Quality of Service and Customer Satisfaction on Arterial Streets: Final Report
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Summary
This study addresses the gap in transportation research regarding how drivers perceive the quality of service (QOS) on urban arterial streets. While the Highway Capacity Manual (HCM) uses engineering metrics like speed and density to define levels of service, these measures often fail to capture road users’ actual perceptions of satisfaction. The research was motivated by the need to develop tools for measuring customer satisfaction, particularly for evaluating Intelligent Transportation Systems (ITS) and infrastructure investments. The primary objective was to identify the specific factors that influence drivers’ perceptions of service quality and satisfaction on urban arterials, a facility type previously understudied from a user-centric perspective. The researchers employed a qualitative, in-vehicle methodology involving 22 participants across four cities: Chicago, Illinois; Tallahassee, Florida; Atlanta, Georgia; and Sacramento, California. Participants drove their personal vehicles along pre-selected routes designed to represent various urban arterial conditions, including morning peak, midday, and afternoon peak periods. During approximately 45-minute drives, participants engaged in think-aloud protocols, verbally identifying roadway elements and conditions that affected their experience. An interviewer and a traffic engineer accompanied each driver to record these comments. Following the drive, participants completed a written survey rating the importance of various roadway, operational, and environmental features. Data were analyzed to categorize driver comments into specific “QOS factors” and broader “driver needs.” The study identified 45 distinct QOS factors grouped into eight investment areas: cross-sectional roadway design, arterial operations, intersection operations, signs and markings, maintenance, aesthetics, other road users, and other factors including ITS. These factors were found to support four fundamental driver needs: efficiency in traffic flow, a sense of safety, aesthetics, and positive guidance. Key findings indicated that drivers valued elements such as signal timing, lane width, pavement quality, and the presence of trees. Drivers also expressed significant concern regarding the behavior of other road users, such as aggressive driving and illegal maneuvers. The results confirmed that factors beyond traditional engineering metrics, such as aesthetics and driver courtesy, significantly influence perceived service quality. The significance of this research lies in its provision of a foundational inventory of QOS factors and driver needs, which can be used to develop standardized tools for measuring customer satisfaction. By linking specific roadway features to driver perceptions, the study offers guidance for transportation agencies aiming to improve investment strategies and evaluate ITS deployments. For instance, the findings suggest that ITS enhancements affecting intersection operations, such as signal timing and left-turn arrows, directly impact driver satisfaction by reducing delay. The report concludes that integrating these user-perception metrics with traditional engineering measures will allow agencies to better assess the effectiveness of transportation services and prioritize improvements that align with public expectations.
Key finding
Drivers identified 45 specific quality of service factors across eight investment areas that collectively support four fundamental driver needs: sense of safety, efficient traffic flow, positive guidance, and aesthetics.
Methodology
on_road
Sample size: 22
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: self report data