Demand Simulation for Dynamic Traffic Assignment
DOI: 10.1016/s1474-6670(17)43892-4
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This thesis addresses the limitations of conventional Origin-Destination (OD) estimation models in Dynamic Traffic Assignment (DTA) systems, specifically their inability to explicitly capture how real-time pre-trip information influences traveler behavior. Traditional models update historical demand based on observed link counts but fail to account for systematic deviations caused by drivers altering their departure times, modes, or routes in response to descriptive or prescriptive guidance. To resolve this, the author proposes and implements a pre-trip demand simulator that integrates disaggregate behavioral models with aggregate OD estimation. This approach allows the system to predict future traffic conditions by explicitly simulating individual driver responses to information, thereby improving the accuracy of demand estimation and prediction within the DynaMIT framework. The methodology combines macroscopic and microscopic modeling techniques. At the disaggregate level, the simulator uses behavioral models to update historical demand based on driver responses to real-time information. These models account for habitual route choices and specific reactions to descriptive information (network conditions) and prescriptive information (route recommendations). The simulator defines user classes based on information access and behavior parameters to capture varying compliance rates. At the aggregate level, an OD estimation and prediction model utilizes the updated OD matrix as a starting point. This model employs a Kalman filtering approach, defining the state vector in terms of OD deviations from historical estimates rather than absolute flows. This structure allows the system to incorporate both the systematic deviation due to information and daily demand fluctuations, while using observed link counts to refine the estimates. The implementation prioritizes computational efficiency and numerical robustness through object-oriented design. The study evaluates the simulator through a series of case studies using a simulated network. The analysis assesses the impact of the behavioral update process, the stochasticity of the results, and the sensitivity of the estimator to input variations. Results demonstrate that the behavioral update process successfully captures deviations from historical demand induced by information. The stochasticity analysis confirms that the simulator generates realistic variations in total updated demand. Sensitivity tests reveal that the estimated demand is robust to perturbations in behavioral parameters and historical demand inputs, with relative errors remaining within acceptable bounds. The framework effectively integrates the explicit representation of pre-trip decisions with aggregate estimation, providing a consistent estimate of network states including link flows, queues, and densities. The significance of this work lies in its contribution to Intelligent Transportation Systems (ITS) by providing a tool that generates more reliable traffic predictions. By explicitly modeling the feedback loop between information provision and driver behavior, the simulator ensures that guidance strategies are based on predicted traffic conditions that account for user compliance. This addresses a critical flaw in existing DTA models, which often assume complete knowledge of future conditions or ignore pre-trip decision-making entirely. The proposed mesoscopic approach offers a viable trade-off between computational performance and estimation accuracy, enabling real-time applications for congestion management. The thesis concludes by outlining further research directions, including the assessment of real-time performance and the evaluation of the simulator in broader planning contexts.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 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 | semantic_scholar | — | — | 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Theoretical Contribution: computational model