This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach...This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach allows us to access the relative performance of transit system in absence of historical data and research to compare with. To explore the possibility of enhancing the performance, scenarios were created for relatively underperforming routes and long route problem by changing the most important input variable and output variables accordingly with regression model where it was relevant. Partial Least Squares (PLS) regression was used to determine the most influential input variables to the output variables. DEA was conducted to access the performance of all routes under these scenarios. Underperforming routes except the longest route under the first set of scenarios, emerge to be better performing efficiently without considerable negative deviation in effectiveness. The result of second set of scenarios for long route problem suggests that the longest route’s performance can be enhanced significantly upon proper route alignment. Scenarios development and evaluation can help lead transit companies to explore the strategies to facilitate operational performance enhancement.展开更多
真实世界多层网络具有多维度、高复杂性的特征,使得仅使用网络拓扑信息进行聚类的算法往往不能精准挖掘网络的公共社区结构。为了解决这一问题,本文提出一种基于非负矩阵分解的半监督模型(Semi-supervised Model with Non-negative Matr...真实世界多层网络具有多维度、高复杂性的特征,使得仅使用网络拓扑信息进行聚类的算法往往不能精准挖掘网络的公共社区结构。为了解决这一问题,本文提出一种基于非负矩阵分解的半监督模型(Semi-supervised Model with Non-negative Matrix Factorization,SeNMF)。首先,该模型设计基于PageRank算法的贪婪搜索方法获取网络的共识先验信息,用以增强每一层网络的拓扑结构,降低网络噪声;然后利用整体非负矩阵分解将所有网络层的低维表示在格拉斯曼流形上融合以获取更优的公共低维表示矩阵;最后利用K-means聚类得到网络的公共社区结构。实验表明,无论是网络层数的增加还是层间噪声的增强,SeNMF模型相较其他算法在多层网络聚类时均具有一定的优越性。展开更多
文摘This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach allows us to access the relative performance of transit system in absence of historical data and research to compare with. To explore the possibility of enhancing the performance, scenarios were created for relatively underperforming routes and long route problem by changing the most important input variable and output variables accordingly with regression model where it was relevant. Partial Least Squares (PLS) regression was used to determine the most influential input variables to the output variables. DEA was conducted to access the performance of all routes under these scenarios. Underperforming routes except the longest route under the first set of scenarios, emerge to be better performing efficiently without considerable negative deviation in effectiveness. The result of second set of scenarios for long route problem suggests that the longest route’s performance can be enhanced significantly upon proper route alignment. Scenarios development and evaluation can help lead transit companies to explore the strategies to facilitate operational performance enhancement.
文摘真实世界多层网络具有多维度、高复杂性的特征,使得仅使用网络拓扑信息进行聚类的算法往往不能精准挖掘网络的公共社区结构。为了解决这一问题,本文提出一种基于非负矩阵分解的半监督模型(Semi-supervised Model with Non-negative Matrix Factorization,SeNMF)。首先,该模型设计基于PageRank算法的贪婪搜索方法获取网络的共识先验信息,用以增强每一层网络的拓扑结构,降低网络噪声;然后利用整体非负矩阵分解将所有网络层的低维表示在格拉斯曼流形上融合以获取更优的公共低维表示矩阵;最后利用K-means聚类得到网络的公共社区结构。实验表明,无论是网络层数的增加还是层间噪声的增强,SeNMF模型相较其他算法在多层网络聚类时均具有一定的优越性。
