• Citer

Use of DEA and PROMETHEE II to assess the performance of older drivers

par Babaee, Seddigheh;Bagherikahvarin, Maryam ;Sarrazin, Renaud ;Shen, Yongjun;Hermans, Elke
Référence Transportation Research Procedia, 10, page (798-808)
Publication Publié, 2015

Article révisé par les pairs

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

In recent years, there has been an increasing concern regarding the safety and mobility of elderly drivers. This study aims to evaluate the overall performance and ranking of a sample of 55 drivers, aged 70 and older, based on data from an assessment battery and a fixed-based driving simulator, by using the concept of composite indicators and multi criteria approach. To do so, drivers completed tests of an assessment battery of psychological and physical aspects as well as knowledge of road signs. Moreover, they took part in a driving simulator test in which scenarios that are known to be difficult for older drivers were included. Composite indicators (CIs) are becoming increasingly recognized as a useful tool for performance evaluation, benchmarking and policy analysis by summarizing complex and multidimensional issues. One of the essential steps in the construction of composite indicators is aggregation and assignment of weights to each sub-indicator which directly affect the quality and reliability of the calculated CIs. In this regard, Data Envelopment Analysis (DEA) and Multi Criteria Decision Aiding (MCDA) have been acknowledged as two popular methods for weighting and aggregation and problem solving: ranking, sorting and choosing. In this case study, on the one hand, we apply a DEA model to calculate the optimal performance index score for each driver. On the other hand, we apply a MCDA method to enrich the analysis of this problem by considering preferential information from Decision Makers (DM). This also results in a ranking of drivers in terms of driving performance. The results of this study show that the best and the worst drivers identified by the two models are similar. These observations point out the interest of using PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluations) and DEA. The high correlation between these results confirms the robustness of our answers.