A novel fuzzy inference method for urban incomplete road weight assignment
文献类型：期刊论文
| 作者 | Wang, Longhao2,3,4; Rui, Xiaoping1 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2023-10-05 |
| 页码 | 15 |
| 关键词 | Weight assignment path planning algorithm fuzzy inference road network |
| ISSN号 | 1009-5020 |
| DOI | 10.1080/10095020.2023.2261768 |
| 通讯作者 | Rui, Xiaoping(ruixp@hhu.edu.cn) |
| 英文摘要 | One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road's weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing - conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information. |
| WOS关键词 | ORIGIN-DESTINATION MATRIX ; NETWORK ; INFORMATION ; PREDICTION |
| 资助项目 | National Key Research and Development Program of China[2019YFC1804304] ; National Natural Science Foundation of China[41771478] ; Fundamental Research Funds for the Central Universities[2019B02514] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001092303200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199173]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Rui, Xiaoping |
| 作者单位 | 1.Hohai Univ, Sch Earth Sci & Engn, Nanjing, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 4.Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Longhao,Rui, Xiaoping. A novel fuzzy inference method for urban incomplete road weight assignment[J]. GEO-SPATIAL INFORMATION SCIENCE,2023:15. |
| APA | Wang, Longhao,&Rui, Xiaoping.(2023).A novel fuzzy inference method for urban incomplete road weight assignment.GEO-SPATIAL INFORMATION SCIENCE,15. |
| MLA | Wang, Longhao,et al."A novel fuzzy inference method for urban incomplete road weight assignment".GEO-SPATIAL INFORMATION SCIENCE (2023):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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