Eric Jaffe at The Atlantic Cities says: Road Fees Don’t Hurt the Poor as Much as You Might Think – :
“Altshuler bases his position on a couple surveys conducted in metro areas that have adopted HOT lanes in the recent past. One was done in San Diego circa 2001. At that time, about 80 percent of low-income respondents agreed with the concept that people should be able to use an express lane on Interstate 15 for a fee — a greater percentage of agreement than people from high-income brackets (70 percent). Additionally, two thirds of people who didn’t even use the lanes still supported them.
A similar survey was done in 2006 in Minnesota. That work showed a 60 percent approval rate for HOT lanes on Interstate 394. A stronger analysis of this corridor, done by Tyler Patterson and David Levinson [PDF], found that income levels did predict use of the express lane (with higher-income drivers using them more often), but that lower-income drivers could also benefit from the shift of traffic out of the free lanes (as well as always having the express option in a time crunch).
(And a far more recent survey, released in April, showed that two-thirds of people making less than $50,000 a year said they’d use express toll lanes — the same percentage as people making more than that.)”
My comprehensive review of the topic is: Levinson, David (2010) Equity Effects of Road Pricing: A Review. Transport Reviews 30(1) 33-57.
The NCHRP had a report on this as well: Equity of Evolving Transportation Finance Mechanisms.
A key point is that HOT lanes also enable freeway BRT where it might otherwise be unaffordable to construct. The express lanes are uncongested and can be used by buses to maintain speed. An example is the I-35W corridor (Orange Line) south of downtown Minneapolis, which is not complete (Lake Street Station is still missing, e.g.), but has a BRT station at 46th.
We have posted some preliminary results from the traffic analysis of the I-35W Bridge collapse here: Evaluation of the Transportation Effects of the I-35W Collapse. The reports are near the bottom of the page. These are currently under review and comments are welcome.
One of the interesting scientific questions that emerges from the tragedy of the I-35W Bridge Collapse is how traffic responds. There are several time horizons for looking at this.
Most immediately are those who are on the link leading up to the bridge. MnDOT’s traffic cameras show the cars turning around on the freeway within seconds of the bridge collapsing, before the dust clears literally. “Video footage of the collapse from Mn/DOT traffic camera 628. 6:05 p.m., Aug. 1, shows an edited two-minute clip from a traffic camera at the south end of the bridge. Initially, the camera is pointed to the south away from the bridge. When traffic comes to a stop, the camera pans to the north where the bridge has just collapsed. (wv file)”. This is a rational response on the part of drivers who don’t know what else may collapse. As my wife says, there are two types of people “those who run towards the meteorite and those who run from it”. Survivors are those who ran from it.
Over the next few minutes and hours, word of the bridge collapse spread. My student Shanjiang Zhu has organized MnDOT’s loop detector data into a movie that shows the 15 minute traffic counts on all the loop detectors in the Twin Cities, comparing that number with the previous Wednesday’s count at the same time of day. Blue indicates lower volumes, red higher volumes. Clearly after the collapse, people heard quickly through various sources (cell phone, variable message signs, radio, etc.), and avoided large swaths of I-35W in the vicinity (which turns blue) and complementary feeder links, while competititve substitute links (Mn 100, I 35E, parts of I-94) saw an increase. We still have to compute how overall traffic volume and Vehicle Kilometers Traveled changed.
Once people were informed, on subsequent days people searched for alternatives. The alternative the first day for some was to avoid driving, but that quickly changed, and different routes became natural substitutes. A second movie compares the counts on the 15 days after the collapse with the average of the previous 8 weeks same day of week (so a Thursday is compared with the eight pre-collapse Thursdays). This illustrates the changes network wide. The
movie is available.
Finally, there may be some longer term adaptations, but we don’t have enough information only one month into the changed situation to know about this yet. With colleagues Henry Liu and Kathleen Harder, we have obtained a National Science Foundation Small Grant for Exploratory Research to look at all of these issues in some more depth.