A confirmatory factor analysis of road users’ cognitive attitudes towards contributing factors of traffic congestion
DOI: 10.1007/s44327-025-00053-7
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Summary
This study investigates how road users in Kumasi, Ghana, cognitively perceive and interpret the factors contributing to traffic congestion. While previous research has identified various causes of congestion, there is a lack of comprehensive evaluation regarding how users in low- and middle-income countries assess the extent of these contributions and their underlying interrelationships. The authors argue that understanding these perceptions is critical because they influence travel behaviors, such as route choice and departure times, which ultimately affect traffic flow. The research aims to identify key contributing factors and dimensions from the users' perspective and explore the interrelationships among these dimensions to inform more effective congestion mitigation strategies. The researchers employed a mixed-mode survey technique, combining face-to-face interviews and online questionnaires, to collect data from 539 valid respondents, including drivers and passengers. Participants rated the significance of 14 potential congestion factors using a 5-point Likert scale. The study utilized Confirmatory Factor Analysis (CFA) via SPSS AMOS to test a hypothesized model comprising four latent dimensions: traffic demand, control devices, side friction, and incidents. This statistical approach allowed the authors to verify the factor structure and examine the relationships between observed variables and underlying constructs, moving beyond the descriptive statistics used in prior studies. The analysis revealed that road users perceive poor traffic signal functioning, potholes, high traffic volume, on-street markets, indiscriminate parking, and rainfall as critical contributors to congestion. The CFA model demonstrated satisfactory goodness-of-fit indices, confirming that the contributing factors cluster into four interconnected dimensions. Specifically, "traffic demand" was measured by vehicular volumes; "control devices" by signal and checkpoint operations; "side friction" by parking, pedestrian interactions, and road conditions; and "incidents" by weather and breakdowns. The model showed statistically significant loadings for all retained items, indicating that users’ perceptions of these specific factors strongly align with the broader underlying dimensions. The findings provide a structured framework for understanding traffic congestion from the user’s cognitive perspective. By identifying the specific dimensions and their interrelationships, the study offers transport planners and engineers evidence-based insights for prioritizing resources. The authors conclude that tailoring mitigation initiatives to address these key perceived factors—particularly those with high potential payoff—can lead to more sustainable transport systems. This approach bridges a gap in the literature by applying advanced statistical modeling to user perceptions in a developing urban context, offering a foundation for policies that account for the complex, multidimensional nature of congestion.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 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 | 2026-06-25 |
| 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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