Travel behavior study shows drivers are spending less time traveling and more time at home

CTS Catalyst reports on our research: Travel behavior study shows drivers are spending less time traveling and more time at home

Something unprecedented has happened to Americans’ travel patterns. Even before the recent recession, total distance traveled per person had started to decline, and the rate of total vehicle travel had begun to steadily decrease as well.

In a new five-part series of research reports sponsored by the Minnesota Department of Transportation and the Metropolitan Council, U of M researchers are delving into a set of rich data encompassing more than four decades of travel behavior surveys to enable the region’s transportation planners to better understand how its residents make decisions about whether, when, where, and why to travel.

In the first study, researchers examined how changes in the accessibility of destinations—such as jobs, shopping, and leisure activities—have changed travel behavior in the past 20 years.

“We started with a detailed analysis of travel surveys conducted by the Metropolitan Council in 1990, 2000, and 2010,” says David Levinson, the study’s principal investigator and RP Braun/CTS Chair in the Department of Civil, Environmental, and Geo- Engineering. “We found that people are spending slightly less time in motion and more time at home. We also found that accessibility is a significant factor in determining not only travel behavior but overall time budgeting in general. In short, each person has to decide how they will use the time allotted to them each day, and many of those decisions are directly related to the transportation and land-use systems in place.”

A deeper look into the data sheds additional light on the relationship between accessibility and travel behavior. For example, trip durations for workers have gone up for all activities between 1990 and 2010. More noticeably, distances for trips have increased markedly: workers take jobs farther from their homes and shop farther from their homes. Travel speeds also increased for the average worker, due to more travel on faster suburban roadways that carry a larger share of all travel. In contrast, for non-workers, trip durations and overall travel time have gone down.

“Interestingly, although time, distance, and speed per trip has generally risen for workers, the number of those trips is declining,” Levinson says. “As a result, overall, fewer miles are being traveled and less time is being allocated to travel.”

Total time spent shopping also decreased for workers and for males, likely caused in part by an increase in online commerce. “The Internet has provided electronic accessibility, much as the transportation network has in the material world,” Levinson explains. “It helps to facilitate commerce, communication, education, and leisure. This may lead to a decreased need for people to travel, and account for more time spent at home.”

Jonathan Ehrlich, planning analyst with the Metropolitan Council, says the research “helps us get more value from our travel surveys and will aid in understanding how travel is changing, and what the risks are in the assumptions and models we use for planning and forecasting.”

The findings will prove useful not just for Twin Cities transportation planners but for planners and engineers worldwide. “Our models can be easily adapted to data from other cities or for other activities besides work,” Levinson says. “This creates an approach that can be used to gauge the impact of a transportation project from an accessibility standpoint and determine how that project will translate into time allocation.”

Other parts of the study will look at changes in telecommuting behavior over time, the effect of transit quality of service on people’s activity choices and time allocation, changes in travel behavior by age cohort, and analysis of bicycling and walking in light of land-use and transportation system changes. The Catalyst will feature coverage of these projects as they are completed.


Related Links

Travel Time Perception Errors: Causes and Consequences

Congratulations to soon to be Dr. Carlos Carrion (shown in the center of the picture, between alums Nebiyou Tilahun and Pavithra Parthasarthi), who recently defended his Ph.D. Thesis “Travel Time Perception Errors: Causes and Consequences” (a draft of which is linked). He is working as a post-doctoral researcher at MIT/SMART in Singapore.
1600T

Travel Time Perception Errors: Causes and Consequences
Abstract:
This research investigates the causes, and consequences behind travel time perception. Travel times are experienced. Thus, travelers estimate the travel time through their own perception. This is the underlying reason behind the mismatch between travel times as reported by a traveler (subjective travel time distribution) and travel times as measured from a device (e.g. loop detector or GPS navigation device; objective travel time distribution) in collected data. It is reasonable that the relationship between subjective travel times and objective travel times may be expressed mathematically as: Ts = To + ξ. Ts is a random variable associated with the probability density given by the subjective travel time distribution. To is a random variable associated with the probability density given by the objective travel time distribution. The variable ξ is the random perception error also associated with its own probability density. Thus, it is clear that travelers may overestimate or underestimate the measured travel times, and this is likely to influence their decisions unless E(ξ) = 0, and Var(ξ) ≈ 0. In other words, travelers are “optimizing” (i.e. executing decisions) according to their own divergent views of the objective travel time distribution.
This dissertation contributes novel results to the following areas of transportation research: travel time perception; valuation of travel time; and route choice modeling. This study presents a systematic identification of factors that lead to perception errors of travel time. In addition, the factors are related to similar factors on time perception research in psychology. These factors are included in econometric models to study their influence on travel time perception, and also identify which of these factors lead to overestimation or underestimation of travel times. These econometric models are estimated on data collected from commuters recruited from a previous research study in the Minneapolis-St. Paul region (Carrion and Levinson, 2012a, Zhu, 2010). The data (surveys, and Global Positioning System [GPS] points) consists of work trips (from home to work, and from work to home) of subjects. For these work trips, the subjects’ self-reported travel times, and the subjects’ travel times measured by GPS devices were collected. Furthermore, this dissertation provides the first empirical results that highlight the influence of perception errors in the valuation of travel time, and in the dynamic behavior of travelers’ route choices. Last but not least important, this dissertation presents the most comprehensive literature review of the value of travel time reliability written to date.

A Portfolio Theory of Route Choice

PortfolioTheory

Recently published:

Although many individual route choice models have been proposed to incorporate travel time variability as a decision factor, they are typically still deterministic in the sense that the optimal strategy requires choosing one particular route that maximizes utility. In contrast, this study introduces an individual route choice model where choosing a portfolio of routes instead of a single route is the best strategy for a rational traveler who cares about both journey time and lateness when facing stochastic network conditions. The proposed model is compared with UE and SUE models and the difference in both behavioral foundation and model characteristics is highlighted. A numerical example is introduced to demonstrate how such model can be used in traffic assignment problem. The model is then tested with GPS data collected in metropolitan Minneapolis–St. Paul, Minnesota. Our data suggest there is no single dominant route (defined here as a route with the shortest travel time for a 15 day period) in 18% of cases when links travel times are correlated. This paper demonstrates that choosing a portfolio of routes could be the rational choice of a traveler who wants to optimize route decisions under variability.

JEL-Code: R41, R48, D63
Keywords: Transportation planning, route choice, travel behavior, link performance

Selfishness and Altruism in the Distribution of Travel Time and Income.

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Recently published:

  • Tilahun, Nebiyou, and David Levinson (2013) Selfishness and Altruism in the Distribution of Travel Time and Income. [presentation] Transportation (online first) [doi]
    Abstract: Most economic models assume that individuals act out their preferences based on self-interest alone. However, there have also been other paradigms in economics that aim to capture aspects of behavior that include fairness, reciprocity, and altruism. In this study we empirically examine preferences of travel time and income distributions with and without the respondent knowing their own position in each distribution. The data comes from a Stated Preference experiment where subjects were presented paired alternative distributions of travel time and income. The alternatives require a tradeoff between distributional concerns and the respondent’s own position. Choices also do not penalize or reward any particular choice. Overall, choices show individuals are willing forgo alternatives where they would be individually well off in the interest of distributional concerns in both the travel time and income cases. Exclusively self-interested choices are seen more in the income questions, where nearly 25 % of respondents express such preferences, than in the travel time case, where only 5 % of respondents make such choices. The results also suggest that respondents prioritize their own position differently relative to regional distributions of travel time and income. Estimated choice models show that when it comes to travel time, individuals are more concerned with societal average travel time followed by the standard deviation in the region and finally their own travel time, while in the case of income they are more concerned with their own income, followed by a desire for more variability, and finally increasing the minimum income in their region. When individuals do not know their fate after a policy change that affects regional travel time, their choices appear to be mainly motivated by risk averse behavior and aim to reduce variability in outcomes. On the other hand, in the income context, the expected value appears to drive choices. In all cases, population-wide tastes are also estimated and reported.Keywords: selfishness, altruism, travel time distribution, income distribution, preferences, inequality, choice experiment.

Welcome to Meteorological Spring

Today I saw one bus unable to get up a hill and one crash, both due to weather conditions (I got some video of the bus after its failure to climb the hill, but none of the crash, which was a minor fender-bender with some grill damage to the offending vehicle with apparently no injuries). Meetings are canceled left and right. My son’s school was canceled. My daughter’s school (a different school in the same building) was not. Welcome to Meteorological Spring.
More on Why we become such bad drivers when it snows at Streets.MN

I Love My Commute | streets.mn

Cross-posted from streets.mn, my “Valentine’s Week” entry: I Love My Commute :

I Love My Commute

This week (Valentine’s Week), we have challenged ourselves at Streets.MN to be positive, which is really much harder than complaining (you see, already I am complaining about having to not complain). So we have a theme, about which we are not entitled to be snarky, What I love about Minnesota.

Wild Turkeys

Red Fox

So what I love about Minnesota is my commute. It is 30 minutes each way from my house to my workplace at the University on foot. Most of the walk is along the river and parks. In short, it is highly walkable, and even the freeway crossings are not difficult as neither involves an interchange. On a good day I might see a bald eagle, a red fox, or a family of wild turkeys. Further, the shortest path route allows me to see the traffic on I-94 twice, so I can check on the status of bottlenecks (which I realize is a highly idiosyncratic reason to like one’s commute, but it’s a professional hazard).

MyCommuteH2W

The best thing is that Minnesota has enough congestion to keep me employed, but not so much to severely impact my personal quality of life.

The Pain of Paying

JW sends me to Dan Ariely on “The Pain of Paying”


JW writes:

Here is an interesting presentation by Dan Ariely about the pain of paying. I think there are implications for infrastructure spending. There is a tradeoff between reducing the pain of paying and creating a moral conflict, or developing morally dubious payment schemes. For example, general revenue funds are a common pool resource with all of the tragedy of the commons issues – as people try to exploit the “resource” first before it is exhausted. Tolls create a higher pain of paying than gas taxes. Motor vehicle registration fees probably fall in between. Property taxes may not be recognized as funding local roads and so the pain (and anger) may be misdirected.
Vehicle mileage taxes create a higher pain level than fuel taxes I think.

Ariely has a nice framing and discusses “saliency”. Andrew Odlyzko and I identified mental transaction costs as a related factor in:

More people are driving alone, carpooling, biking, walking, and taking transit to work. There are more people working.

Wendell Cox nicely summarizes the recent American Community Survey @ Newgeography: A Summary of 2011 Commuting Data Released Today :

“As estimated employment improved from 137.9 million in 2010 to 138.3 from 2010 to 2011, there was an increase of 800,000 in the number of commuters driving alone, which, as usual, represented the vast majority of commuting (105.6 million daily one way trips), at 76.40 percent. This was not enough, however, to avoid a small (0.17 percentage point) decline in market share.
Car pooling experienced a rare increase of 120,000 commuters, which translated into a 0.1 percentage point loss in market share, to 9.68 percent. Transit increased 190,000 commuters, and had a 0.09 percentage point increase in market share, to 5.03 percent. This brought transit’s market share to above its 2008 share of 5.01 percent and near its 1990 market share of 5.11 percent.
Working at home increased by 70,000, with a modest 0.1 percentage point increase from 2010.”

Toward transit dominance

05-1_mohring_effect

Mode choice is not generally a marginal thing. For a given market (a market here is an origin-destination (OD) pair, by time of day. [We could further break this down by purpose of trip, or socio-economic class of the traveler, but we won’t here.]), either almost everyone chooses one mode or another. Very few markets are competitive. To be competitive, the alternatives have to be perceived as having almost exactly the same travel time, frequency, reliability, and other characteristics, or the advantage in one characteristic has to be exactly offset by another. I am going to briefly describe transit use patterns.
Consider downtown Minneapolis. The table below, from Planning for Place and Plexus (chapter 5) shows estimates of work trip transit mode shares into downtown (the destination) from all origins. As can be seen, in some cases (peak hour), mode share in 2000 was 44 percent. If for all origins, the mode share was 44 percent, then for some origins it was much higher than 44 percent, and for others it was much lower than 44 percent.

 

Source Transit Mode Share Scope
Census results (2000) 25% All downtown, All day, work trips only
Cordon Count- Minneapolis plan (1995) 34% All trips, Peak Period (Survey teams at 100+ entrance points counting people entering downtown)
Employer survey (SRF Consulting, 2000 Downtown Transportation Study) 40% Work trips, peak hour
TBI survey (2001) 36-41% All downtown, peak period, work trips (5% sample of regional households)
TBI survey 43-44% All downtown, peak hour, work trips
Minneapolis downtown transportation plan 24-58% Depending on location, peak period
Metropolitan Council, TBI 26.5% Entire day (avg inbound/outbound)
Metropolitan Council, TBI 39% Peak period (avg inbound/outbound periods)
Metropolitan Council, TBI 44% Peak Hour (avg)

Downtown is one kind of market, and larger cities than Minneapolis will even have higher transit mode shares. Non-downtown is a different kind of market, with a transit mode share much closer to zero. The regional mode share for all trips in Twin Cities is estimated at 5 percent for work trips. If the destination mode share is much higher than 5 percent for downtown Minneapolis (and downtown St. Paul, and the University), then it must be lower than 5 percent for other destinations. The US national number for mode share for all trips is under 2 percent, from the 2009 NHTS (though up from 2001). The 2000 Twin Cities TBI gives us an unweighted estimate of 1.4 percent of all trips by public bus. Soon the 2011 TBI will be out, and we can update.
Theory suggests there are two equilibria because transit is a positive feedback system (and the primary competing mode, automobiles, is a negative feedback system). The more transit riders, the more revenue, the higher the rate of buses (or trains) per hour (and the better the service, as with more riders, express and other services can be offered). At high levels of ridership (relatively high mode shares), losing a few riders because of small random exogenous shock, or even a bus-full will not be noticed in the travel times (schedule delays) of the remaining riders. At medium levels of ridership, losing just enough riders to result in service cutbacks will have a noticed effect on headways and thus schedule delays, driving transit ridership down further. This is the vicious circle that has destroyed transit in most of the US. As students of systems theory know, vicious circles are just virtuous circles in reverse. An exogenous shock increasing transit use should increase supply provided, reducing waits, and thus further increasing use. We imagine this might be a sharp sudden increase in the price of fuel. This only happens if the supply system is responsive, which typically happens with free markets, but not necessarily under government management.
So in a world where people do have the ability to have an automobile, either many travelers (in a narrowly-defined market) almost always use transit, and the frequency is high (the case for selected to origins to well-served activity centers), or almost no one does (the case almost everywhere else).
05-2_feedback_new
This says to me, fixed-route transit investment should be highly, highly focused in markets (OD pairs) where it is, or can cost effectively and financially sustainably become, the dominant carrier.
The transit goal should be reframed.
Transit is not competing to double its regional mode share for all trips from 1.5 to 3 percent. It is competing to increase its mode share in specific markets from 40 percent to 60 percent to 80 percent, and to add markets where it can dominate. (Regional mode share might be a byproduct of that, but it is an improper goal). Otherwise, the service is spread out like peanut butter and does nothing well.
To be clear, we cannot put the genie back in the bottle. As a society, almost all new urban form since the 1920s has been climbing up Mt. Auto and down Mt. Transit. Every change we make to the network to make it more convenient for cars makes it less convenient for transit. Every change in land use adapted to the automobile is maladapted to an environment served by transit. It would probably take another century of concerted effort to reverse this, and there is no evidence that efforts are concerted.
Yet, there remain markets, mostly those that existed before the 1920s, where transit is competitive, and even dominant. Instead of chasing butterflies, transit systems should focus on its dominant and dominatable markets, and play to its strengths. Everyone can think of local butterflies that are diffusing rather than concentrating transit’s attention.
If, where, and when the transit service is good, it will attract transit-oriented people to organize their lives around transit services, and may encourage new people to become transit riders. It might even encourage transit-oriented development to shelter those transit-oriented people, and transit-oriented stores and businesses to serve them. It cannot do this where the service remains poor.
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Notes:
1. Depending on how precise we want to be with our definitions of origins, we can figure this out from Census data (at the block group or tract level). But we can’t know this from data at the block level. Unfortunately for analysts, there is a wide degree of variation within very small geographies, as people typically walk to transit, and walking is sensitive to relatively small distances and micro-scale factors. The Travel Behavior Inventory is too small a sample at the block level to compute block level mode shares directly, (as is the Census or American Community Survey). Models will give us estimates, and a regional planning model with 1200 transportation analysis zones and 24 time slices will estimate this number for 34,560,000 markets. In integers, most of those would be zero trip markets. In the planning model which uses real numbers, each of those markets has some probability of using transit.
2. There are insufficient observations for the Twin Cities from NHTS (apparently 11 unweighted transit users) to estimate transit mode share for the Twin Cities from the NHTS.
3. In my view, the purpose of transit is of course transportation, since other outcomes, like land development, follow from the utility of the network in providing real services.
4. In contrast to transit, where people are mostly a benefit in terms of service time, the more people who drive, the higher the travel time for all concerned (since capacity is hard to add in the short run). Driving is self-limiting (~2000 vehicles per hour per lane), transit services are limited at much higher levels of capacity (usually not reached except in the largest cities), and are usually instead limited by demand.