Name
Abstract | ||
|---|---|---|
Massive traffic surveillance data extracted from vehicle detectors such as cameras provide essential information for revealing urban traffic pattern. However, most existing tools only allow users to analyze the data in specific time periods and regions with particular requirements. In this paper, we work closely with traffic domain experts and investigate a novel way of reframing visual traffic analysis tasks into the combinations of various atom categorical/numerical features and visual presentation. The categorical features contain primitive attributes such as vehicle type, O/D status and driving direction, and the numerical features contains information such as vehicle frequency and speed. The combination of above features includes four basic operations, namely and, or, xor and not to support diversified user requirements. Basic and advanced visualization methods such as trajectory view and flow distribution view are provided to demonstrate the combination results. Through interactive assembling of various atom operations, analysts could derive different query conditions to meet existed and potential upcoming analysis requirements such as locating suspicious vehicles (e.g., fake plate vehicles). Furthermore, AtoMixer, a visual analytic system is developed to support spatio-temporal investigative tasks for traffic surveillance data. We evaluate the effectiveness and scalability of our approach with real world traffic surveillance data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cola.2019.03.001 | Journal of Computer Languages |
Keywords | DocType | Volume |
Human-centered computing,Visual analytics,Visual query | Journal | 53 |
ISSN | Citations | PageRank |
2590-1184 | 1 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo-Dao Sun | 1 | 171 | 11.24 |
| Yin Zhao | 2 | 445 | 16.08 |
| Dizhou Cao | 3 | 1 | 0.34 |
| Jianyuan Li | 4 | 34 | 2.48 |
| Ronghua Liang | 5 | 376 | 42.60 |
| Yipeng Liu | 6 | 1 | 0.34 |