Rulla Al-Haideri, Bilal Farooq, Karim Ismail
arXiv 14 Jul 2026 · Econometrics
arXiv:2607.12299 · PDF · DOI · OpenAlex · Extracted main text
We propose a Quantum-Sequential Choice Model (Q-SCM) for modelling driver mental state evolution in interactive traffic environments. The proposed framework retains the classical latent class choice structure, but replaces the conventional class membership formulation with a quantum cognitive state model. A unique feature of this model is that the quantum component is confined to the class membership layer, while the action choice layer remains a classical RUM. The driver's latent state is represented as a two-state quantum system on the Bloch sphere including neutral and defensive states. Perceptual cues, including separation distance, closing time-to-collision (CTTC), and lane deviation induce sequential unitary rotations governed by Pauli matrices. This formulation allows the model to capture memory, phase effects, cue order dependence, and transitions between behavioural regimes that depend on prior cue history. To ensure well-behaved state evolution, we introduce three control mechanisms: a monotonicity constraint that prevents pendulum-like overshoot, a geodesic safeguard mechanism that ensures convergence toward the defensive state under sustained threat exposure, and a relaxation step that allows recovery toward the neutral baseline when the threat weakens. The model is estimated using 85,754 observations from 9,610 drivers extracted from naturalistic trajectories. The empirical results show that defensive state formation is not governed only by the instantaneous values of traffic cues, but also by the accumulated cue history and the order in which cues are processed.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Al-Haideri, Rulla and Ismail, Karim and Farooq, Bilal and Weiss, Adam (2026) Modelling driver behaviour as a continuum between defensive and neutral states self | 0.928 | 4 | 3 | 100% |
| 2 | Wang, Zheng and Solloway, Tyler and Shiffrin, Richard M. and Busemey… (2014) Context effects produced by question orders reveal quantum nature of human judgments | 0.737 | 3 | 2 | 100% |
| 3 | Greene, William H. and Hensher, David A (2003) A latent class model for discrete choice analysis: contrasts with mixed logit | 0.644 | 2 | 2 | 100% |
| 4 | Nielsen, Michael A. and Chuang, Isaac L (2010) Quantum Computation and Quantum Information | 0.585 | 3 | 3 | 33% |
| 5 | Busemeyer, Jerome R. and Wang, Zheng (2015) What Is Quantum Cognition, and How Is It Applied to Psychology? | 0.585 | 3 | 1 | 100% |
| 6 | Di Gangi, Massimo and Vitetta, Antonino (2021) Quantum utility and random utility model for path choice modelling: Specification and aggregate calibration from traffic counts | 0.585 | 3 | 1 | 100% |
| 7 | Pothos, Emmanuel M. and Busemeyer, Jerome R (2013) Can quantum probability provide a new direction for cognitive modeling? | 0.585 | 3 | 1 | 100% |
| 8 | Vitetta, Antonino (2016) A quantum utility model for route choice in transport systems | 0.585 | 3 | 1 | 100% |
| 9 | Bruza, Peter D. and Wang, Zheng and Busemeyer, Jerome R (2015) Quantum cognition: a new theoretical approach to psychology | 0.511 | 2 | 1 | 100% |
| 10 | Epping, Gunnar P. and Kvam, Peter D. and Pleskac, Timothy J. and Bus… (2023) Open system model of choice and response time | 0.511 | 2 | 1 | 100% |
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