Name
Abstract | ||
|---|---|---|
Sensing turn-level or lane-level traffic conditions not only enables navigation systems to provide users with more detailed and finer-grained information, it can also improve the accuracy in the search for the fastest routes and in short-term predictions of traffic conditions. The widespread collection and application of taxis' GPS data enable us to sense urban traffic flow on a large scale. Since current GPS positional accuracy cannot reach the lane level, existing approaches using GPS trajectory data only analyze traffic conditions at the road level. Whereas some studies attempted to detect lane-level traffic conditions using lane-level data, the high cost of data collection considerably limits their practical application. To address this limitation, this article proposes an approach for detecting traffic congestion from taxis' GPS trajectories at the turn level. Based on analyzing features of GPS trajectories and identifying valid trajectory segments, the proposed approach detects congested trajectory segments of three different intensities. It then identifies congestion events in each turning direction through a clustering approach. Finally, congestion intensity, time of the day when congestion occurred and queue length in each turning direction at a road intersection in Wuhan, China are explored and analyzed. The results support the feasibility of this approach for detecting and analyzing traffic congestion at the turn level. Compared with other approaches that detect traffic congestion using GPS trajectory data, the proposed approach analyzes congestion at a finer-grained level (the turn level). Compared with other approaches that detect traffic congestion at the lane level, the proposed approach can sense traffic congestion over a larger area and at a lower cost. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compenvurbsys.2018.11.007 | Computers, Environment and Urban Systems |
Keywords | Field | DocType |
Traffic congestion,GPS trajectories,Intersection,Turn-level congestion,Big data | Data collection,Data mining,Traffic flow,Queue,Taxis,Global Positioning System,Cluster analysis,Geography,Trajectory,Traffic congestion | Journal |
Volume | ISSN | Citations |
74 | 0198-9715 | 2 |
PageRank | References | Authors |
0.36 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zihan Kan | 1 | 24 | 2.22 |
| Luliang Tang | 2 | 59 | 10.87 |
| Mei-Po Kwan | 3 | 336 | 45.13 |
| Chang Ren | 4 | 11 | 4.88 |
| Dong Liu | 5 | 113 | 34.08 |
| Qingquan Li | 6 | 1181 | 135.06 |