Name
Abstract | ||
|---|---|---|
Predicting traffic flows on multiple inter-city roads play a critical role in traffic management. Temporal patterns of traffic flow can dynamically change over time as a result of traffic management measures such as the construction of new roads. Given this possibility, it is sensible to use only recent data, instead of all past data. In this study, by incorporating the latent factor model into a conventional approach, we construct a novel traffic prediction method for multiple inter-city roads based on the most recent training data. This formulation leads to a reduction in the number of model parameters, since it assumes that there is a set of patterns underlying the data (in our case, the periodic patterns shared across roads in traffic flow data), which inherently offers robustness against sparse observations. In addition, we adopt stochastic variational Bayes method to solve the optimization problem, which allows us to update model parameters online. By continually updating the model based on the most recent training data, we can instantly provide accurate predictions of inter-city traffic flow that have intermittently changing patterns. Using a real-world traffic flow dataset collected in the Greater Tokyo Area via GPS-equipped mobile phones, we evaluate the predictive performance of the proposed method, and confirm that it performs better than existing methods in the sparse domain. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MDM.2017.27 | 2017 18th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
online traffic flow prediction,convolved bilinear Poisson regression,multiple intercity roads,traffic management measures,new roads construction,stochastic variational Bayes method,optimization problem,intermittent changing patterns,real-world traffic flow dataset,Greater Tokyo Area,GPS-equipped mobile phones | Data mining,Traffic generation model,Data modeling,Traffic flow,Convolution,Computer science,Robustness (computer science),Artificial intelligence,Optimization problem,Machine learning,Bayes' theorem,Bilinear interpolation | Conference |
ISSN | ISBN | Citations |
1551-6245 | 978-1-5386-3933-7 | 1 |
PageRank | References | Authors |
0.37 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maya Okawa | 1 | 10 | 3.23 |
| Hideaki Kim | 2 | 49 | 6.39 |
| Toda, Hiroyuki | 3 | 22 | 4.89 |