Elucidating Super-Compressible States in Inner Structure of Vehicular Flow
Source abstract
Abstract
The article deals with the open questions in the theory of vehicular headway modeling. Specifically, the question of the existence of anomalous constellations in vehicular traffic micro-structure, in which the rate of fluctuations (measured by the stochastic compressibility) exceeds the fluctuation level of systems with non-interacting elements. The solution to this open problem is converted into the mathematical format working with the so-called balance particle systems, where seeking relevant relations is more straightforward and thus significantly easier. Presented research has shown that unit compressibility represents (despite popular opinion) the upper limit only for particle systems, in which there is no attractive interaction between the particles. In the article, the specific system is constructed in which the presence of an attractive force component will cause higher fluctuations than in the Poisson systems of non-interacting elements. This means that traffic constellations with higher compressibility (so-called super-compressible constellations) can be explained either by a discrepancy between the empirical traffic flow and the mathematical model used, or by the presence of attractive forces acting between individual vehicles. Using empirical vehicular data (measured on two parallel freeway lanes under reconstruction), we show that super-compressible states occur even though overtaking is prohibited. This means, therefore, that these super-compressible states arose without a doubt due to the mutual attraction of successive vehicles. In addition, the article shows that the presence of the aforementioned attractive forces appears predominantly in the fast lane, and only in situations where the traffic density is relatively low. At higher densities, the two freeway lanes are markedly synchronized, the opportunity for a sporty style of driving vanishes and the reason for changing lanes disappears. Under these circumstances, the attractive force component vanishes, which finally leads to the transition of the entire traffic system back to standard sub-compressible states.
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Research context
OpenAlex topics: Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, 120 00 Prague, Czech Republic
Faculty of Transport Engineering, University of Pardubice, 530 01 Pardubice, Czech Republic
Funding
TAČR · Ministry of Education, Youth, and Sports of the Czech Republic (MŠMT ČR)
References (34)
Show source-provided references
1. A cellular automaton model for freeway traffic
2. Traffic flow in 1D cellular automaton model including cars moving with high speed10.1143/JPSJ.65.1868 ↗
3. An exact solution of a one-dimensional asymmetric exclusion model with open boundaries10.1007/BF01050430 ↗
4. Treiber, M., and Kesting, A. (2013). Traffic Flow Dynamics, Springer.10.1007/978-3-642-32460-4 ↗
5. Kerner, B.S. (2004). The Physics of Traffic, Springer.10.1007/978-3-540-40986-1 ↗
6. Vehicle headway modeling and its inferences in macroscopic/microscopic traffic flow theory: A survey10.1016/j.trc.2017.01.007 ↗
7. Statistical rigidity of vehicular streams—Theory versus reality10.1088/2399-6528/ab0d47 ↗
8. Inner structure of vehicular ensembles and random matrix theory10.1016/j.physleta.2016.03.037 ↗
9. Modeling highway-traffic headway distributions using superstatistics
10. A Markov Model for Headway/Spacing Distribution of Road Traffic10.1109/TITS.2010.2050141 ↗
11. Headway distribution models of two-lane roads under mixed traffic conditions: A case study from India10.1007/s12544-017-0276-2 ↗
12. Service headway distribution analysis of FASTag lanes under mixed traffic conditions10.1016/j.physa.2022.127904 ↗
13. Spatial correlation analysis of traffic flow on parallel motorways in Germany10.1016/j.physa.2022.127367 ↗
14. Super-random states in vehicular traffic—Detection & explanation10.1016/j.physa.2021.126418 ↗
15. Traffic and related self-driven many-particle systems10.1103/RevModPhys.73.1067 ↗
16. (2023, August 14). Wikipedia. Available online: https://en.wikipedia.org/wiki/Relation_(mathematics).
17. Fundamentals of traffic flow10.1103/PhysRevE.55.3735 ↗
18. Useful Headway Models10.1016/0041-1647(75)90008-8 ↗
19. Coherent moving states in highway traffic10.1038/25499 ↗
20. Trajectory data-based traffic flow studies: A revisit10.1016/j.trc.2020.02.016 ↗
21. Pedestrian headways—Reflection of territorial social forces10.1016/j.physa.2017.08.013 ↗
22. The log-normal distribution of headways
23. Equilibrium distributions in thermodynamical traffic gas10.1088/1751-8113/40/22/004 ↗
24. Chen, X.M., Li, L., and Shi, Q. (2014). Traffic Flow Dynamics, Springer.
25. Hamilton-like statistics in one-dimensional driven dissipative many-particle systems10.1140/epjb/e2009-00121-8 ↗
26. Short-range plasma model for intermediate spectral statistics10.1007/s100510170357 ↗
27. Dyson’s Coulomb gas on a circle and intermediate eigenvalue statistics10.1088/0305-4470/23/6/018 ↗
28. Understanding interarrival and interdeparture time statistics from interactions in queuing systems10.1016/j.physa.2006.01.048 ↗
29. Lhotaková, A. (2023). Scaling and Estimating for GIG-Distributed Data. [Master’s Thesis, Czech Technical University].
30. Pánek, V. (2022). Statistical Properties of Thermodynamic Particle Gas with Combined Potential. [Master’s Thesis, Czech Technical University].
31. Application of Bayesian model averaging for modeling time headway distribution10.1016/j.physa.2023.128747 ↗
32. Mehta, M.L. (2004). Random Matrices, Academic Press. [3rd ed.].
33. Spectral Rigidity and Eigenfunction Correlations at the Anderson Transition10.1134/1.567208 ↗
34. Bohigas, O. (1991). Random Matrix Theories and Chaotic Dynamics, Elsevier Science Publisher.