- Tytuł:
- Road network partitioning method based on Canopy-Kmeans clustering algorithm
- Autorzy:
-
Lin, Xiaohui
Xu, Jianmin - Powiązania:
- https://bibliotekanauki.pl/articles/949836.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
traffic engineering
road network
road network partition
Canopy-Kmeans algorithm
macroscopic fundamental diagram
inżynieria ruchu
sieć drogowa
sterowanie siecią drogową
algorytm Canopy-Kmeans - Opis:
- With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the characteristics of traffic flow. However, road network partition can be regarded as a clustering process of the division of road segments with similar attributes, and thus, the clustering algorithm can be used to divide the sub-areas of road network, but when Kmeans clustering algorithm is used in road network partitioning, it is easy to fall into the local optimal solution. Therefore, we proposed a road network partitioning method based on the Canopy-Kmeans clustering algorithm based on the real-time data collected from the central longitude and latitude of a road segment, average speed of a road segment, and average density of a road segment. Moreover, a vehicle network simulation platform based on Vissim simulation software is constructed by taking the real-time collected data of central longitude and latitude, average speed and average density of road segments as sample data. Kmeans and Canopy-Kmeans algorithms are used to partition the platform road network. Finally, the quantitative evaluation method of road network partition based on macroscopic fundamental diagram is used to evaluate the results of road network partition, so as to determine the optimal road network partition algorithm. Results show that these two algorithms have divided the road network into four sub-areas, but the sections contained in each sub-area are slightly different. Determining the optimal algorithm on the surface is impossible. However, Canopy-Kmeans clustering algorithm is superior to Kmeans clustering algorithm based on the quantitative evaluation index (e.g. the sum of squares for error and the R-Square) of the results of the subareas. Canopy-Kmeans clustering algorithm can effectively partition the road network, thereby laying a foundation for the subsequent road network boundary control.
- Źródło:
-
Archives of Transport; 2020, 54, 2; 95-106
0866-9546
2300-8830 - Pojawia się w:
- Archives of Transport
- Dostawca treści:
- Biblioteka Nauki
Artykuł