Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Economic and Environmental Aspects of Engine Selection in Cargo Transportation

A company’s productivity and success measure its effectiveness. This article presents the problem of economic efficiency and aspects related to environmental protection. The basic parameters analysed were the engine type and how it affects the environment. Over three months, the analysis was carried out and used the data envelopment analysis. As a result of the research carried out, the comparison of the amount of fuel used with the amount of transported goods should answer the following questions: What type of engine should be ordered for new trucks to show the best efficiency during operation? What is the efficiency of the currently used engines? How do they affect the environment? The method proposed in the article makes it possible to define the initial requirements for the definition of truck units, which is included in the conclusions of this paper.

Multi-Criteria Analysis in the Decision-Making Approach for the Linear Ordering of Urban Transport Based on TOPSIS Technique

The effects of urban transport are highly concerning. The rapid urbanization and motorization in smart cities have a huge impact on sustainability. The goal of the paper is to analyse the smart cities selected, in terms of the urban transport. This paper presents an overview of research works published between 1991 and 2020 concerning urban transport and MCDM (multi-criteria decision making). The author highlights the importance of decision-making criteria and their weight, as well as techniques. Seven criteria and forty-four objects were used as the input of the approach. The entropy weight method was used to compute the weight of each criterion. The TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) was applied to calculate the assessment and ranking of transport performance for each smart city. Portland was found to be the best location for transport enterprises and projects; Tbilisi was ranked last. The values of the relative closeness coefficient ranged from 0.03504 to 0.921402. Finally, some suggestions for future research are discussed.