Technical Efficiency of European Metro Systems: The Effects of Operational Management and Socioeconomic Environment

Abstract

This study focuses on the relationship between the operational performance of metro systems and their socioeconomic contexts. We use a two-stage methodology applied to a sample of 17 European metro systems. First, we apply a stochastic frontier approach to establish the optimal production function and to evaluate the efficiency and effectiveness levels of each firm through offer and demand-characterizing indicators, respectively. Only internal production factors are included in the first stage of this analysis. In a second stage, we use a similar modeling approach, but considering an additional set of variables characterizing the socioeconomic environment of the urban areas in which metro systems operate. This method allows observing the effects on operational performance measurements due to the inclusion of external factors, and consequently, drawing some conclusions on the technical efficiency of metro systems and their operations in beneficial or adverse surrounding environments. Different scores resulting from both perspectives evidence the contributions of the socioeconomic factors to improve the reliability of performance measurements and to reduce false inefficiencies. The results show that 12 of the analyzed systems are being affected by an unfavourable socioeconomic environment and/or their network suffers from some adequacy problems with regard to demand. The remaining 5 systems should improve their management strategies, since their results are being supported by a favourable surrounding environment.

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

“…These references in rail transport systems generally compare different public transport agencies at regional or national levels. In [30] 17 European URT networks have been evaluated using a two-stage methodology focusing on the relationship between the operational performance and their socioeconomic contexts. In [31] urban public transport systems of 652 Chinese cities have been analyzed, highlighting the high efficiency of URT.…”

“…The first stage of the proposed methodology uses EDA for deriving state-of-the-art quantitative indicators [30]: network length, number of stations, the number of trains, the number of frequencies, the number of employees, the number of operated kilometers, and the number of passengers. This data is usually publicly available at transport operator level, useful for comparing operator's efficiency, but it is more difficult to find at line level, limiting the analysis of the efficiency of rail network elements.…”

“…• In [30] the network length, the number of stations and cars are the inputs (CAPEX), whereas the number of employees is considered the only input (OPEX), due to the scarcity of materials and energy consumption information, two relevant inputs (OPEX). Additional variables considered to be inputs are ratios between these variables (e.g., the network length divided by the number of cars), historical data, as well as socioeconomic variables, such as area, population density of the core city, average household size, unemployment rate, GDP (Gross Domestic Product) per capita, and diesel pump price.…”

“…Additional variables considered to be inputs are ratios between these variables (e.g., the network length divided by the number of cars), historical data, as well as socioeconomic variables, such as area, population density of the core city, average household size, unemployment rate, GDP (Gross Domestic Product) per capita, and diesel pump price. In [30] two models are computed: (i) efficiency, using the number of cars-kilometers produced as output, and (ii) effectiveness, considering the number of transported passengers. The large number of variables and the limited number of analyzed URT networks (17) ends up with most of the evaluated systems considered highly efficient (here most URT networks excel in some, disjoint parameters, increasing its efficiency).…”

“…Comparing, for the first time, to the best of our knowledge, a single URT system at the line level, and also against other transport models from the traveler perspective, focusing on the efficiency and sustainability, and skipping the wide range of sociodemographic variables that require two-step modeling, as for [30,33].…”