Method Based on Floating Car Data and Gradient-Boosted Decision Tree Classification for the Detection of Auxiliary Through Lanes at Intersections
| Autor: | Wang Yuqian, Li Xiaolong, Wu Jing, Wu Yuzhen, Tan Yongbin, Cheng Penggen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Geography Planning and Development lcsh:G1-922 ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Rapid detection 0502 economics and business 0202 electrical engineering electronic engineering information engineering Earth and Planetary Sciences (miscellaneous) auxiliary through lane Computers in Earth Sciences gradient-boosted decision tree floating car data 050210 logistics & transportation intersection lane business.industry 020208 electrical & electronic engineering 05 social sciences Floating car data Pattern recognition lane number Classification methods Gradient boosting Artificial intelligence business Classifier (UML) Decision tree model lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 7, Iss 8, p 317 (2018) ISPRS International Journal of Geo-Information Volume 7 Issue 8 |
| ISSN: | 2220-9964 |
| Popis: | The rapid detection of information on continuously changing intersection auxiliary through lane is a major task of lane-level navigation data updates. However, existing lane number detection methods possess long update cycles and high computational costs. Therefore, this study proposes a novel method based on floating car data (FCD) for the detection of auxiliary through lane changes at road intersections. First, roads near intersections are divided into three sections and the spatial distribution characteristics of the FCD of each section are analyzed. Second, the FCD is preprocessed to obtain a standardized FCD dataset by removing redundant data through an improved amplitude-limiting average filtering method. Third, a basic classifier for the number of lanes is constructed. Fourth, the final number of lanes of the road section is determined by combining the basic classifier and the gradient-boosted decision tree model. Finally, the presence of an auxiliary through lane at the intersection is determined in accordance with the change in the number of intersection lanes. The method was tested using data for a road in Wuchang District, Wuhan City. Experimental results show that this method can rapidly obtain auxiliary through lane information from the FCD and is superior to other classification methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|