Name
Abstract | ||
|---|---|---|
Traffic incidents and their impacts have been largely studied to improve road safety and to reduce incurred life and economic losses. However, the inaccuracy of incident data collected from transportation agencies, especially the start time, poses a great challenge to traffic incident research. We present INFIT, a system that infers the incident start time utilizing traffic data collected by loop sensors. The core of INFIT is IIG, our newly developed inference algorithm. The key idea is that IIG considers the traffic speed at multiple upstream locations, to mitigate the randomness in traffic data and to distinguish among multiple impact factors. INFIT includes an interactive interface with real-world incident datasets. We demonstrate INFIT with three exploratory use cases and show the usefulness of our inference algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2983323.2983339 | ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
Impact Propagation,Traffic Data,Traffic Incidents | Data mining,Use case,Inference,Computer science,Randomness | Conference |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingxuan Yue | 1 | 2 | 1.74 |
| Liyue Fan | 2 | 168 | 10.19 |
| Cyrus Shahabi | 3 | 5010 | 411.59 |