Research PaperNo Access

Influence of driver behavior in the emergence of traffic gridlocks

Enrique Pazos

Instituto de Investigación en Ciencias Físicas y Matemáticas, Universidad de San Carlos de Guatemala, Edificio T-1, Ciudad Universitaria Z. 12 Guatemala 01012, Guatemala

E-mail Address: [email protected]

Search for more papers by this author

https://doi.org/10.1142/S0129183121500510Cited by:1 (Source: Crossref)

Abstract

We present a microscopic driving algorithm that prescribes the acceleration using three parameters: the distance to the leading vehicle, to the next traffic light and to the nearest stopping point when the next traffic light is in the red phase. We apply this algorithm to construct decision trees that enable two driving behaviors: aggressive and careful. The focus of this study is to analyze the amount of aggressive drivers that are needed in order to generate a traffic gridlock in a portion of a city with signalized intersections. At rush hour, aggressive drivers will enter the intersection regardless if they have enough time or space to clear it. When their traffic light changes they block other drivers, thus providing the conditions for a gridlock to develop. We find that gridlocks emerge even with very few aggressive drivers present. These results support the idea of promoting good driving behavior to avoid heavy congestion during rush hours.

Keywords:

PACS: 02.70.−c, 89.65.−a, 05.65.+b

References

1. D. Chowdhury, L. Santen and A. Schadschneider, Phys. Rep. 329, 199 (2000). Crossref, Web of Science, ADS, Google Scholar
2. D. Helbing, Rev. Mod. Phys. 73, 1067 (2001). Crossref, Web of Science, ADS, Google Scholar
3. K. Nagel and M. Schreckenberg, J. Phys. I 2, 2221 (1992). Crossref, Web of Science, ADS, Google Scholar
4. O. Biham, A. A. Middleton and D. Levine, Phys. Rev. A 46, p. R6124 (1992). Crossref, Web of Science, ADS, Google Scholar
5. J. Esser and M. Schreckenberg, Int. J. Mod. Phys. C 8, 1025 (1997). Link, Web of Science, ADS, Google Scholar
6. D. Chowdhury and A. Schadschneider, Phys. Rev. E 59, R1311 (1999). Crossref, Web of Science, ADS, Google Scholar
7. A. Mhirech and A. A. Ismaili, Int. J. Mod. Phys. C 24, 1350050 (2013). Link, Web of Science, ADS, Google Scholar
8. L. A. Pipes, Transp. Res. 1, 21 (1967). Crossref, Google Scholar
9. G. F. Newell, Transp. Res. B: Methodol. 36, 195 (2002). Crossref, Web of Science, Google Scholar
10. M. Treiber, A. Hennecke and D. Helbing, Phys. Rev. E 62, 1805 (2000). Crossref, Web of Science, ADS, Google Scholar
11. P. A. Ehlert and L. J. Rothkrantz, Microscopic traffic simulation with reactive driving agents, in Proc. Intelligent Transportation Systems (IEEE, 2001). Crossref, Google Scholar
12. C. F. Daganzo, Transp. Res. B: Methodol. 39, 934 (2005). Crossref, Web of Science, Google Scholar
13. N. Geroliminis and C. F. Daganzo, Transp. Res. B: Methodol. 42, 759 (2008). Crossref, Web of Science, Google Scholar
14. A. Mhirech, Canad. J. Phys. 89, 1273 (2011). Crossref, Web of Science, ADS, Google Scholar
15. L. Qi, M. Zhou and W. Luan, IEEE Trans. Intelli. Transp. Syst. 18, 1882 (2016). Crossref, Web of Science, Google Scholar
16. D. Helbing and B. Tilch, Phys. Rev. E 58, 133 (1998). Crossref, Web of Science, ADS, Google Scholar
17. J. A. Laval and L. Leclercq, Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 368, 4519 (2010). Crossref, Web of Science, ADS, Google Scholar
18. B. S. Kerner, Phys. Rev. E 84, 045102 (2011). Crossref, Web of Science, ADS, Google Scholar
19. A. Schadschneider, Physica A 313, 153 (2002). Crossref, Web of Science, ADS, Google Scholar
20. G. Paissan and G. Abramson, Europ. Phys. J. B 86, 153 (2013). Crossref, Web of Science, ADS, Google Scholar
21. H. Fan, B. Jia, J. Tian and L. Yun, Physica A 415, 172 (2014). Crossref, Web of Science, ADS, Google Scholar
22. K. J. Szajowski and K. Włodarczyk, Mathematics 8, 433 (2020). Crossref, Web of Science, Google Scholar