Next Article in Journal
Formation and Characterization of Inkjet-Printed Nanosilver Lines on Plasma-Treated Glass Substrates
Previous Article in Journal
Towards New Mappings between Emotion Representation Models
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(2), 279; https://doi.org/10.3390/app8020279

Multiple-Factor Based Sparse Urban Travel Time Prediction

1,2,3
,
1
,
1,4,* , 5,* , 6
and
1
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
3
Key Lab of Aerospace Information Security and Trusted Computing of the Ministry of Education, Wuhan University, Wuhan 430079, China
4
Huawei Technologies Co., Ltd., Shenzhen 518129, China
5
Department of Geography, Kent State University, Kent, OH 44240, USA
6
School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Received: 4 December 2017 / Revised: 17 January 2018 / Accepted: 7 February 2018 / Published: 12 February 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [2825 KB, uploaded 12 February 2018]   |  

Abstract

The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time. View Full-Text
Keywords: big probe vehicles data; data sparsity; spatiotemporal relationships; multi-factor influences; artificial neural networks; link travel time prediction big probe vehicles data; data sparsity; spatiotemporal relationships; multi-factor influences; artificial neural networks; link travel time prediction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhu, X.; Fan, Y.; Zhang, F.; Ye, X.; Chen, C.; Yue, H. Multiple-Factor Based Sparse Urban Travel Time Prediction. Appl. Sci. 2018, 8, 279.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top