Name
Title | ||
|---|---|---|
Modeling heterogeneous routing decisions in trajectories for driving experience learning |
Abstract | ||
|---|---|---|
Road latent cost, which quantifies how desirable each road is for traveling, is important information to enable many smartcity applications such as route recommendation. Arguably, vehicle trajectories are a good source to learn these costs as drivers intelligently incorporate them into their routing decisions. However, major past approaches misinterpret drivers' behaviors and suffer from trajectory sparsity problem, mainly because they adopt an edge-centric perspective which fails to exploit the sequential information in the entire trajectories. To address these shortcomings, we model drivers' routing decision process which targets at global path optimality, and present a framework to reliably discover those costs by exploiting entire trajectories while isolating the influence of heterogeneous destinations. Extensions are also made to address several issues in practice. Extensive experiments on real world data show that the road costs learned in this way significantly outperform past approaches in several urban computing tasks and require less data for learning. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2632048.2632089 | UbiComp |
Keywords | Field | DocType |
user interfaces,trajectory,general,decision modeling,road latent cost | Routing decision,Computer science,Exploit,Urban computing,Decision model,Artificial intelligence,Machine learning,Trajectory,Destinations | Conference |
Citations | PageRank | References |
10 | 0.59 | 22 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangchuan Zheng | 1 | 112 | 6.20 |
| Lionel M. Ni | 2 | 9462 | 802.67 |