Mining Road Network Correlation for Traffic Estimation via Compressive Sensing
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 7390270 |
Pages (from-to) | 1880-1893 |
Journal / Publication | IEEE Transactions on Intelligent Transportation Systems |
Volume | 17 |
Issue number | 7 |
Publication status | Published - 1 Jul 2016 |
Link(s)
Abstract
This paper presents a transport traffic estimation method which leverages road network correlation and sparse traffic sampling via the compressive sensing technique. Through the investigation on a traffic data set of more than 4400 taxis from Shanghai city, China, we observe nontrivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct representation bases to sparsely represent the traffic conditions of all road segments in a road network. With the trait of sparse representation, we propose a traffic estimation approach that applies the compressive sensing technique to achieve a city-scale traffic estimation with only a small number of probe vehicles, largely reducing the system operating cost. To validate the traffic correlation model and estimation method, we do extensive trace-driven experiments with real-world traffic data. The results show that the model effectively reveals the hidden structure of traffic correlations. The proposed estimation method derives accurate traffic conditions with the average accuracy as 0.80, calculated as the ratio between the number of correct traffic state category estimations and the number of all estimation times, based on only 50 probe vehicles' intervention, which significantly outperforms the state-of-the-art methods in both cost and traffic estimation accuracy.
Research Area(s)
- compressive sensing, Correlation modeling, traffic estimation
Citation Format(s)
Mining Road Network Correlation for Traffic Estimation via Compressive Sensing. / Liu, Zhidan; Li, Zhenjiang; Li, Mo et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 7, 7390270, 01.07.2016, p. 1880-1893.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 7, 7390270, 01.07.2016, p. 1880-1893.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review