A hybrid returns to scale-DEA model for sustainable efficiency evaluation of urban transportation systems

Abstract

The urban transportation network has an undeniable role in addressing the economic, social and environmental issues caused by the traffic. Transportation managers seek to use the existing facilities and capacities in an optimal way to increase customer satisfaction. Therefore, it is necessary to develop an approach to evaluate the performance of the urban transportation system to provide service to citizens effectively. This study develops an approach based on the extended version of the data envelopment analysis (DEA) model to measure the nonradial efficiency and super-efficiency of metro-stations considering the sustainability concept. The developed non-radial DEA model considers the hybrid returns to scale the form of technology by combining constant and variable returns to scale assumptions to improve its applicability to identify efficient and inefficient stations. This DEA model also incorporates the non-discretionary inputs and different types of outputs (i.e. undesirable, negative and non-negative) to improve discrimination power and the ability to interpret the results. The findings help decision-makers identify super-efficient stations as a benchmark for future planning and finding the best location to construct metro-stations. Furthermore, this research enables managers to optimally use the resources to increase the transferred passengers, reduce customer dissatisfaction and optimise the annual profit.

Keywords:

• Sustainable development
• urban transportation
• efficiency evaluation
• data envelopment analysis
• hybrid returns to scale

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Notes

1 Charnes, Cooper, and Rhode

2 Banker, Charnes, and Cooper

Additional information

Funding

No funding was received for conducting this study.

Notes on contributors

Zohreh Moghaddas

Zohreh Moghaddas is an assistant professor at the department of mathematics and statistics, Islamic Azad University, Qazvin, Iran. She obtained her Ph.D. degree in applied mathematics from the science and research branch of Azad University, Tehran, Iran. Her research interests revolve around a diverse array of topics, including operation research, optimisation, mathematical modelling and data envelopment analysis. Through her scholarly pursuits, she seeks to contribute to the advancement of knowledge and practical applications in these fields.

Samuel Yousefi

Samuel Yousefi is a doctoral student at the School of Engineering, University of British Columbia, Kelowna, Canada, and his research focuses on developing analytical and decision-support frameworks to manage supply chain networks and logistic systems. He received his B.Sc. and M.Sc. degrees in industrial engineering from Urmia University of Technology, Urmia, Iran, in 2012 and 2014, respectively. He has published more than 35 articles in leading international journals in engineering and management (e.g. International Journal of Production Economics, Computers & Industrial Engineering and IEEE Transactions on Engineering Management). In addition to organising several special issues in the field of data and decision analytics, he has collaborated with more than 80 international journals and conferences as a reviewer. His main research interests include supply chain and operations management, decision science, risk analysis and business intelligence.

Mahsa Mohammadi

Mahsa Mohammadi is a doctoral student at The University of British Columbia, Kelowna, Canada, and her research focuses on urban logistics and delivery services in the e-commerce industry. In her master’s thesis, she demonstrated her expertise in employing robust and multi-objective optimisation techniques to optimise sustainable supply chain networks. Driven by her passion for operations research, logistics and transportation planning, disaster management, machine learning and reinforcement learning, she is constantly seeking new avenues for advancement within these disciplines. Her contributions to these fields have led to publications in peer-reviewed journals, including Computer & Operations Research, International Journal of Disaster Risk Reduction, and Computers, Environment and Urban Systems.

Babak Mohamadpour Tosarkani

Babak Mohamadpour Tosarkani obtained his Ph.D. and M.A.Sc. degrees in mechanical and industrial engineering from Toronto Metropolitan University (formerly Ryerson University), Toronto, ON, Canada, in 2020 and 2017, respectively. In 2012, he earned an M.B.A. in Finance from Multimedia University, Cyberjaya, Malaysia. Currently, Dr. Tosarkani is an assistant professor in the Faculty of Applied Science at The University of British Columbia (UBC), Kelowna, BC, Canada. Before joining the UBC faculty, he was an assistant professor of operations research and management science at the Shannon School of Business at Cape Breton University, Sydney, NS, Canada. Dr. Tosarkani has a background in engineering management. His research interests include operations and supply chain analytics, disaster management, circular economy and strategic sustainable development.