A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking
The impact of disturbances on a transportation network varies depending on the location and characteristics of the affected highway segments. Given limited resources, it is crucial to prioritize the protection and repair of highway segments based on their importance to maintaining overall network pe...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/1/28 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587433518039040 |
|---|---|
| author | Huizhe Lyu Yang Li Chenxu Liu Zhonghao Li Lin Xu Wei Wang Jun Chen |
| author_facet | Huizhe Lyu Yang Li Chenxu Liu Zhonghao Li Lin Xu Wei Wang Jun Chen |
| author_sort | Huizhe Lyu |
| collection | DOAJ |
| description | The impact of disturbances on a transportation network varies depending on the location and characteristics of the affected highway segments. Given limited resources, it is crucial to prioritize the protection and repair of highway segments based on their importance to maintaining overall network performance during disruptions. This paper proposes a novel method for ranking the importance of highway segments, leveraging a novel local–transit percolation and clustering-based method. Initially, the highway network is constructed by Graph theory, and the k-means clustering method is applied considering each segment’s transit and local traffic flows. Subsequently, a local–transit percolation model is constructed to generate an initial ranking of segments based on the size of the second-largest clusters during the percolation phase transition. A secondary ranking is performed by refining the results from the clustering phase. Results of a control experiment show that, compared to baselines, the proposed ranking approach demonstrates a significantly improved ability to sustain network demand and connectivity when high-ranked segments are moved. The model uncertainty analysis was conducted by adding noise to the gantry records, and the experiments demonstrated that the model exhibits robustness under noisy conditions. These findings highlight the effectiveness and superiority of the proposed method. |
| format | Article |
| id | doaj-art-7a820b2b9ea94205986b7306e93aa6d4 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-7a820b2b9ea94205986b7306e93aa6d42025-01-24T13:50:32ZengMDPI AGSystems2079-89542025-01-011312810.3390/systems13010028A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance RankingHuizhe Lyu0Yang Li1Chenxu Liu2Zhonghao Li3Lin Xu4Wei Wang5Jun Chen6School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaThe impact of disturbances on a transportation network varies depending on the location and characteristics of the affected highway segments. Given limited resources, it is crucial to prioritize the protection and repair of highway segments based on their importance to maintaining overall network performance during disruptions. This paper proposes a novel method for ranking the importance of highway segments, leveraging a novel local–transit percolation and clustering-based method. Initially, the highway network is constructed by Graph theory, and the k-means clustering method is applied considering each segment’s transit and local traffic flows. Subsequently, a local–transit percolation model is constructed to generate an initial ranking of segments based on the size of the second-largest clusters during the percolation phase transition. A secondary ranking is performed by refining the results from the clustering phase. Results of a control experiment show that, compared to baselines, the proposed ranking approach demonstrates a significantly improved ability to sustain network demand and connectivity when high-ranked segments are moved. The model uncertainty analysis was conducted by adding noise to the gantry records, and the experiments demonstrated that the model exhibits robustness under noisy conditions. These findings highlight the effectiveness and superiority of the proposed method.https://www.mdpi.com/2079-8954/13/1/28highwayresiliencepercolation theoryclusteringsegment importance ranking |
| spellingShingle | Huizhe Lyu Yang Li Chenxu Liu Zhonghao Li Lin Xu Wei Wang Jun Chen A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking Systems highway resilience percolation theory clustering segment importance ranking |
| title | A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking |
| title_full | A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking |
| title_fullStr | A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking |
| title_full_unstemmed | A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking |
| title_short | A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking |
| title_sort | local transit percolation and clustering based method for highway segment importance ranking |
| topic | highway resilience percolation theory clustering segment importance ranking |
| url | https://www.mdpi.com/2079-8954/13/1/28 |
| work_keys_str_mv | AT huizhelyu alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT yangli alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT chenxuliu alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT zhonghaoli alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT linxu alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT weiwang alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT junchen alocaltransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT huizhelyu localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT yangli localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT chenxuliu localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT zhonghaoli localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT linxu localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT weiwang localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking AT junchen localtransitpercolationandclusteringbasedmethodforhighwaysegmentimportanceranking |