Recently in travel behavior Category

We spend time to afford more space. We commute further to get more land. But the more time we spend traveling to our remote land, the less time we have to appreciate it.

If we work 8 hours per day, sleep 8 hours per day, the maximum daily commute would be 4 hours each way. However in that case you are driving 4 hours for a bed, and have no time to appreciate it (ignoring holidays and weekends). Where we live really doesn't matter much (aside from other family issues).

If we worked at home, we would have 0 minutes of commute, and 8 hours to enjoy our land. Where we lived would be very important (this is of course complicated by other family members work, school, etc.)

People generally choose under a 30 minute commute (one-way), leaving 7 hours a day to do other things. This includes both the appreciating the neighborhood environment and the physical structure itself.

Consider a daily time budget, which is largely locationally independent:

  • 8 hours sleeping
  • 8 hours working
  • 1.5 hours traveling (say for a worker, 60 minutes commuting and 30 minutes other travel)
  • 1 hour at other out-of-home activities
  • 3 hours in front of a screen
  • 2.5 maximum number of hours to enjoy your location.

So every 1 minute less spent traveling is 1 minute more at the margin to enjoy your location. If an extra minute spent traveling (from 90 to 91 minutes say) reduces time available to enjoy the place (from 150 minutes to 149) minutes, we have to ask if those 149 minutes at the newer place are 0.67% "better" than the 150 minutes at the older place. Maybe they are.

Spending 30 minutes more travel (e.g. 15 minutes each way) reduces time available from 150 to 120 minutes. Now we have to ask if the minutes at the new location spent are 20% "better". Maybe they are. But it is hard to expect to be 20% happier, or 20% more likely that you will be happy, from physical surroundings when so much of your life will be similar.

The data on happiness is complicated. This article by Eric Jaffe summarizes the research which claims people in small towns are happier than people in cities. How much? About 10%. That is of course not directly comparable to our 20%, since it encompasses the happiness of the whole day, not just the marginal time. But even if commuting is the least pleasant thing people do, it still might be worth it for a better environment. Happiness may also be improved by working less, though this article doesn't make a whole lot of sense, or seem terribly feasible.

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.


Travel Time Perception Errors: Causes and Consequences


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


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


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 |

Now at, my "Valentine's Week" entry: I Love My Commute :

"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)."

And notice (you cannot fail) the new (temporary) background.

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:

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.

SourceTransit Mode ShareScope
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 survey43-44%All downtown, peak hour, work trips
Minneapolis downtown transportation plan24-58%Depending on location, peak period
Metropolitan Council, TBI26.5%Entire day (avg inbound/outbound)
Metropolitan Council, TBI39%Peak period (avg inbound/outbound periods)
Metropolitan Council, TBI44%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.



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.

I got quoted last weekend in the Oregonian about peak travel: Columbia River Crossing needs $900 million from Washington and Oregon, but how to raise it remains elusive:

"David Levinson, a University of Minnesota professor who studies transportation issues, argues that the trend is long-term and is as much cultural as financial.

Teens, historically the most avid drivers, are waiting longer to get their licenses and are driving less, pushed by higher costs and also tougher rules for young drivers, stronger enforcement of drunk driving laws, even technology. Another theory: smart phones and the Internet have supplanted the car as a central platform of young people's social lives.

Cars themselves have also changed. Some don't burn a drop of gas or pay a penny in gas taxes. Others use less, due in part to tougher federal mileage standards. 'It's official government policy to drive down gas tax revenue,' Levinson said. "

Recently published:

Transportation systems are built with the intention to serve communities by providing accessibility and mobility. Yet seniors residing in these communities face different challenges compared to regular commuters. Seniors have special needs in terms of desired destinations and challenges faced due to limitations in mobility and decline of accessibility levels where they reside. In this research paper we discuss major findings from a mail-out mail-in survey conducted in Hennepin County, Minnesota to measuring met and unmet urban transportation needs of seniors. Compared to previous research this study uses primary collected data rather than relying on travel surveys, which does not measure the unmet urban transportation needs of seniors. The findings from this survey is consistent in term of measuring the existing travel behavior of seniors, which raises our confidence in the information being collected related to the unmet transportation needs of seniors. Seniors are found to be generally independent and rely mainly on auto usage to reach desired destinations at higher rates compared to the rest of the population. The majority of seniors reported although they are currently independent they do know that such independency is not permanent and they have to learn more about alternatives available to them. This study helps transportation engineers and planners in better understanding the current and future challenges that they will face with an aging population.

Jessica Schoner just received an honorable mention from APA's Transportation Planning Division for her paper (which was a class term paper (technically 2 term papers), not a thesis or dissertation!): Shifting Gears: A cross-regional analysis of bicycle facility networks and ridership. A Reviewer said: "Of all the years doing this contest this is by far the best on bicycling I've seen." If you care about network structure, or about travel behavior, or about bicycles, read it.


Recently published:

Abstract. This research aims to identify the role of network architecture in influencing individual travel behavior using travel survey data from Minneapolis-Saint Paul and Florida (Fort Lauderdale and Miami). Various measures of network structure, compiled from existing sources, are used to quantify roadway networks, and to capture the arrangement and connectivity of nodes and links in the networks and the spatial variations that exist among and within networks. The regression models show that travel behavior is correlated with network design.

Keywords: network structure, travel behavior

When you visit a small town, your hosts often meet you at the airport (or train station). When you go to a big city, they don't. Clearly this depends on your relative importance (The President will be greeted in every city), and whether you have hosts expecting you, and whether you are a regular/irregular visitor.

But for a random person, how big need a city be such that your hosts don't meet you at the airport?


Working paper:

Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. This study evaluates widely applied shortest path assumption by evaluating morning commute routes followed by residents of the Minneapolis - St. Paul metropolitan area. Accurate GPS and GIS data were employed to reveal routes people used over an eight to thirteen week period. Most people do not choose the shortest path. Using three weeks of that data, we find that current route choice set generation algorithms do not reveal the majority of paths that individuals took. Findings from this study may provide guidance for future efforts in building better route choice models.

JEL-Code: R41, R48, D63

Keywords: Transportation planning, route choice, travel behavior, link performance

Working paper:WalkingMap

  • Huang, Arthur and Levinson, David (2011) Accessibility, network structure, and consumers’ destination choice: a GIS analysis of GPS travel data.
    Anecdotal and empirical evidence has shown that road networks, destination accessibility, and travelers' choice of destination are closely related. Nevertheless, there have not been systematic investigations linking individuals' travel behavior and retail clusters at the microscopic level. Based on GPS travel data in the Twin Cities, this paper analyzes the impacts of travelers' interactions with road network structure and clustering of services at the destination on travelers' destination choice. A multinomial logit model is adopted. The results reveal that higher accessibility and diversity of services in adjacent zones of a destination are associated with greater attractiveness of a destination. Further, the diversity and accessibility of establishments in an area are often highly correlated. In terms of network structure, a destination with a more circuitous or discontinuous route dampens its appeal. Answering where and why people choose to patronize certain places, our planning, our findings shed light on the design of road networks and clusters from a travel behavior perspective.
    (working paper)

A Portfolio Theory of Route Choice


Working paper:

  • Zhu, Shanjiang and David Levinson (2010), A Portfolio Theory of Route Choice Presented at 4th International Symposium on Transportation Network Reliability, July 2010, Minneapolis, MN.

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 model is then tested with GPS data collected in metropolitan Minneapolis-St. Paul, Minnesota. Our data suggest strong correlation among link speed when analyzing morning commute trips. 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


  • Carrion-Madera, Carlos, Nebiyou Tilahun, and David Levinson (2011) Effects of Mode Shares on Mode Choice. (working paper)

  • This study considers the influence of the knowledge of existing mode shares on travelers mode choice. This contrasts with traditional mode choice models, where the main objective is to predict the overall mode shares as the aggregate of individual mode choices according to variables encompassing attributes of the modes, and characteristics of the travelers. In this study, a computer-administered adaptive stated preference survey is developed and applied to a sample of subjects selected from the University of Minnesota. The results indicate that the presence of mode shares in the mode choice model does influence the decision of travelers.

    The importance of being early

    NoofTravelers Graph
    Recently published:

  • Parthasarathi, Pavithra, Anupam Srivastava, Nikolas Geroliminis, and David Levinson (2011) The Importance of Being Early. Transportation 38(2) pp. 227-247 [doi]

    This research quantifies the relationship between the cost of earliness and lateness by empirically observing commute trips from two different sources. The first empirical analysis uses individual level travel survey data from six metropolitan regions while the second analysis uses traffic data from the Twin Cities freeway network. The analysis conducted in this research provides a method to estimate the ratio of the costs of earliness to lateness for different datasets. This can be a useful tool for traffic engineers and planners, to assist them in the development and implementation of improved control strategies for congested cities. The results also corroborates the hypothesis of earliness being less expensive than lateness and show that the finding holds steady over time and across different regions and levels.

  • From Gizmodo: Google Killed Map Traffic Estimates Because It Just Didn't Work

    If you're wondering how road traffic's gonna slow you today, don't turn to Google Maps anymore—the site's killed its estimates. Not because it wasn't popular. It turns out those road calculations didn't exactly correlate to, you know, reality.

    The Atlantic describes the discovery of perturbed Maps users, who complained to Google when they noticed the change. Its answer?

    [W]e have decided that our information systems behind this feature were not as good as they could be. Therefore, we have taken this offline and are currently working to come up with a better, more accurate solution. We are always working to bring you the best Google Maps experience with updates like these!"
    Translation: traffic didn't work. And as the Atlantic's Nicholas Jackson asks, how could Google be sucking down so much locational data from Android drivers and be botching it to the point that they pulled it down entirely? [The Atlantic]"

    A big defeat for the biggest information provider. But using in-vehicle GPS on mobile phones as a probe is coming, and will eventually get it right (approximately, if lagged). The problem of course is that traffic is dynamic, and even a 5 minute lag will be quite off if there is an incident or something non-steady state. However as a signal of whether things are normal, it probably works.


    Information provision is probably best for what an individual will not know from routine behavior—random incidents and unfamiliar territory. The qualitative conclusion that incidents and the unexpected are where the greatest gains from traveler information are to be found reinforces the results from our simulations. Those models show that a low level of probes can provide useful information by rapidly detecting incidents, whereas a much greater number is needed to provide any gains from recurring congestion.

    AP tells us: Study: Long commutes could fatigue airline pilots :

    One in five airline pilots lives at least 750 miles from work, according to a study by scientific advisers to the government, raising concerns that long commutes to airports could lead to fatigue in the cockpit.

    I hope they are not driving to the airport every day, 10 hours each way at 75 MPH, it would only leave them 4 hours for work and none for sleep.

    A new paper by Haugen et al.: Proximity, accessibility and choice: A matter of taste or condition? suggests that in Sweden, accessibility has increased between 1995 and 2005.

    Drawing on a combination of register data and travel survey data, this research explores changes in the accessibility to different amenities for the Swedish population between 1995 and 2005, as well as the reasons behind the changes: redistribution of either amenities or the population. Overall, proximity has increased concerning most of the amenities during the period. However, despite decreasing ‘potential’ distances, actual travel distances are growing longer due to, for example, an increasing selectivity in preferences. An analysis of the acces- sibility development for service amenities shows that restructuring within the service sector is the main cause of the changes, and to a lesser extent population redistribution.

    This is consistent with our results for the Twin Cities.

    Infrastructurist cites our work: New Reports: Higher Gas Prices Mean Safer Roads

    Politicians continue to search for answers to the problem of America’s rising gas prices — low as these prices remain in global terms — but many are searching in the wrong places. Redskins quarterback bustRep. Heath Shuler has proposed a 45-day federal gas tax “holiday,” as if the 18 years since the tax was last raised were not holiday enough. Sen. Mark Begich of Alaska has proposed an “expensive federal subsidy” that “makes no sense” and is “counterproductive” to economic recovery, writes Robert Puentes at The New Republic:

    It would actually reward high-income households and those that buy the most gas and do nothing for the 9.2 percent of the labor force that is unemployed or those who are retired and living on Social Security.

    Social scientists, meanwhile, continue to explore the potential benefits of higher gas prices. A new report from Canadian researchers connects higher fuel costs with reduced sprawl. A pair of recent studies from Mississippi State (via The Transportationist) link higher gas prices with safer roads.

    The first, which appeared in the Journal of Safety Research (pdf) last December, studied the relationship between gas prices and car accidents in Mississippi between 2004 and 2008. The researchers report both both short- and intermediate-term links between high prices and reduced crashes, with intermediate effects generally stronger. From a policy standpoint, the researchers conclude:

    that if decision makers wish to reduce traffic crash rates, increased gasoline taxes are a considerable option because raised gasoline prices reduce traffic crashes directly.

    In the January issue of Accident Analysis & Prevention (pdf), the same research team (give or take a couple members) studied the same time period for relationships between gas prices and drunk driving. The researchers report a connection between higher gas prices and fewer crashes caused by drunk driving, particularly one-car, property-damaging accidents. Exactly why high prices reduce less severe crashes is unclear; perhaps lighter drinkers, responding to economic changes, drink even less. Meanwhile, the researchers conclude, the effect is limited with regard to more severe crashes, perhaps because heavier drinkers are less likely under any condition to alter their behavior:

    [H]igher gasoline prices are less likely to deter heavier drinkers from drunk driving, as heavier drinkers are less likely to change driving behaviors due to gasoline price changes and may even drink more in response to economic stress.

     Modeled Behavior reports on: Altruism and discrimination in traffic:

    "A new working paper by Redzo Mujcic and Paul Frijters uses the question of ‘Who stops for whom in traffic?’ to shed light on several important and interesting issues related to when, why, and for whom we exhibit altruism. Here is how they summarize their results:
    We study social preferences in the form of altruism using data on 959 interactions between random commuters at selected traffic intersections in the city of Brisbane, Australia. By observing real decisions of individual commuters on whether to stop (give way) for others, we find evidence of (i) gender discrimination by both men and women, with women discriminating relatively more against the same sex than men, and men discriminating in favour of the opposite sex more than women; (ii) status-seeking and envy, with individuals who drive a more luxury motor vehicle having a 0.18 lower probability of receiving a kind  treatment from others of low status, however this result improves when the decision maker is  also of high status; (iii) strong peer effects, with those commuters accompanied by other  passengers being 25 percent more likely to sacrifice for others; and (iv) an age effect, with  mature-aged people eliciting a higher degree of altruism."

    Network Structure and Travel


    Congratulations to soon to be Dr. Pavithra Parthasarathi, who recently was awarded the 2011 John S Adams Award for Excellence in Transportation Research and Education, and who successfully defended her Ph.D. Thesis "Network Structure and Travel" (a draft of which is linked) on May 5, 2011. She accepted a job with the Hampton Roads Transportation Planning Organization (HRTPO) in Norfolk, VA, starting May 16th.


    Changing the design aspects of urban form is a positive approach to improving transportation. Land use and urban design strategies have been proposed to not only to bring about changes in travel behavior but as a way of providing a better quality of life to the residents. While the research on the relationship between urban form and travel behavior has been pretty extensive, there is a clear gap in the explicit consideration of the underlying transportation network, even though researchers acknowledge its importance. This dissertation aims to continue on the research interest in understanding travel behavior while explicitly accounting for the underlying transportation network structure.

    Transportation networks have an underlying structure, defined by the layout, arrangement and the connectivity of the individual network elements, namely the road segments and their intersections. The differences in network structure exist among and between networks. This dissertation argues that travelers perceive and respond to these differences in underlying network structure and complexity, resulting in differences in observed travel patterns. This hypothesized relationship between network structure and travel is analyzed in this dissertation using individual and aggregate level travel and network data from metropolitan regions across the U.S. Various measures of network structure, compiled from existing sources, are used to quantify the structure of street networks. The relation between these quantitative measures and travel is then identified using econometric models.

    The underlying principle of this research is that while the transportation network is not the only indicator of urban form and travel, an understanding of the transportation network structure will provide a good framework for understanding and designing cities. The importance of such an understanding is critical due to the long term and irreversible nature of transportation network decisions. The comprehensive analyses presented in this dissertation provide a clear understanding of the role of network design in influencing travel.

    Our studies are also picked up by the Insurance Journal ... Study: High Gas Prices Lead to Fewer Auto Accidents: ""

    As gasoline prices reach $4 a gallon throughout the nation, pain at the pump seems to have at least one silver lining for drivers and insurers.

    The rising cost of gas also drives a decline in all traffic accidents, including drunk-driving crashes, according to a new study by Mississippi State’s Social Science Research Center.

    Researcher Guangqing Chi, an assistant professor of sociology at the university, published his findings in the Journal of Safety Research and Accident Analysis and Prevention.

    Chi examined a range of factors related to driving-related accidents in the state, including age, gender and race. The study analyzed total traffic crashes between April 2004 and December 2008, comparing gas prices to traffic safety statistics.

    “The results suggest that prices have both short-term and intermediate-term effects on reducing traffic crashes,” he reports in the journal article.

    Among other points, the research also shows gas prices having a short-term impact on crashes involving younger drivers and intermediate-term impact related to older drivers and men.

    Chi said short-term impact refers to immediate effects, for example how a current month’s average gasoline prices affect the same month’s traffic crashes. Intermediate-term impact refers to effects over a one-year subsequent time period.

    While previous research linked traffic-related fatalities to gas price fluctuations, limited research has shown the effects of prices on all traffic accidents. No research previously examined the link between drunk-driving crashes and gas prices, Chi observed.

    His research also found significant connections between gas prices and a reduced frequency of alcohol-related crashes.

    Other researchers contributing to the study include SSRC director Arthur Cosby; David Levinson, an associate professor of civil engineering at the University of Minnesota; and Mohammed Quddus, a senior lecturer in transportation studies at the University of Loughborough, United Kingdom.

    At  Modeled Behavior Yet another externality of gasoline consumption Adam Ozimek picks up our piece on gas prices and drunk driving and runs with it ...

    In addition to global warming, congestion, geopolitical costs, oil spills, and health problems like asthma and allergies, we now have another externality to gasoline consumption to justify a pigouvian tax: drunk driving. A new study by Chi et al (6 co-authors!) uses data from Mississippi to show that lower gas prices are related to drunk driving related accidents. The authors claim the study is the first to examine this relationship, and is important because from a theoretical perspective the relationship could be positive or negative.

    The ways that gas could inversely relate to drunk driving are obvious: lower prices make it cheaper to drive and give people more disposable income, which means it’s less expensive go out drinking and driving and people have more money to do so. In addition, the marginal cost of driving a to a farther away bar decreases. Also, higher gas prices may cause people to shift to different modes of transportation, like walking or taking the bus, which (freakonomists aside) are less likely to result in drunk driving accident.

    A positive relationship is less obvious, but could result if gas prices increases enough that the negative wealth effect (more expensive gas makes you poorer) is severe enough that it creates economic hardship, which can lead people to drink more.On the face of it, the positive relationship seems much less likely than the negative relationship, and this is what the empirical evidence found in this study suggests. The chart below shows the indexed values of drunk driving accidents and gas prices.


    These results increase the growing gap between the nominal price of gas and the true cost of it, and strengthen the case for a pigouvian tax… not that externalities, efficiency, or empirical realities seem to matter much in the political debate on this issue.

    A caveat though: these results should not be taken as dispositive but rather suggestive. The empirical analysis is pretty simple, does not get into a really serious attempt to examine causality, and has some fairly serious omissions. For instance, the authors do not control for weather in their analysis. They mention that it would be difficult to aggregate to the monthly level, but I think average temperature would probably suffice. Second, and relatedly, they do not control for seasonality. This is pretty important in a time-series context where you are very likely to see both drunk driving and gas prices increase in the summer and decrease in the winter. Finally, and this is a more minor econometric point, they choose between a poisson and negative binomial regression models by selecting the one with the higher log-likelihood, which I do not believe is a sufficient means to determine whether there is enough overdispersion in the data to warrant the use of negative binomial over poisson. More importantly, they don’t tell us whether the use of negative binomial, or OLS for that matter, affects the results compared to poisson, which would have taken 30 seconds to determine and would tell us something about the robustness of their econometric results. Given that the relationship between prices and accidents disappears for males when the analysis is partitioned by gender, it is not hard to believe that the results are potentially not robust.

    All that said, the authors do argue that nobody has empirically examined this issue before, and the results are highly theoretically believable. In fact, I find the theory alone strong enough to conclude that a relationship is likely. At the very least when thinking about gas prices we should consider that drunk driving may be yet another cost of low gas prices, and this study should definitely be enough to prompt more research into this.

    The Technium: Easy Exotic

    Kevin Kelly on three types of travel (by which he seems to mean recreational travel or tourism, but might apply day to day): Easy Exotic:

    "You can graph the three extremes as three corners of a Travel Triangle: Relaxation, Destination, and Experience. The ideal trip would have an equal balance of all three, but most trips favor one side over the others. In my own personal travel I favor experience and destination and have almost no interest in relaxation. Your mileage may vary.

    The three extremes represent a set of overlapping qualities.

    Experience includes learning, change, difference, passions, uncertainties. A trip in this corner emphasizes encountering strange things, having your mind changed, going beyond your comfort, meeting as much otherness as you can.

    Destination includes traveling with goals and achievements in mind -- completing a long thru-hike, or journey to a mountain peak, or all the state capitals, or completing a race, to be the first, or your personal best.

    Relaxation is just that: rest, comfort, renewal, a sabbatical, a retreat from the worries and business of everyday life. It may include luxury but might be primitive or primeval.

    Jenelius Graph
    Recently published:

    Abstract: The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time on itself as well as other trips, which is important to consider for long-lasting disturbances and when assessing the value of travel information. In this paper we extend the single trip approach into a two trips chain and activity scheduling model. Preferences are represented as marginal activity utility functions that take scheduling flexibility into account. We analytically derive trip timing optimality conditions, the value of travel time and schedule adjustments in response to travel time increases. We show how the single trip models are special cases of the present model and can be generalized to a setting with trip chains and flexible scheduling. We investigate numerically how the delay cost depends on the delay duration and its distribution on different trips during the day, the accuracy of delay prediction and travel information, and the scheduling flexibility of work hours. The extension of the model framework to more complex schedules is discussed.

    Research highlights:

    • Extends single-trip modeling approach for value of reliability and delay costs.

    • Trip chain and activities model with scheduling flexibility.

    • Derives values of travel time and schedule adjustments in response to journey delay.

    • Shows single trip scheduling models are special cases.

    • Handles imperfect delay prediction, information and long-lasting disruptions.

    Keywords:Congestion; Disruption; Delay cost; Reliability; Schedule; Value of time

    Wired's Autopia:

    How Smartphones Can Improve Public Transit :

    An interesting study of commuters in Boston and San Francisco found people are more willing to ride the bus or train when they have tools to manage their commutes effectively. The study asked 18 people to surrender their cars for one week. The participants found that any autonomy lost by handing over their keys could be regained through apps providing real-time information about transit schedules, delays and shops and services along the routes.

    Though the sample size is small, the researchers dug deep into participants’ reactions. The results could have a dramatic effect on public transportation planning, and certainly will catch the attention of planners and programmers alike. By encouraging the development of apps that make commuting easier, transit agencies can drastically, and at little cost, improve the ridership experience and make riding mass transit more attractive.

    David Levinson

    Network Reliability in Practice

    Evolving Transportation Networks

    Place and Plexus

    The Transportation Experience

    Access to Destinations

    Assessing the Benefits and Costs of Intelligent Transportation Systems

    Financing Transportation Networks

    View David Levinson's profile on LinkedIn

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