Metrics drive behaviors some not intended, Example of CA HOV change for Hybrid Cars

PUE metric is simple and effective way for people to understand the power and cooling efficiency of their data center.  The more people discussing PUE, the lower the numbers go, the difference hopefully will decrease.  A strange example of a difference causing an unintended affect is in California's booting the "Prius Perk" from the HOV lanes.


Loss Of California HOV-Lane 'Prius Perk' Slows All Traffic


Published October 14, 2011

| High Gear Media

Many California drivers silently cheered on July 1, when the yellow stickers on 85,000 hybrid cars expired--meaning their drivers were no longer allowed to travel solo in the carpool lane.

According to a new study released Monday at the University of California-Berkeley, though the loss of the single-occupant privilege that was cheered by other drivers may have made their own lives paradoxically worse.

The study is here.

We verify that slow speeds in a special-use lane, such as a carpool or bus lane, can be due to both high demand  for that lane and slow speeds in the adjacent regular-use lane.  These dual influences are confirmed from months of data collected from all freeway carpool facilities in the San Francisco Bay Area.  Additional data indicate that  both influences hold not only for other types of special-use lanes, including bus lanes, but also for other parts of the world.

The findings do not bode well for a  new  US  regulation stipulating that most classes of LowEmitting Vehicles, or LEVs, are to vacate slow-moving carpool lanes.  These LEVs invariably constitute small percentages of traffic; e.g. they are only about 1% of the freeway traffic demand in the San Francisco Bay Area.  Yet, we show that relegating some or all of these vehicles to regular-use lanes can significantly add to regular-lane congestion, and that this, in turn, can also be damaging to vehicles that continue to use the carpool lanes.  Counterproductive outcomes of this kind are predicted first by applying kinematic wave analysis to a real Bay Area freeway.  The site stands to suffer less from the  regulationthan will others in the region. Yet,  we predict that the site’s people-hours and vehicle-hours traveled during the rush will each increase by  more than 10%, and that carpool-lane traffic will share  in  the damages.  Real data from the site support these predictions.  Further parametric analysis of a hypothetical, but more generic freeway system indicates that  these kinds of  negative outcomes will be widespread.  Constructive ways to amend the new regulation are discussed, as are promising strategies to increase the vehicle speeds in carpool lanes by improving the travel conditions in regular lanes.