Effect of perception irregularity on chain-reaction crash in low visibility
Takashi Nagatani
Physica A: Statistical Mechanics and its Applications, 2015, vol. 427, issue C, 92-99
Abstract:
We present the dynamic model of the chain-reaction crash to take into account the irregularity of the perception–reaction time. When a driver brakes according to taillights of the forward vehicle, the perception–reaction time varies from driver to driver. We study the effect of the perception irregularity on the chain-reaction crash (multiple-vehicle collision) in low-visibility condition. The first crash may induce more collisions. We investigate how the first collision induces the chain-reaction crash numerically. We derive, analytically, the transition points and the region maps for the chain-reaction crash in traffic flow of vehicles with irregular perception times. We clarify the effect of the perception irregularity on the multiple-vehicle collision.
Keywords: Vehicular dynamics; Chain-reaction crash; Irregularity; Dynamic transition; Self-driven many-particle system (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437115001648
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:427:y:2015:i:c:p:92-99
DOI: 10.1016/j.physa.2015.02.058
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().