Work

Grading of Traffic Interruptions in Highways to Tibet Based on the Entropy Weight-TOPSIS Method and Fuzzy C-Means Clustering Algorithm

Year: 2024

Type: article

Abstract: The interruption of transportation on the way to Tibet has brought great losses to the Tibetan region. The work proposed a model that integrated the entropy weight-TOPSIS method with the fuzzy C-means clustering algorithm to discuss the causes and characteristics of traffic interruptions on the four main highways to Tibet. This approach aimed to quantify and grade traffic interruption states. First, the entropy weight-TOPSIS method was used to mitigate dimensions among various indices and quantitatively evaluate the status values of traffic interruptions. Then, the fuzzy C-means clustering algorithm was employed to grade these values. The proposed model graded traffic interruption states into four levels by evaluating the duration, mileage, and severity of traffic interruptions. Moreover, the four-level classification scheme can reflect the severity of traffic blocking events more precisely while maintaining a lower PE (Partition Entropy) value. In the four-level classification, the Sichuan–Tibet Highway and Xinjiang–Tibet Highway experienced more level-3 and level-4 serious interruptions, while most high-level interruptions on the Qinghai–Tibet Highway were classified as level-2 ordinary interruptions. The Yunnan–Tibet Highway, with limited data and primarily level-1 classification, was not analyzed in detail. These findings provide a reference for highway management departments to formulate targeted maintenance and emergency measures, especially the Sichuan–Tibet highway, which needs more attention and resource investment to improve its disaster resistance and reduce the impact of traffic interruptions.

Source: Applied Sciences

DOI: 10.3390/app14199094

Authors: Jian Tian, Zhiqiang Li, Suyan Zhuang, Jianfeng Xi, Min Li

Institutions: China Academy of Transportation Sciences, Jilin University

Language: English

Topic: Multi-Criteria Decision Making

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,728