Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Abstract

This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), and suggested indicators (e.g., weekly frequencies, link occupancy rates, and CO2 footprint per journey) to directly characterize the efficiency and sustainability of this transport mode. The second stage is to assess the efficiency of URT with two original models, based on a thorough selection of input and output variables, which is one of the key contributions of EDA to this methodology. The first model compares URT against other urban transport modes, applicable to route personalization, and the second scores the efficiency of URT lines. The main outcome of this paper is the proposed methodology, which has been experimentally validated using open data from the Transport for London (TfL) URT network and additional sources.

Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis

“…In terms of applications, scholars have deployed big data for DEA studies to measure efficiency in a variety of settings, such as the environmental efficiency of regional industry (Chen & Jia, 2017), energy saving and carbon dioxide emissions (An et al, 2017;Zhu et al, 2020), forestry resources (Li et al, 2017), coal-fired power plants, iron and steel enterprises cleaner production technologies (Gong et al, 2017), natural resource allocation and utilisation (Zhu et al, 2017), transportation systems (Chu et al, 2018), eco-efficiency andeco-innovation (Kiani Mavi et al, 2019;Kiani Mavi & Kiani Mavi, 2021), electric power plants (Khezrimotlagh et al, 2019), facility layout design (Tayal et al, 2020), urban rail transit (Taboada & Han, 2020), supply chain management (Badiezageh et al, 2018), and infant failure of the vibration and noise of a washing machine (He et al, 2019), among others.…”

“…In this paper, Big Data-Machine Learning (ML) is used to reduce and derive the sustainable criteria for sustainability. Taboada and Han (2020) assessed the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics and DEA, under a big data context. Zhu et al (2020) proposed a new DEA model to analyse the energy and environmental efficiency of industrial sectors from China's 30 provincial-level regions in order to determine the potential and route for energy saving and carbon emission reduction.…”

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

“…The Data Space: Exploratory data analysis [130,131] is always recommended as a step to spend some time in. Irrespective of the problem at hand, some data can always be obtained from the process plant.…”

Advances in Public Transport Platform for the Development of Sustainability Cities

“…Security is also an important issue within public transpormation, in reference [12] the secure management of railway transportation systems has been analyzed. Finally, analytical models using Machine Learning and Deep Learning have been explored as part of this special issue [13,14]. Also, two case studies, carried out in the city of Barcelona, Spain [15] and Taipei, Taiwan [15], have been described.…”