Modelling lane changing behaviour in approaches to roadworks: Contrasting and combining driving simulator data with stated choice data
DOI: 10.1016/j.trc.2019.12.003
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the challenge of modeling mandatory lane-changing behavior in approaches to roadworks, where drivers’ underlying target lane preferences (plans) are often latent and obscured by traffic constraints that prevent execution (actions). Traditional trajectory data only reveals observed actions, making it difficult to distinguish between preference-driven delays and constraint-induced delays. The authors aim to contrast and combine driving simulator data, which captures constrained actions, with stated choice (SC) data, which reveals unconstrained preferences, to develop more accurate probabilistic models for traffic microsimulation and management. The research utilized data from 40 participants who completed both a high-fidelity driving simulator experiment and a stated preference survey. The simulator, featuring a motion-base vehicle and immersive visual environment, recorded driver trajectories and surrounding traffic conditions (speeds, positions) at 60Hz as participants approached a double lane closure on a four-lane motorway under low and high traffic flow scenarios. The SC survey presented static screenshots from the simulator to 35 of these participants, asking for their preferred target lanes at various distances from the closure without the need to execute the maneuver. The authors developed a joint latent class model comprising two components: a target lane choice model (logit structure) and a gap acceptance model (log-normal distribution of critical gaps). This framework allowed for the estimation of generic parameters across four latent classes, capturing heterogeneity in preferences for specific lanes and the timing of lane changes. Initial analysis revealed a strong correspondence between simulator and SC data regarding the proportion of drivers moving into open lanes as they approached the closure, with shares increasing from approximately 70% at 600–400 yards to over 90% in the final 200 yards. However, the correlation between stated intent to change lanes quickly and actual simulator behavior was low (0.08), highlighting the significant impact of surrounding traffic constraints. The modeling results indicated that utilities for closed lanes became increasingly negative as drivers approached the roadworks, and a significant penalty was associated with changing lanes. The joint latent class model successfully integrated both data sources, demonstrating that SC data could effectively inform the latent plan component while simulator data informed the gap acceptance component. The study concludes that combining stated choice and driving simulator data provides deeper insights into lane-changing behavior than either source alone, allowing researchers to untangle driver preferences from external constraints. This approach offers a cost-effective method for model development, as SC data can reduce the reliance on expensive simulator experiments without compromising fidelity. The findings have implications for traffic management and the improvement of behavioral realism in traffic microsimulation tools, particularly in understanding mandatory lane changes near roadworks.
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 | Crossref | — | — | 1 | 2026-06-16 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-25 |
| 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 | — | — | 5 | 2026-07-05 |
| promote | success | — | — | — | 1 | 2026-06-16 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; 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).
- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model