Access Across America: Auto 2015

CTS Catalyst September 2016 just came out, and announces our Access Across America: Auto 2015 study: Study estimates accessibility to jobs by auto in U.S. cities. The article is reprinted below:

Map of Accessibility to jobs by auto in U.S.
Accessibility to jobs by auto

A new report from the University’s Accessibility Observatory estimates the accessibility to jobs by auto for each of the 11 million U.S. census blocks and analyzes these data in the 50 largest (by population) metropolitan areas.

“Accessibility is the ease and feasibility of reaching valuable destinations,” says Andrew Owen, director of the Observatory. “Job accessibility is an important consideration in the attractiveness and usefulness of a place or area.”

Travel times are calculated using a detailed road network and speed data that reflect typical conditions for an  8 a.m. Wednesday morning departure. Additionally, the accessibility results for 8 a.m. are compared with accessibility results for 4 a.m. to estimate the impact of road and highway congestion on job accessibility.

Map of U.S. showing reduced job accessibility due to congestion
Reduced job accessibility due to congestion

Rankings are determined by a weighted average of accessibility, with a higher weight given to closer, easier-to-access jobs. Jobs reachable within 10 minutes are weighted most heavily, and jobs are given decreasing weights as travel time increases up to 60 minutes.

Based on this measure, the research team calculated the 10 metropolitan areas with the greatest accessibility to jobs by auto (see sidebar).

A similar weighting approach was applied to calculate an average congestion impact for each metropolitan area. Based on this measure, the team calculated the 10 metropolitan areas where workers experience, on average, the greatest reduction in job access due to congestion (see sidebar).

Areas with the greatest loss in job accessibility due to congestion

  1. Los Angeles
  2. Boston
  3. Chicago
  4. New York
  5. Phoenix
  6. Houston
  7. Riverside
  8. Seattle
  9. Pittsburgh
  10. San Francisco

Metropolitan areas with the greatest job accessibility by auto

  1. New York
  2. Los Angeles
  3. Chicago
  4. Dallas
  5. San Jose
  6. San Francisco
  7. Washington, DC
  8. Houston
  9. Boston
  10. Philadelphia

“Rather than focusing on how congestion affects individual travelers, our approach quantifies the overall impact that congestion has on the potential for interaction within urban areas,” Owen explains.

“For example, the Minneapolis–St. Paul metro area ranked 12th in terms of job accessibility but 23rd in the reduction in job access due to congestion,” he says. “This suggests that job accessibility is influenced less by congestion here than in other cities.”

The report—Access Across America: Auto 2015—presents detailed accessibility and congestion impact values for each metropolitan area as well as block-level maps that illustrate the spatial patterns of accessibility within each area. It also includes a census tract-level map that shows accessibility patterns at a national scale.

The research was sponsored by the National Accessibility Evaluation Pooled-Fund Study, a multi-year effort led by the Minnesota Department of Transportation and supported by partners including the Federal Highway Administration and 10 state DOTs.


Related Links

Demand for Future Transport

There are differing beliefs about the effects of autonomous vehicles on travel demand. On the one hand, we expect that automation of itself is a technology that makes travel easier, it pushes the demand curve to the right. For the same general cost, people are more willing to travel. Exurbanization has a similar effect (and automation and exurbanization form a nice positive feedback system as well).

Demand vectors for vehicle travel in a changing technological and socio-economic environment.
Demand vectors for vehicle travel in a changing technological and socio-economic environment.

On the other hand, the move from private vehicle ownership to mobility as a service, which is likely in larger cities means that the marginal cost of a trip might rise from very low (since the vehicle is already owned) to high (since the cost of the vehicle has to be recovered on a per-trip basis). This moves the demand curve to the left. It is similar in effect to urbanization (and urbanization and mobility-as-a-service form a nice positive feedback system). Lots of other changes also move the demand curve to the left, including demographic trends, substituting information technologies for work, socializing, and shopping, and dematerialization.

Income moves the willingness to pay for the same amount of travel up or down.

Changes in the price structure of travel move along the demand curve as shown here.

The End of Traffic and the Future of Access: A Roadmap to the New Transport Landscape. By David M. Levinson and Kevin J. Krizek.
The End of Traffic and the Future of Access: A Roadmap to the New Transport Landscape. By David M. Levinson and Kevin J. Krizek.

This is one scheme for thinking about the effects of new technologies on travel demand (which we will introduce in the . How these vectors net out is a problem that could be solved with analytical geometry, if only we knew their relative magnitudes. In The End of Traffic and the Future of Transport, we argue demand in the US is generally moving a bit more to the left than the right (though the last year saw sharp reductions in fuel costs and higher incomes and thus moved us more to the right than the left). But we also note that new automation technologies change the available capacity of roads through improved packing of vehicles in motion and smaller vehicles. Less demand plus more supply reduces congestion effects in the net.

Network Econometrics and Traffic Flow Analysis

Congratulations to Alireza Ermagun for successfully defending his dissertation: “Network Econometrics and Traffic Flow Analysis.” He will soon be off to do a post-doc. img_2498

 

Short-term traffic forecasting aims to predict the number of vehicles on a link during a given time slice, typically less than an hour. For decades, transportation analysts tackled the forecasting of traffic conditions, while focusing on the temporal dependency of traffic conditions in a road segment. Following the emergence of spatial analysis in traffic studies, a growing interest has aimed to embed spatial information in forecasting methods. These approaches generally take advantage of the information that many of the cars that will be on one link soon are already on the network upstream of the relevant location, and of typical patterns of flow.

While embedding the spatial component in forecasting methods acts as a catalyst, its functioning is hindered by the constraints of spatial weight matrices. The positivity of components in spatial weight matrices postulates that traffic links have a positive spatial dependency. In essence, this hypothesis is necessary to represent complementary (upstream and downstream) traffic links. For simple single facility corridors, this may be sufficient. On the flip side of the coin is the competitive nature of traffic links. This nature demonstrates that competitive links bear a significant proportion of diverted vehicles, when one of them is saturated or closed. Short-term traffic forecasting is initially confined to scrutinizing complementary links. In consequence, the competitive nature of traffic links has been overlooked in the spatial weight matrix configuration and short-term traffic forecasting.

This dissertation overcomes this challenge by introducing concepts, theories, and methods dealing with network econometrics to gain a deeper understanding of how the components are interact in a complex network. More precisely, it introduces distinctive network weight matrices, yet alike in concepts and theories, to extract the existing spatial dependency between traffic links. The network weight matrices stem from the concepts of betweenness centrality and vulnerability in network science. Their elements are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrices are tested in congested and uncongested traffic conditions in both simulation-based and real-world environments.

 

From the simulation-based viewpoint, a 3 × 3 grid network and Nguyen-Dupuis network are designed and adopted as main test networks along with several toy networks for pedagogical purposes. To simulate traffic flow, a macroscopic traffic flow model is selected due to the purpose of this dissertation, which deals with traffic flow in a link in a specific time slice and does not include the single vehicle-driver units. From the real-word viewpoint, a grid-like sub-network is selected from the Minneapolis – St. Paul highway system, which comprises 687 detectors and 295 traffic links. The traffic flow of each link is extracted in 30 seconds increments for 2015 as the most recent year.

The results of the analysis lead to a clear and unshakable conclusion that traditional spatial weight matrices are unable to capture the realistic spatial dependency between traffic links in a network. Not only do they overlook the competitive nature of traffic links, but they also ignore the role of network topology and demand configuration in measuring the spatial dependence between traffic links. Neglecting these elements is not simply information loss. It has nontrivial impacts on the outcomes of research and policy decisions. However, using the proposed network weight matrices as a substitute for traditional spatial weight matrices exhibit the capability to overcome these deficiencies. The network weight matrices are theoretically defensible in account of acknowledging traffic theory. As the elements of the network weight matrix more closely reflect the dependence structure of the traffic links, the weight matrix becomes more accurate and defensible. They capture the competitive and complementary nature of links and embed additional network dynamics such as cost of links and demand configuration.

Building on real-world data analysis, the results contribute inexorably to the conclusion that in a network comprising links in parallel and series, both negative and positive correlation showe up between links. The strength of the correlation varies by time-of- day and day-of-week. Strong negative correlations are observed in rush hours, when congestion affects travel behavior. This correlation occurs mostly in parallel links, and in far upstream links where travelers receive information about congestion (for instance from media, variable message signs, or personal observations of propagating shockwaves) and are able to switch to substitute paths. Irrespective of time-of-day and day-of-week, a strong positive correlation is observed between upstream and downstream sections. This correlation is stronger in uncongested regimes, as traffic flow passes through the consecutive links in a shorter time and there is no congestion effect to shift or stall traffic.

 

Although this dissertation tests and validates the network weight matrices in the road traffic network problem to derive the realistic spatial dependency between traffic links, they have potential for implementation in other disciplines such as geography, regional, and social network sciences. The network weight matrices have further applications not only in models of physical flow, but also in social networks for which links or nodes may be either competitive or complementary with each other.

You have seen other work we have done together, including research related to this dissertation, earlier on the blog. He has a quite a few publications with me, and he was only at Minnesota two years, (and is not counting papers with others since he’s been here) including:

 

Build new infrastructure and make foreigners pay for it.

I know what you are thinking, a Presidential nominee of a once serious political party has proposed to build a wall on the border with a foreign country, and “make them pay for it“. This sounds absurd. It is. It won’t happen.

Tokyo 2006 - 166Yet there is infrastructure we can have with no money down. We can get new infrastructure and “make” foreign countries pay for it.

I speak, of course of toll roads. Foreign countries, and private firms from foreign countries, have shown interest in building US infrastructure. Reason Foundation’s Annual Privatization Report identifies 31 large highway concessions, 20 of which were funded by tolls. The vast majority had international financial participation, and the whole amount was worth over $38 Billion.

Really, what could be better? No need to raise domestic taxes. Someone else builds a road or rail line or airport terminal or port we want and have already approved. If we break off diplomatic relations with their home country, they can’t roll up the infrastructure and take it home. If they go bankrupt (a not uncommon proposition), the facility stays and keeps operating. If we switch to flying cars, and their investment is stranded, they are on the hook for all the future losses. All we have to do is pay for the use of the public works while we use it, which we should be doing anyway.

The history of transport is very much the history of “foreign” investors building infrastructure “overseas” in developing countries. Just look at the history of US infrastructure financing in the 19th century, and you will see much international participation (e.g. the Erie Canal). The US has similarly developed infrastructure elsewhere in the developing world. If the rest of the world believes the expected rate of return in the US is higher than elsewhere, we should not disabuse them of that notion.

America has many dysfunctions, our collective inability to properly finance, price, and manage infrastructure is one I pay a lot of attention to. It’s not going to get substantially better any time soon (the next decade or so). There are many reasons for this, which have been discussed on the blog before.

Neil Stephenson wrote nearly 25 years ago in Snow Crash:

When it gets down to it — talking trade balances here — once we’ve brain-drained all our technology into other countries, once things have evened out, they’re making cars in Bolivia and microwave ovens in Tadzhikistan and selling them here — once our edge in natural resources has been made irrelevant by giant Hong Kong ships and dirigibles that can ship North Dakota all the way to New Zealand for a nickel — once the Invisible Hand has taken away all those historical inequities and smeared them out into a broad global layer of what a Pakistani brickmaker would consider to be prosperity — y’know what? There’s only four things we do better than anyone else:
music
movies
microcode (software)
high-speed pizza delivery

So if we can supply the world with software and pizza delivery (in the immortal words of Neil Stephenson), and they supply us with roads, the Law of Comparative Advantage suggests we are all better off.

All of which reminds me of the Simpson’s episode Radioactive Man, in which a movie is made in Springfield, and the locals take Hollywood for all its worth:

 

Jiminy jillickers! – We’re shutting down production.

– Yeah, well, we only have $1,000 left anyway.

Ah, there’s a $1,000 “leaving town” tax.

I hope you’re all satisfied.
You bankrupted a bunch of naive movie folks.
Folks from a Hollywood where values are different.
They weren’t thinking about the money.
They just wanted to tell a story.
A story about a radioactive man and you slick small-towners took ’em for all they were worth.

Should we give them some of their money back?

No.

 

 

Worsening highway traffic slows down paid express lanes

Rebecca Beitsch at Stateline (republished at PBS Newshour) wrote Worsening highway traffic slows down paid express lanes. I get quoted:

What Are They Paying For?
As tolls have climbed around the country, researchers have noticed an interesting phenomenon: Rather than deterring drivers, as they are supposed to, higher prices tend to attract them.

David Levinson, a researcher at the University of Minnesota, said that when express tolls rise, many drivers take it as a sign that regular lanes are congested, rather than realizing that it means that the express lanes are especially crowded.

Levinson said people aren’t good at estimating how much time they’re saving by taking the express lane, and assume they are saving a lot because they are passing other cars. In fact, they may end up paying a high price for just a few minutes.

Even so, Levinson views the overwhelming popularity of express lanes as a positive development.

“If people are willingly paying lots of money voluntarily for this service it must be a valuable service,” he said. “That’s a success not a failure.”

The paper referred to is:

Montgomery County, Maryland Planning Documents

I worked at the Montgomery County, Maryland Planning Department, and over the years, accumulated a number of reports, which were properly public domain, but not available online in any form that I could find. I had them scanned and OCRed and then uploaded them to archive.org, which is a great institution.

While old planning documents may not stir the heart of everyone, this is good collection from an important agency that once did cutting edge work. It is also far from complete.  So have at it:

  1. Comprehensive Growth Policy Study (1989) (4 Volumes)
  2. General Plan Refinement Goals and Objectives (1992) (3 reports)
  3. Transitway HOV Network Master Plan (5 reports) (1995)
  4. Environmental Guidelines (1993)
  5. 1994 Census Update (1995)
  6. Glenmont Sector Plan Issues Report (1994)
  7. WMATA Development Related Ridership Study II (1989)
  8. Annual Growth Policy 1973-2000 (75 reports)
    1. Collection of Annual Growth Policy and Related Reports from Montgomery County Planning Department – Maryland National Capital Parks and Planning Commission, Montgomery County Executive, and Montgomery County Council (1973 – 2000).
      1973_URBAN GROWTH POLICY POPULATION, HOUSING, AND EMPLOYMENT ISSUES.pdf
      1974_COUNTY GROWTH POLICY, DIRECTIONS FOR GROWTH POLICY.pdf
      1974_COUNTY GROWTH POLICY.pdf
      1974_Final report of the Advisory Committee on County Growth Policy MONTGOMERY COUNTY PLANNING BOARD.pdf
      1975_ANNUAL GROWTH POLICY APPENDIX, FISCAL IMPACT ANALYSIS.pdf
      1975_ANNUAL GROWTH POLICY, FISCAL IMPACT ANALYSIS-SEQUEL NO. 1 ENVIRONMENT  TRANSPORTATION.pdf
      1975_ANNUAL GROWTH POLICY, FISCAL IMPACT ANALYSIS.pdf
      1976_ANNUAL GROWTH POLICY, FORECAST PEOPLE JOBS  HOUSING.pdf
      1977_ANNUAL GROWTH POLICY, CARRYING CAPACITY  ADEQUATE PUBLIC FACILITIES.pdf
      1978_GROWTH POLICY REPORT, STAGING-TRANSPORTATION.pdf
      1979_ANNUAL GROWTH POLICY, PLANNING, STAGING  REGULATING.pdf
      1979_TECHNICAL SUPPLEMENT TO THE FIFTH GROWTH POLICY REPORT.pdf
      1980_ANNUAL GROWTH POLICY, LAND SUPPLY  DEMAND.pdf
      1980_COMPREHENSIVE STAGING PLAN.pdf
      1981_REPORT ON COMPREHENSIVE PLANNING POLICIES.pdf
      1982_ADEQUATE PUBLIC FACILITIES ORDINANCE.pdf
      1982_REPORT ON COMPREHENSIVE PLANNING POLICIES.pdf
      1983_REPORT ON COMPREHENSIVE PLANNING POLICIES.pdf
      1984_REPORT ON COMPREHENSIVE PLANNING POLICIES, STAFF DRAFT.pdf
      1986_SHORT TERM TRAFFIC ALLEVIATION POLICY.pdf
      1988_ANNUAL GROWTH POLICY, COUNTY EXECUTIVE RECOMMENDED.pdf
      1988_ANNUAL GROWTH POLICY, MONTGOMERY COUNTY PLANNING BOARD.pdf
      1988_GENERAL PLAN ASSESSMENT STUDY.pdf
      1988_THE REPORT OF THE COMMISSION ON THE FUTURE OF MONTGOMERY COUNTY, MARYLAND.pdf
      1989_ALTERNATIVE TRANSPORTATION SCENARIOS AND STAGING CEILINGS-A.pdf
      1989_ANNUAL GROWTH POLICY, COUNTY EXECUTIVE RECOMMENDED.pdf
      1989_STATUS OF COUNCIL WORKSESSIONS ON FY 90 AGP.pdf
      1990_ANNUAL GROWTH POLICY, ADOPTED BY MONTGOMERY COUNTY COUNCIL.pdf
      1990_ANNUAL GROWTH POLICY, COUNTY EXECUTIVES RECOMMENDED.pdf
      1990_ANNUAL GROWTH POLICY, MONTGOMERY COUNTY PLANNING BOARD.pdf
      1990_POLICY AREA RESTRUCTURING.pdf
      1990_RECOMMENDATIONS ON 91-96CIP FROM MNCPPC.pdf
      1990_REPORT OF THE GROWTH ASSESSMENT TASK FORCE.pdf
      1991_ACTION AGENDA.pdf
      1991_ADEQUATE PUBLIC FACILITIES STUDY.pdf
      1991_ANNUAL GROWTH POLICY, COUNTY EXECUTIVES RECOMMENDED.pdf
      1991_ANNUAL GROWTH POLICY, MONTGOMERY COUNTY COUNCIL.pdf
      1991_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1991_ANNUAL GROWTH REPORT, FINAL DRAFT.pdf
      1992_ANNUAL GROWTH POLICY, COUNTY EXECUTIVES RECOMMENDED.pdf
      1992_ANNUAL GROWTH POLICY, FINAL DRAFT.pdf
      1992_ANNUAL GROWTH POLICY, MONTGOMERY COUNTY COUNCIL.pdf
      1992_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1992_GERMANTOWN TOWN CENTER TRANSPORTATION STAGING ANALYSIS.pdf
      1992_OPP RECOMMENDED ANNUAL GROWTH POLICY.pdf
      1992_POLICY LEVEL REPORT.pdf
      1993_ANNUAL GROWTH POLICY, FINAL DRAFT.pdf
      1993_ANNUAL GROWTH POLICY, INCLUDES GUIDELINES FOR ADMINISTRATION OF THE ADEQUATE PUBLIC FACILITIES ORDINANCE.pdf
      1993_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1993_NORTH BETHESDA-GARRETT PARK MASTER PLAN, STAGING AMENDMENT TO 1992 MASTER PLAN.pdf
      1994_ANNUAL GROWTH POLICY AMMENDMENT, FINAL DRAFT.pdf
      1994_ANNUAL GROWTH POLICY, FINAL DRAFT.pdf
      1994_ANNUAL GROWTH POLICY, GROWTH CAPACITY CEILINGS FY94.pdf
      1994_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1994_RESOLUTION TO ADOPT STAGING AMENDMENT TO NORTH BETHESDA-GARRETT PARK MASTER PLAN.pdf
      1994-1995_ANNUAL GROWTH POLICY, THE COUNTY EXECUTIVES RECOMMENDATIONS.pdf
      1995 ANNUAL GROWTH POLICY.pdf
      1995_ANNUAL GROWTH POLICY, FINAL DRAFT.pdf
      1995_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1995_ANNUAL GROWTH POLICY.pdf
      1995-1997_ANNUAL GROWTH POLICY_STAFF DRAFT.pdf
      1996_ANNUAL GROWTH POLICY, FINAL DRAFT.pdf
      1997-1999_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1998_ANNUAL GROWTH POLICY, AMMENDED BY THE COUNTY COUNCIL.pdf
      1998_ANNUAL GROWTH POLICY, CEILING ELEMENT.pdf
      1999_ANNUAL GROWTH POLICY, STAFF DRAFT.pdf
      1999_TRANSPORTATION POLICY REPORT, STAFF DRAFT.pdf
      1999_TRANSPORTATION POLICY REPORT.pdf
      1999-2001_ANNUAL GROWTH POLICY, FINAL DRAFT.pdf
      2000_ANNUAL GROWTH POLICY, GUIDELINES FOR THE ADMINISTRATION OF ADEQUATE PUBLIC FACILITIES.pdf
      APF LEGAL CHALLENGE, SCHNEIDER V. MNCPPC AND KETTLER V. MNCPPC.pdf

Public Transit, Active Travel, and the Journey to School: A Cross-nested Logit Analysis

Recently published:

Abstract

Apart from the active travel modes, public transit is an opportunity to accomplish a imageportion of the recommended daily physical activity. Much of the previous research has been limited to descriptive analysis for quantifying the active component of public transit by the complexity associated with advanced econometrics models. This paper overcomes this challenge by applying a two-level cross-nested logit model. We used the school trip information of 3,441 middle and high school students in Tehran for this analysis. We showed a 1% increase in home-to-school distance reduces physical activity by 0.91%. Building on the traditional models, which consider public transit a solely non-active mode, this reduction equals 2.21%. Therefore, ignoring the “quasi-active” role of public transit overestimates the physical activity reduction of students by 142%. We also found a 1% decrease in access to transit stations diminishes physical activity by 0.04%. This diminish is 0.02% when we used the traditional nested logit model. This is the direct consequence of the model misspecification stemming from ignoring the active component of public transit trips.

“Transit Makes you Short”: On Health Impact Assessment of Transportation and the Built Environment

How the p-value and coefficient changes with observation size
How the p-value and coefficient changes with observation size

Recently published:

Abstract

This study conducts an in-depth analysis to alert policymakers and practitioners to erroneous results in the positive impacts of transit use on health measures. We explore the correlation of transit use and accessibility by transit and walking with self-reported general health, Body Mass Index (BMI), and height. We develop a series of linear regression and binary logit models. We also depict the coefficient-p-value-sample-size chart, and conduct the effect size analysis to scrutinize the practically significant impacts of transit use and accessibility on health measures. The results indicate transit use and accessibility by transit and walking are significantly associated with general health and BMI. However, they are practically insignificant, and the power of the large sample in our particular case causes the statistically insignificant variable to become significant. At a deeper level, a 1% increase in transit use at the county level diminishes the BMI by only 0.0037% on average. The elasticity of transit use also demonstrates that every 1% increase in transit use would escalate the chance of having excellent or very good general health by 0.0003%. We show there is a thin line between false positive and true negative results. We alert both researchers and practitioners to the dangerous pitfalls deriving from the power of large samples and the weakness of p-values. Building the results on just statistical significance and sign of the parameter of interest is worthless, unless the magnitude of effect size is carefully quantified post analysis.