Accessibility map of San Francisco
A new report in the Access Across America series from the Accessibility Observatory estimates the accessibility to jobs by walking in the 50 largest (by population) metropolitan areas in the United States.
The report—Access Across America: Walking 2014—presents detailed accessibility values for each of the 50 metropolitan areas, as well as detailed block-level color maps that illustrate the spatial patterns of accessibility within each area. Rankings were determined by a weighted average of accessibility, giving a higher weight to closer jobs.
Top 10 accessible metro areas:
A separate publication, Access Across America: Walking 2014 Methodology, describes the data and methodology used in this evaluation.
by Kay Axhausen
In simple terms, we are either traveling unaffected by the movements of others (which engineers call freeflow), or we are slowed by others (which we call queueing), either directly, due to interactions (such as congestion), or because of controls to manage those interactions (like traffic signals).
In freeflow, the traveller or customer can choose her own speed without imposing her choice on other nearby travelers. She can choose her own time and receives immediate service at some “counter”: the toll plaza, the empty road, the waiter in a restaurant, the ATM, the website for booking a flight, the cashier in the supermarket, the advisor at the bank, the food counter in the cafeteria and many others.
Utilization rate (ρ) is the ratio of arrivals (at the back of the line) per unit time (λ) to departures (from the front of the line) (μ). Crucial for the lived experience and for the planner or manager is that the waiting time in queues escalates to very high numbers when the degree of saturation reaches 80% to 85% of the available capacity. The Figure shows this explosion for the mathematically simplest to capture queue: one serving station with demand and supply following an exponential distribution of arrival and service times, in the notation of the field an M/M/1 queue. Still, this pattern holds across all systems of queues with any randomness at all.
There are peaks of demand which the human preferences generate: we want to work during the day and have to work at the same time as our supply chain and customers and co-workers, we all want to be at the pool during the brilliant summer day, we prefer the fashionable cool restaurant or the McDonalds right next to big office building.
Queues emerge in many transport situations: a group leaving a room having to wait at the door, as only one or two people can physically walk through it at any one moment in time; or a funeral procession occupying both lanes of a road but traveling at a slow speed. A system which works well with an uniform number of arrivals (1 car every 2 seconds exactly) sees queues form when it gets 4 cars in 1 second, even if it gets no cars for the next 7 seconds and thus has the same arrival rate.
Queues emerge not only when demand exceeds the provided supply, but when the provided supply changes. The most visible example is when there is a physical change in capacity when traveling along a road, such as losing a lane, climbing a hill, or seeing a two lanes merge into one or even one lane diverging into two. But the supply can also change dynamically. Think about a toll plaza. Not all the tollbooth lanes are staffed all the time. If supply is roughly in sync with demand, the lines are short. If supply far exceeds demand, there are no lines, but when supply falls short of demand, queues are long. Unfortunately for the customer, no supplier can afford to provide so many counters or so much space in the transport system that travelers always encounter freeflow conditions.
The supplier wants to offer a certain level of service, i.e. speed or waiting time until service starts, but without having too much unused space, empty seats in the bus or train, or underemployed staff, which are costs without revenue. Furthermore, providing capacity encourages the users to arrive at the peak time, which makes the peaking problem worse in the longer term.
To make things worse in daily practice, the capacity of the serving stations varies randomly around a mean for a variety reasons: weather, lighting conditions, the experience of the drivers, skill level of the person serving, random crashes in computer networks.
Tom Vanderbilt write Five myths about traffic in the Washington Post:
1. More roads = less traffic.
This is the granddaddy of all traffic myths, one still held dear by the average driver and certain precincts of state highway offices. More funding for more roads is on the way in Texas, where the governor declared that residents are “tired of being stuck in traffic.” On Memorial Day, it will assume the stature of an intuitive truth: If they just built more roads, we’d be home by now.
But that reasoning doesn’t stand. First, Memorial Day is one of a handful of peak travel days. “You don’t build a church for Easter Sunday,” as the saying goes (a lesson most shopping malls in America have not heeded, judging by their acres of empty parking lots). More broadly, whatever short-term gain that comes with capacity expansion is generally eaten up by longer-term behavioral shifts. As University of Toronto researchers Gilles Duranton and Matthew Turner describe what has been called the “fundamental law” of traffic congestion: “People drive more when the stock of roads in their city increases.”Aren’t planners simply keeping up with population growth? Perhaps. Except that growth in vehicle miles traveled has consistently outpaced population growth over the past few decades. And as transportation researcher David Levinson has noted, U.S. roads are already bristling with spare capacity and inefficient use is rampant (such as too-large single-occupant cars all traveling to work at the same time on too-wide lanes). He argues that we should focus time and resources on using the highways we already have more efficiently, rather than on building more.
MPR News reporter Jon Collins covers our recent bike study: New analysis shows far more Twin Cities residents walking, biking to work. My quotes below, though most of the work is due to PhD Candidate Jessica Schoner and Prof. Greg Lindsey. The full report will be published within a week.
Minneapolis’ recent investments in bicycling and walking infrastructure, such as trails and bike boulevards, may be one reason that rates in the city appear to have been increasing at such a rate, said David Levinson, a University of Minnesota professor of transportation and principal investigator of the study.
“If you want to attract people who want to bicycle, putting the facilities in will attract them,” Levinson said. “People who know they want to bicycle are more likely to live in Minneapolis than to move to the suburbs.”
Although women who bicycle took as many trips as men who bicycle, the proportion of women who bicycle even in Minneapolis is about one-and-a-half times smaller than men. Even though Minneapolis leads the region in bicycle-friendly development, Levinson said the gender gap may be due to the fact that the infrastructure still isn’t developed to the point where many people feel safe bicycling.
“Right now we allocate road space for moving cars, we allocate road space for storing cars, but very few places are reallocating road space for moving bicycles,” Levinson said. “Is moving bicycles more important than storing cars?
Sometimes people think places are farther away than they really are, and other times they are closer. Freeways seem to take shorter than they really area, local streets longer. This in part has to do with task complexity, or the “mental transaction costs” involved in traveling.1
When I need to make a lot of small driving and navigation decisions, like on a signalized route with lots of turns, I need to focus on driving more times. Each time I am engaging my conscious brain in traveling decisions. More brain-space is occupied by traveling thoughts.
Other factors include temporal relevance (is the trip important), temporal expectancies (what do I think the travel time will be) temporal uncertainty (how reliable is my estimate of travel time), affective elements (what is the emotional state of the traveler), absorption and attentional deployment (am I paying attention to the task at hand) and arousal (how physically activated am I, am I on drugs?).2
When I can drive on an uncongested freeway, I can avoid many such thoughts. Driving is less salient. Time passes faster. As the expression goes, “time flies when you are having fun”
In travel surveys we have a common phenomenon of rounding reported times, and times are usually rounded up. So if a trip was 14 minutes, it would be rounded to 15 minutes. If it were 22 minutes, it might be rounded to 25 or even 30 minutes. This makes self-reported times significantly biased in travel analysis. Until recently, that was the only data available. But now with the advent of GPS devices and cheap sensors tracking traffic across networks, we can get much better speed and travel time estimates.
Vierordt’s Law claims people are more likely to over-estimate short times and under-estimate long times.
We did not corroborate this with a driving simulator study for waiting at a traffic signal.3 Perceived and actual waiting time were virtually identical for the first 30 seconds, but for times greater than 30 seconds, actual waiting time was higher than perceived waiting time, up to 120 seconds. At 120 seconds, the trend was for perceived time to overtake actual time, but that was the cut-off for the experiment, so perception findings in this situation require more information. However, the annoyance level at 120 seconds of waiting was much higher than the annoyance of waiting 30 seconds. Further people hated stops.
Of course, with all of this, it depends on how you frame the question, what you ask, and what travelers were expecting. Comparing a computer-administered stated preference with one in which travelers were in a driving simulator completely flipped preferences for traveling (waiting for free flowing travel vs. muddling through in congestion).4
1 Parthasarathi, Pavithra, David Levinson, and Hartwig Hochmair (2013) “Network Structure and Travel Time Perception.” PLOS ONE: 8(10): e77718.
2 Carrion, C. and D. Levinson (2012). Uncovering the influence of commuters’ perception on the reliability ratio. Technical report.
3 Wu, X., D. M. Levinson, and H. X. Liu (2009). Perception of waiting time at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board 2135(1), 52–59.
4 Levinson, D., K. Harder, J. Bloomfield, and K. Carlson (2006). Waiting tolerance: Ramp delay vs. freeway congestion. Transportation Research Part F: Traffic Psychology and Behaviour. 9 (1), 1–13.
In light of Amtrak 188, this seems appropriate from England in 2007
We were returning from Harrogate to London last night on the Great North Eastern Railway (GNER) after attending the well-run and interesting UTSG conference.
The trip from Harrogate to Leeds was uneventful. On the trip from Leeds to Kings Cross in London, we were interrupted by what I believe the announcer said was a Code 3 on Coach M. (We were on a different coach so at the time didn’t know what that was), though we did not stop there.
Later the announcer told us that there had been vandalism, a rock through a window, which needed to be repaired before we proceeded.
The coach was held at Peterborough for about 15 minutes while repairs were made to the broken window, and we arrived 15 minutes late. When we arrived, I could not locate the vandalism, so it must have been cleaned up fairly well.
Three observations spring to…
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With the recent Amtrak crash, media has once again taken interest in the sorry state of passenger rail in the US.
I get quoted in an article by Alan Neuhauser for US News and World Report: With Amtrak, Smaller May Be Better: Slashing the rail service’s budget may be just the thing Amtrak needs. We talked for 17 minutes. My soundbite:
“Getting rejected by Congress might be the best thing for rail in the Northeast,” says David Levinson, a transportation engineering professor at the University of Minnesota. “It would have a short-term disruptive effect unless something else is put in place,” he allows, but “for real change, you have to change the governance: who’s getting the money, who’s spending the money, how much they’re getting.”
Throughout the past decade, the popularity and importance of biking and walking have increased significantly across the nation. Pinpointing exactly how people incorporate biking and walking into their travel behavior, however, is a persistent challenge.
In an extensive five-part research study sponsored by the Minnesota Department of Transportation (MnDOT) and the Metropolitan Council, U of M researchers are exploring a rich set of data generated through detailed travel behavior inventories in the Twin Cities region. The data allowed them to analyze changes in walking and biking behavior during the past decade.
“Overall, our team found that auto travel decreased between 2000 and 2010, while biking and walking increased during that time,” says Professor David Levinson, RP Braun/CTS Chair in the Department of Civil, Environmental, and Geo- Engineering (CEGE) and the study’s principal investigator.
Bicycling grew from 1.4 percent of all trips in 2000 to 2.2 percent in 2010—an increase of 58 percent. Walking started with a larger share in 2000 (4.5 percent) and grew by a larger amount to 6.6 percent of trips in 2010—a 44 percent increase. One of the most important findings is that the actual bicycle mode share in the Twin Cities region is two to three times larger than reported by national data.
“Though the private auto still consistently dominates travel in the region, these relatively small mode shares translate to a substantial number of walk and bike trips on an average day in the region,” says Jessica Schoner, CEGE research assistant and lead author of the study. “We estimate about 12 million daily trips across the metro area, which means that on an average day people are making 190,000 bike trips and 735,000 walking trips.”
The researchers noted key differences between bicyclists and pedestrians and their walk and bike trips, including demographics, geography, trip purpose, and trip distance. For example, they found that while men and women choose to walk at an equal rate, a “gender gap” persists among bicyclists.
“Most of the growth in cycling came from increases in men commuting by bicycle, but closer examination of the gender gap in bicycling revealed some encouraging information,” Schoner says. “The gap appears to be in bicycling participation rates of men and women—there was no observed gap in the frequency of making bicycle trips among cyclists. This shows that programs encouraging women to try bicycling may help to further boost bicycling mode share.”
New bike infrastructure appears to have played a key role in the increase of bicycle mode share in the past decade. “While infrastructure was a significant factor in predicting bicycling in 2001, by 2010 the quantity of bicycle lanes around the home no longer differentiated bicyclists from non-bicyclists,” Schoner says. “This suggests that the Twin Cities’ expanded bicycle infrastructure has created pervasive and easy access throughout the city.”
Greg Lindsey, professor in the Humphrey School of Public Affairs and MnDOT’s first Scholar-in-Residence, is a co-author of the report. He was also the principal investigator of research conducted under MnDOT’s Bicycle and Pedestrian Counting Initiative.
“MnDOT is developing a program to monitor bicycle and pedestrian traffic that is modeled after the motor vehicle count program,” says Lisa Austin, MnDOT bicycle and pedestrian planning coordinator. “When used together, the traffic volumes and the travel diaries provide a clearer picture of how, when, and why people are traveling. This helps MnDOT and other agencies plan for better systems and analyze safety.”
The first project completed under the five-part study examined how changes in the accessibility of destinations have altered travel behavior in the past 20 years (see article in the February 2015 Catalyst). Additional parts of the study will look at the effect of transit quality of service on people’s activity choices and time allocation, changes in travel behavior by age group, and transportation system changes. The Catalyst will feature coverage of these projects as they are completed.
- Research project page
- “Travel behavior study shows drivers are spending less time traveling and more time at home,” CTS Catalyst, Feb. 2014
Reprinted from CTS Catalyst.