Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

Freeway Travel Time Prediction Research Based on A Deep Learning Approach

Authors
Junfeng Zhang, Hongxi Chen, Hong Zhou, Zhihai Wu
Corresponding Author
Junfeng Zhang
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.97How to use a DOI?
Keywords
Deep learning, Toll data, Travel time prediction, Automatic encoder
Abstract
Accurate prediction of freeway travel time is quite significant for traffic management and travelers' trip decisions. A model of stacked automatic encoders for freeway travel time prediction is proposed. First, obtain the hourly average travel time by processing freeway toll data. Considering data quantity and travel time scheduling, a travel time prediction model based on stacked automatic encoders is established then. The automatic encoder is trained with unsupervised learning step by step by using historical travel time data, adjust the parameters of prediction layer with gradient descent method. Ultimately the one-hour travel time is predicted using the past three-hours' average travel time. Compared to the traditional BP neural network prediction model, the Root Mean Square Error of this method drops by 13.6% ,verifying the validity of the model.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.97How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Junfeng Zhang
AU  - Hongxi Chen
AU  - Hong Zhou
AU  - Zhihai Wu
PY  - 2016/09
DA  - 2016/09
TI  - Freeway Travel Time Prediction Research Based on A Deep Learning Approach
PB  - Atlantis Press
SP  - 487
EP  - 493
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.97
DO  - https://doi.org/10.2991/amitp-16.2016.97
ID  - Zhang2016/09
ER  -