Name
Abstract | ||
|---|---|---|
Together with the availability of new mobility data, the development of new intelligent transport systems (ITS) have increased, in order to provide new key performance indicators toward the improvement of the management of traffic awareness in cities. ITS rely on accurate transit infrastructure data that often contain erroneous information (e.g., inconsistencies or is out of date). In this paper, we propose a new methodology that makes use of GPS traces to automatically detect or correct bus stop locations, reconstruct bus route shapes, and estimate time schedules. The methodology performs different steps: 1) data cleaning and detection of trips; 2) bus stop extraction through data mining techniques; 3) route shape reconstruction; and 4) time schedule estimation. A case study using real GPS data from the City of Dublin, Ireland, is performed. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TITS.2014.2344297 | Intelligent Transportation Systems, IEEE Transactions |
Keywords | Field | DocType |
global positioning system,data mining,intelligent transportation systems,public transport,road traffic,dublin,gps traces,its,ireland,bus infrastructure data denoising,bus infrastructure data generation,bus route shape reconstruction,bus stop extraction,bus stop location detection,bus trip detection,data cleaning,data mining techniques,intelligent transport systems,mobility data,time schedule estimation,traffic awareness management,transit infrastructure data,geographic information systems,geospatial analysis,learning artificial intelligence,feature extraction,algorithms,shape,clustering algorithms,trajectory,methodology,schedules | Performance indicator,Simulation,Transport engineering,Real-time computing,Feature extraction,Schedule,Global Positioning System,Intelligent transportation system,Engineering,TRIPS architecture,Cluster analysis,Trajectory | Journal |
Volume | Issue | ISSN |
16 | 2 | 1524-9050 |
Citations | PageRank | References |
1 | 0.42 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio Pinelli | 1 | 972 | 50.96 |
| Francesco Calabrese | 2 | 242 | 15.93 |
| Eric Bouillet | 3 | 1 | 0.42 |