A combination of DEA and AIMSUN to manage big data when evaluating the performance of bus lines

Abstract

The bus network, including several bus lines (BLs), is the most important public transportation system in many cities. A bus line (BL) may be inefficient compared to the other BLs. Therefore, solutions are needed to make it efficient. Data envelopment analysis (DEA) is a mathematical programming model that can measure the performance of BLs and provide solutions to improve the performance of inefficient BLs. However, DEA can consider only a few attributes when evaluating BLs, while this evaluation requires big data to be considered. Moreover, DEA solutions may negatively affect exogenous variables, in particular social and environmental variables. Although these variables are not directly related to measuring BLs’ efficiency, they may affect the implementation of DEA solutions. For example, reducing the number of buses on an inefficient line can increase its efficiency. However, it may indirectly increase the use of personal vehicles, resulting in more pollution and traffic. If the magnitude of this pollution or traffic is too high, decision-makers (DMs) may ignore improving the efficiency of that BL. Thus, the magnitude of possible negative consequences of DEA solutions, especially on critical social and environmental variables, should be estimated before their implementation. This study uses AIMSUN simulation software to pre-evaluate the negative effects of DEA solutions on critical exogenous social and environmental variables. We use a real example to illustrate that DEA solutions sometimes may not be implemented due to their harmful negative consequences.