Name
Abstract | ||
|---|---|---|
The analysis of public transportation data is receiving an increasing amount of attention from the research community in the past few years. This interest is fueled by the widespread installation and open access to a variety of sensor technologies for collecting data on the state of the transport system in many cities around the world. Different cities provide different data sources and in many cases the only common dataset is represented by GPS data of the vehicle fleet. Very often, the data contain erroneous or missing information that should be corrected before proceeding with their analysis. In this paper, we propose a methodology to de-noise scheduled bus stops and detect time schedule information using GPS AVL (Automatic Vehicle Location) data. The methodology performs different sequential steps: i) cleaning process and detection of trips; ii) bus stop extraction; ii) bus stop clustering; iv) feature extraction; v) classification model construction and application. Moreover, the impact on the whole process of different methods applied in different steps is empirically evaluated on datasets with different temporal extent. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ITSC.2013.6728570 | ITSC |
Keywords | Field | DocType |
global positioning system,data analysis,data mining,feature extraction,learning (artificial intelligence),object detection,road traffic,road vehicles,traffic engineering computing,gps avl,gps data,automatic vehicle location data,bus stop clustering,bus stop extraction,classification model construction,cleaning process,machine learning algorithms,public transportation data analysis,robust bus-stop identification,scheduled bus stops denoising,sensor technology,spatio-temporal analysis,time schedule information detection,transport system,trip detection,vehicle fleet,automatic vehicle location,schedules,methodology,information processing,learning artificial intelligence | Data mining,Object detection,Information processing,Simulation,Public transport,Feature extraction,Schedule,Global Positioning System,Engineering,Cluster analysis,Automatic vehicle location | Conference |
ISSN | Citations | PageRank |
2153-0009 | 3 | 0.51 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio Pinelli | 1 | 972 | 50.96 |
| Calabrese, F. | 2 | 3 | 0.51 |
| Bouillet, E.P. | 3 | 3 | 0.51 |