Name
Title | ||
|---|---|---|
The Evaluation Method of Road Network Connectivity Reliability Based on Key Segments Identification |
Abstract | ||
|---|---|---|
Traditional evaluation method of network connectivity reliability recognizes the road connectivity condition by using the two-value approach. This method makes the calculation increase with the index growth rate when the link numbers increase and is hard to apply in the high-density urban road network. Based on the relationship between the segment condition and the road network capability under the disaster, the paper presents that the key segments are the segments which have the highest broken-down probability and decisive influence on the road network capability. Further, this paper designed a new evaluation method of connectivity reliability based on the key segments identification, identified the key segments using Beijing data, and analyzed the influence rule after the typical key segments being broken down. The result shows that the new method provides an effective and practical tool to improve the anti-destruction capability of the road network. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSO.2009.166 | CSO (2) |
Keywords | Field | DocType |
antidestruction capability,decisive influence,beijing data,road safety,network connectivity reliability,road network connectivity reliability,road accidents,disasters,high-density urban road network,road network capability,evaluation method,disaster,new evaluation method,road network,broken-down probability,two-value approach,road traffic,key segment identification,key segments identification,index growth rate,traditional evaluation method,key segment,high-density urban road network connectivity reliability evaluation method,new method,road connectivity condition,probability,data analysis,filtration,indexation,vehicle dynamics,reliability,data mining,linking number,computer networks,object recognition,data models,testing | Data modeling,Network connectivity,Computer science,Road traffic,Artificial intelligence,Beijing,Machine learning,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3605-7 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-jie Liu | 1 | 50 | 4.49 |
| Huimin Wen | 2 | 6 | 3.34 |
| Yong Gao | 3 | 8 | 2.16 |