Use of DEA and PROMETHEE II to Assess the Performance of Older Drivers
DOI: 10.1016/j.trpro.2015.09.033
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Summary
This study addresses the growing concern regarding the safety and mobility of elderly drivers by developing a robust method to assess their relative driving performance. As the population ages, maintaining driving independence is crucial, yet age-related declines in sensory, motor, and cognitive abilities pose significant risks. The research aims to create a reliable screening procedure that evaluates older drivers using composite indicators derived from multidimensional data, thereby helping individuals and policymakers identify weaknesses and plan for improvement. The researchers evaluated a sample of 55 drivers aged 70 and older (mean age 76.49) who held valid licenses and were active drivers. Data collection involved two phases: a neuropsychological assessment battery and a fixed-based driving simulator test. The assessment battery measured psychological ability (Mini Mental State Examination, Digit Span Forward, Useful Field of View), physical ability (Snellen Chart, Get-Up-and-Go test, Four-test Balance Scale), and knowledge of road signs. The simulator test, designed with scenarios known to be difficult for older drivers (e.g., intersections, gap acceptance), measured driving performance through mean complete stops, average following distances, and mean driving speeds across various road segments. To analyze this data, the authors constructed a composite indicator using two distinct methods: Data Envelopment Analysis (DEA) and PROMETHEE II, a Multi-Criteria Decision Aiding (MCDA) method. The DEA model utilized a multiple-layer approach to aggregate 16 hierarchical indicators into a single performance index, employing a cross-index method for ranking. PROMETHEE II was applied to generate a complete ranking based on pairwise comparisons, incorporating decision-maker preferences and specific preference functions for certain criteria. The results demonstrated a high degree of consistency between the two analytical methods. The Spearman’s correlation coefficient between the DEA and PROMETHEE II rankings was 0.937, and the Kendall’s correlation coefficient was 0.789, indicating strong agreement. Both models identified similar drivers as the best and worst performers. For instance, Driver ID 50 ranked first in both methodologies, while Driver ID 52 ranked last in both. Although minor discrepancies existed in the middle ranks (e.g., Driver ID 24 ranked 3rd in DEA but 15th in PROMETHEE II), the robustness of the extreme rankings was confirmed. The study also noted that PROMETHEE II results were stable regardless of whether raw or normalized data were used, with a Spearman correlation of 0.9923 between the two data treatments. The significance of this work lies in its validation of using both DEA and PROMETHEE II for evaluating complex, multidimensional performance metrics like driving ability. The high correlation between the methods confirms the reliability of the rankings, suggesting that these tools can effectively identify high-performing benchmarks and underperforming drivers. This approach provides a structured framework for assessing fitness to drive, potentially aiding in the development of targeted interventions for older drivers with specific deficits. The authors suggest future work could combine these methods with geometrical analysis tools to provide deeper insights into individual driving characteristics and incorporate absolute evaluation standards by including an ideal driver profile.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | openalex | — | — | 5 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model