Name
Abstract | ||
|---|---|---|
Transit signal priority (TSP), which is one of the most important issues in intelligent transportation systems, aims to provide priority signals with an advanced inspection system to public transport vehicles. In this paper, by introducing the advanced object detection technique into intelligent transport systems, we propose an automatic bus detection algorithm and apply it to the transit signal priority (TSP) system. The contributions of this paper fall into two folds: (1) we propose a bus detection algorithm. In this algorithm, an illumination-independent color feature is used for bus detection, which is useful in practical illumination environments. In addition, the widely-used sparse representation technique is extended to cost-sensitive kernel sparse representation, that can effectively combine different features for bus detection. (2) A transit signal priority control scheme is proposed based on the bus detection results. This control scheme optimizes the traffic lights signal according to whether a bus is coming or not. Experimental and simulation results show that the proposed intelligent TSP system based on bus detection is effective. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2013.02.006 | Neurocomputing |
Keywords | Field | DocType |
intelligent transport system,advanced inspection system,transit signal priority control,transit signal priority,bus detection result,automatic bus detection algorithm,bus detection algorithm,advanced object detection technique,priority signal,bus detection,sparse representation | Kernel (linear algebra),Object detection,Computer science,Sparse approximation,Real-time computing,Public transport,Intelligent transportation system | Journal |
Volume | ISSN | Citations |
118, | 0925-2312 | 3 |
PageRank | References | Authors |
0.53 | 12 | 3 |