Technology Influence on Travel Demand and Behaviors
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Summary
This study investigates the influence of Transportation Network Companies (TNCs), such as Uber and Lyft, on travel demand, mode choice, and behaviors in medium-sized cities within the Southeastern United States. Motivated by the rapid growth of shared-use economy applications and a lack of systematic understanding regarding their impact on urban congestion and transportation efficiency, the research aims to identify determinants driving TNC adoption and quantify their effects on travel patterns. The study addresses a gap in existing literature, which has largely focused on European or Australian contexts or lacked evidence-based conclusions on congestion impacts in U.S. auto-oriented environments. The research employed a mixed-methods approach comprising a comprehensive literature review and three distinct case studies. The first case study involved an online survey of 600 millennials (born 1981–1996) in North Carolina to assess travel behavior and awareness of ride-hailing services in a market with recent service expansion. The second case study utilized a travel diary questionnaire survey of over 450 transportation users in the Birmingham, Alabama metro area to document attitudes, preferences, and specific trip details regarding TNC usage. The third case study evaluated the feasibility of developing an agent-based simulation model for the Birmingham region using the Multi-Agent Transport Simulation (MATSim) platform. This modeling effort aimed to address the limitations of traditional traffic simulation models in simulating shared modes, employing a data-driven approach to handle data sparsity and synthesize population data for the simulation. Key findings from the North Carolina survey revealed that 66% of millennials had used Lyft, Uber, or both, with significant variations in usage across ethnic and racial groups, indicating high awareness and adoption even in lower-density areas. The Birmingham survey found that 45% of respondents had used TNC services. Primary determinants for TNC selection included convenience, reduced traffic safety concerns (particularly for late-night trips), lack of parking availability at destinations, and lack of personal vehicle availability. The modeling case study successfully developed a prototype MATSim model for Birmingham, identifying necessary data inputs and demonstrating the feasibility of using agent-based simulation to study the impact of shared-use economy applications on local and regional congestion. The study concludes that technology significantly influences driving choices and mode selection in the Southeast, with TNCs serving as a viable alternative driven by convenience and safety perceptions. The development of the prototype agent-based model provides a foundation for future research to quantify the integration of TNCs with public transit and their broader impacts on urban congestion. These findings offer transportation agencies tools to better plan Mobility as a Service (MaaS) strategies and provide TNC operators with insights into local market needs and travel behavior trends.
Key finding
66% of surveyed millennials in North Carolina and 45% of Birmingham residents had used TNC services, with adoption primarily driven by convenience, safety, and parking availability.
Methodology
mixed_methods
Sample size: 1050
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 | — | — | 24 | 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: observational prevalence