Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation te...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	PloS one Ročník 10; číslo 3; s. e0119044
Hlavní autoři:	Ma, Xiaolei, Yu, Haiyang, Wang, Yunpeng, Wang, Yinhai
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 17.03.2015 Public Library of Science (PLoS)
Témata:	Architectural engineering Artificial neural networks Calibration China Collaboration Computer simulation Data mining Deep learning Engineering Evolution Geographic Information Systems Global positioning systems GPS Graphics processing units Intelligent transportation systems Internet of Things Laboratories Learning Learning theory Mathematical models Mathematical problems Mitigation Models, Theoretical Natural language processing Network analysis Neural networks Neural Networks (Computer) Predictions Process parameters Recurrent neural networks Satellite navigation systems Studies Theory Traffic congestion Traffic flow Traffic models Transportation Transportation - methods Transportation industry Transportation networks Urban areas China Beijing China United States > US
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 Conceived and designed the experiments: XM Yinhai Wang. Performed the experiments: XM HY. Analyzed the data: XM HY. Contributed reagents/materials/analysis tools: Yunpeng Wang. Wrote the paper: XM. Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0119044