Online Traffic Speed Estimation for Urban Road Networks with Few Data: A Transfer Learning Approach

Yu，James J.Q.

10.1109/ITSC.2019.8917502

2019-10-01

2153-0009

978-1-5386-7025-5

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

4024-4029

27-30 Oct. 2019

83 Symonds St, Grafton, Auckland, New zealand

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

Online traffic speed data of urban road networks serve as the foundation of modern intelligent transportation systems. Much research has been conducted on developing methods, mostly model-based or machine learning ones, to estimate the data with GPS record for one, few adjacent roads, or the entire vehicular transportation network. While the machine learning methods generally yield satisfactory estimation accuracy, their accomplishments are established on a plethora of historical GPS records which may not be readily available for many urban transportation systems. In this paper, we investigate a transfer learning approach to provide speed data estimations with few data. We ground this work on a graph convolutional generative autoencoder that can generate the estimations for an entire transportation network in one go, and modify its internal computation graph to reduce the size of network topology-dependent model parameters. Subsequently, pre-trained models from road networks with massive historical data can be re-used in other networks with few data, which are only employed to adjust a small number of parameters. To assess the effectiveness of the proposed approach, comprehensive case studies are conducted, in which outstanding speed estimations can be obtained with significantly shorter training time.

Roads Estimation Convolution Data models Real-time systems Global Positioning System

第一 ; 通讯

英语

EI ; CPCI-S

Transportation

Transportation Science & Technology

WOS:000521238104016

20195207920391

Data communication systems ; E-learning ; Intelligent systems ; Intelligent vehicle highway systems ; Machine learning ; Online systems ; Roads and streets ; Speed ; Topology

Highway Systems:406.1 ; Roads and Streets:406.2 ; Highway Transportation:432 ; Railroad Transportation:433 ; Digital Computers and Systems:722.4 ; Artificial Intelligence:723.4 ; Computer Applications:723.5 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85076804270

Scopus

Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Yu，James J.Q.. Online Traffic Speed Estimation for Urban Road Networks with Few Data: A Transfer Learning Approach[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:4024-4029.