Latest revision as of 02:29, 2 February 2021

Abstract

Contrary to traditional thinking and driver intuition, here we show that there is no benefit to ground vehicles increasing their packing density at stoppages. By systematically controlling the packing density of vehicles queued at a traffic light on a Smart Road, drone footage revealed that the benefit of an initial increase in displacement for close-packed vehicles is completely offset by the lag time inherent to changing back into a ‘liquid phase’ when flowresumes. This lag is analogous to the thermodynamic concept of the latent heat of fusion, as the ‘temperature’ (kinetic energy) of the vehicles cannot increase until the traffic ‘melts’ into the liquid phase.These findings suggest that in situations where gridlock is not an issue, drivers should not decrease their spacing during stoppages in order to lessen the likelihood of collisions with no loss in flowefficiency. In contrast, motion capture experiments of a line of people walking from rest showed higher flow efficiency with increased packing densities, indicating that the importance of latent heat becomes trivial for slower moving systems. Department of Biomedical Engineering and Mechanics at Virginia Tech Institute for Creativity, Arts, and Technology

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• https://doi.org/10.1088/1367-2630/aa95f0 under the license http://iopscience.iop.org/info/page/text-and-data-mining

• http://hdl.handle.net/10919/84307,

https://doi.org/10.1088/1367-2630/aa95f0 under the license cc-by

• http://stacks.iop.org/1367-2630/19/i=11/a=113034/pdf,

http://stacks.iop.org/1367-2630/19/i=11/a=113034?key=crossref.d6fdd4a0603b9a8d3d0fc353c7e27987,

http://dx.doi.org/10.1088/1367-2630/aa95f0 under the license http://creativecommons.org/licenses/by/3.0/

• https://iopscience.iop.org/article/10.1088/1367-2630/aa95f0/pdf,

https://iopscience.iop.org/article/10.1088/1367-2630/aa95f0/meta,

https://ui.adsabs.harvard.edu/abs/2017NJPh...19k3034F/abstract,

https://vtechworks.lib.vt.edu/handle/10919/84307,

https://trid.trb.org/view/1492284,

https://academic.microsoft.com/#/detail/2770718500 under the license http://iopscience.iop.org/info/page/text-and-data-mining

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