System Dynamics Perspective for Automated Vehicle Impact Assessment
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Summary
This report presents a framework for using system dynamics (SD) to assess the impacts of automated vehicle (AV) adoption, specifically focusing on shared-fleet mobility-on-demand services. The research addresses the challenge of predicting complex, non-linear behaviors in transportation systems that arise from causal interactions among users, service providers, and infrastructure. Because fully operational fleet-based AV services do not yet exist, the authors utilize existing transportation modes as proxies to model potential adoption dynamics and business viability. The methodology involves developing two distinct SD models to represent different aspects of the AV ecosystem. First, a Transportation Network Company (TNC) model serves as a proxy for user response, examining driver and passenger recruitment dynamics. This model incorporates causal loop diagrams to illustrate reinforcing cycles, such as how social exposure and word-of-mouth influence technology adoption, and how user adoption drives driver recruitment. Second, a dockless bikeshare model acts as a proxy for fleet management, focusing on the operator’s perspective regarding vehicle utilization and stock levels. Both models were developed through an iterative process, informed by a 2019 workshop with international researchers that identified key institutional roles and causal linkages. The models define exogenous and endogenous factors, such as perceived generalized cost, labor availability, and financial sustainability, to simulate system behavior over time. The findings identify four fundamental building blocks for analyzing any transportation mode: (1) the reinforcing cycle of technology adoption driven by social influence; (2) the business model requirements for financial sustainability, including labor and vehicle availability; (3) the reinforcing effects between service quality and user demand, where higher usage justifies increased service provision; and (4) the balancing effects of congestion, where increased use reduces utility. The TNC model demonstrates how variations in parameters like perceived cost affect the number of signed-up users and drivers, while the bikeshare model illustrates how fleet stock and utilization rates interact under different initial conditions. These simulations highlight that system behavior emerges from structural interactions rather than external shocks alone. The significance of this work lies in its contribution to strategic transportation planning tools. By applying system dynamics, planners can move beyond static equilibrium models to capture emergent behaviors and unintended consequences of AV deployment. The report concludes that SD is a valuable addition to the modeling toolbox for exploring scenarios involving rapidly changing travel demand and evolving business models. Future work aims to expand these models to include pricing, competition among modes, and equity impacts, ultimately supporting robust decision-making for long-range strategic planning in the context of automated vehicles.
Key finding
System dynamics modeling of proxy modes identified four critical building blocks for analyzing transportation mode adoption: the reinforcing cycle of technology adoption, business model sustainability, reinforcing effects of service and users, and balancing effects of congestion.
Methodology
modeling
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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- Theoretical Contribution: computational model