Short-term Passenger Flow Prediction on Bus Stop Based on Hybrid Model

DOI

10.2991/ecae-17.2018.74How to use a DOI?

Keywords

short-term traffic flow prediction; BP neural network; time series model; hybrid model

Abstract

Short-term passenger flow prediction on bus stop is an important base and technical support for bus dispatch strategy. In this paper, a new hybrid prediction model including two single models of BP neural network and time series model was proposed according to the periodicity and randomness properties of short-term passenger flow. By using the IC card data from buses, a three-layer BP neural network model was established to reflect the characteristics of the stability of short-term passenger flow cycle. The time series model of cash ticket passenger flow data was established. Finally, the results of the two models were fitted to get the final prediction results. The results show that the proposed hybrid prediction model can effectively predict short-term passenger flow on bus stop.

Copyright

© 2018, 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  - Zhijian Wang
AU  - Chunlei Yang
AU  - Chao Zang
PY  - 2017/12
DA  - 2017/12
TI  - Short-term Passenger Flow Prediction on Bus Stop Based on Hybrid Model
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 343
EP  - 347
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.74
DO  - 10.2991/ecae-17.2018.74
ID  - Wang2017/12
ER  -