Name
Title | ||
|---|---|---|
An identification model of urban critical links with macroscopic fundamental diagram theory. |
Abstract | ||
|---|---|---|
How to identify the critical links of the urban road network for actual traffic management and intelligent transportation control is an urgent problem, especially in the congestion environment. Most previous methods focus on traffic static characteristics for traffic planning and design. However, actual traffic management and intelligent control need to identify relevant sections by dynamic traffic information for solving the problems of variable transportation system. Therefore, a city-wide traffic model that consists of three relational algorithms, is proposed to identify significant links of the road network by using macroscopic fundamental diagram (MFD) as traffic dynamic characteristics. Firstly, weightedtraffic flow and density extraction algorithm is provided with simulation modeling and regression analysis methods, based on MFD theory. Secondly, critical links identification algorithm is designed on the first algorithm, under specified principles. Finally, threshold algorithm is developed by cluster analysis. In addition, the algorithms are analyzed and applied in the simulation experiment of the road network of the central district in Hefei city, China. The results show that the model has good maneuverability and improves the shortcomings of the threshold judged by human. It provides an approach to identify critical links for actual traffic management and intelligent control, and also gives a new method for evaluating the planning and design effect of the urban road network. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11704-016-6080-7 | Frontiers of Computer Science |
Keywords | Field | DocType |
urban road network, critical links, intelligent transportation system, macroscopic fundamental diagram | Intelligent control,Data mining,Regression analysis,Computer science,Extraction algorithm,Design effect,Simulation modeling,Diagram,Artificial intelligence,Intelligent transportation system,Traffic planning,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 1 | 2095-2236 |
Citations | PageRank | References |
3 | 0.43 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanli Dong | 1 | 3 | 0.43 |
| Yunpeng Wang | 2 | 194 | 25.34 |
| Haiyang Yu | 3 | 6 | 2.50 |