4
DO  - 10.2991/icmeit-19.2019.4
ID  - Deng2019/04
ER  -