Cohort Effects and Their Influence on Car Ownership

Recent working paper

Proportion of population with a driver's license by age and cohort (1990, 2000, 2010).
Proportion of population with a driver’s license by age and cohort (1990, 2000, 2010).

Recent trends in the United States suggest a movement toward saturation of vehicle ownership. This paper examines this trend through an analysis of car ownership in the Minneapolis- St. Paul, Minnesota (USA) metropolitan region. Data from pooled cross-sectional household surveys are used to calibrate a model of car ownership that includes birth cohort effects to capture unobserved variations in preference toward car ownership across generations. Declines in household size and worker status have significant impacts in limiting the growth of car ownership, but they are also coupled by an apparent softening of preferences toward ownership among young adults.

Keywords: car ownership; cohort; generational effect; aging; income; saturation; United States

Physical Activity in School Travel: A Cross-Nested Logit Approach

Recent working paper

The tree decision for a two-level cross-nested logit model
The tree decision for a two-level cross-nested logit model

This paper considers school access by both active (walk, bike), quasi-active (walk to transit) and non-active modes (car) in a two-level cross-nested logit framework. A sample of 3,272 middle and high school students was collected in Tehran. The results of the cross-nested logit model suggest that for people who choose walking, increasing a 1 percent in home-to-school distance reduces the probability of walking by 3.51 percent. While, this reduction is equal to 2.82 and 2.27 percent as per the multinomial and nested logit models, respectively. This is a direct consequence of the model specification that results in underestimating the effect of distance by 1.24 percent. It is also worth mentioning that, a one percent increase in home-to-school distance diminishes the probability of taking public transit by 1.04 among public transit users, while increases the probability of shifting to public transit from walking by 1.39 percent. Further, a one percent increase of the distance to public transport, decreases the probability of students’ physical activity, approximately, 0.04 percent.

Keywords: Public Transit; Active Mode of Travel; School Trips; Tehran

Intra-household Bargaining for School Trip Accompaniment of Children: A Group Decision Approach with Altruism

Recent working paper

The share of travel mode in each escorting group
The share of travel mode in each escorting group

This paper tests a group decision-making model with altruism to examine the school travel behavior of schoolchildren aged between 6 and 18 years in the Minneapolis-St. Paul metropolitan area. The school trip information of 1,737 two-parent families with a schoolchild is extracted from Travel Behavior Inventory data collected by the Metropolitan Council between the Fall 2010 and Spring 2012. The proposed model has four distinctive characteristics compared with traditional developed models in the field of school travel behavior including: (1) considering the schoolchild explicitly in the model, (2) allowing for bargaining or negotiation within households, (3) quantifying the intra-household interaction among family members, and (4) determining the decision weight function for household members. This framework also covers a household with three members, namely, a father, a mother, and a schoolchild, while unlike other studies is not limited to dual-worker families. To test the hypotheses, we developed two models with and without the group-decision approach. Further, the models are separately developed for different age groups, namely schoolchildren aged between 6-12 and 12-18 years. This study considered at a wide range of variables such as work status of parents, age and gender of students, mode of travel, and distance to school. The findings of this study demonstrate that the elasticities of two modeling approaches are different not only in the value, but in the sign in some cases. In more than 90 percent of the cases, further, the unitary household model overestimates the results. More precisely, the elasticities of unitary household model are as large as 2 times more than that of the group-decision model in 25 percent of cases. This is a direct consequence of model misspecification that misleads both long-term and short-term policies where the intra-household bargaining and interaction is overlooked in travel behavior models.

Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle

Recently published

Fig 5. The number of speed observations on each link during the entire study period.
Fig 5. The number of speed observations on each link during the entire study period.


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 the widely applied shortest path assumption by evaluating routes followed by residents of the Minneapolis—St. Paul metropolitan area. Accurate Global Positioning System (GPS) and Geographic Information System (GIS) data were employed to reveal routes people used over an eight to thirteen week period. Most people did 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 guide future efforts in building better route choice models.

Analyzing Multiday Route Choice Behavior using GPS Data

Recent working paper

Three Layer Neural Network for Traveler Classification
Three Layer Neural Network for Traveler Classification

Understanding variability in daily behavior is one of the most important missions in travelbehavior modeling. In traditional method, in order to find the differences, respondents were asked to list the used multiday paths. The quality of results is sensitive to the accuracy of respondents’ memories. However, few empirical studies of revealed route characteristics, chosen by the travelers day-to-day, have been reported in the literature. In this study, accurate Global Position Systems (GPS) and Geographic Information System (GIS) data were employed to reveal multiday routes people used, to study multiday route choice behavior for the same origin-destination (OD) trips. Travelers are classified into three kinds based on their route types. A two-stage route choice  process is proposed. After analyzing the characteristics of different types of travelers, a neural  network was adopted to classify travelers and model route choice behavior. An empirical study  using GPS data collected in Minneapolis-St. Paul metropolitan area was carried out in the  following part. It finds that most travelers follow the same route during commute trips on successive days. The results indicate that neural network framework can classify travelers and model route choice well.

 Keywords: multiday travel behavior, day-to-day modeling, route choice behavior, GPS data, neural networks

Travel Behavior Over Time

Our long-awaited report: Travel Behavior Over Time, is now officially published.



Using detailed travel surveys (the Travel Behavior Inventory) conducted by the Metropolitan Council of the Minneapolis/Saint Paul (Twin Cities) Region in Minnesota for 1990, 2000-2001, and 2010-2011, this report conducts an analysis of changes in travel behavior over time. Specifically looking at changes in travel duration, time, use, and accessibility; telecommuting and its relationship with travel and residential choices; transit service quality and transit use; effects of age and cohort; and changes in walking and bicycling. Much has changed in this period, including the size of the region, demographics, economics, technology, driver licensing, and preferences, examining in turn the effects of investment, development, and population change on behaviors for the Minneapolis-St. Paul region as a whole and for areas within the region. While this research cannot hope to untangle all of the contributing factors, it aims to increase understanding of what did happen, with some explanation of why. This will inform transportation engineers, planners, economists, analysts, and decision makers about the prospective effects of future changes to networks, land use, and demographics while also evaluating the effects of previous network investments.

Thanks to the full team of authors David Levinson, Greg Lindsey, Yingling Fan, Jason Cao, Michael Iacono, Martin Brosnan, Andrew Guthrie, and Jessica Schoner, The MnDOT and CTS staff, and the 25 member Technical Advisory Panel.



Chapter 1 Accessibility and the Allocation of Time: Changes in Travel Behavior 1990-2010
Chapter 2 Telecommuting and its relationships with travel and residential choices: An exploration of the 2000 and 2010 regional travel surveys in the Twin Cities
Chapter 3 Transit Service Quality and Transit Use
Chapter 4 Cohort Analysis of Travel Behavior
Chapter 5 Biking and Walking Over Time
Appendix A Development of the Travel Behavior Over Time Database
Appendix B Additional Tables and Figures
Appendix C Glossary and Acronyms

Open Source Trip Generation

We have long known in the transportation planning community that the use of trip generation for local area review, and ITE’s procedure for estimating trip generation is broken in any number of ways. Shoup’s Truth in Transportation Planning is a classic critique of the problems.

While we could (and perhaps should) throw the whole kit and caboodle into recycling, in practice trip generation methods will be with us decades from now (even as traditional work, shopping and driving disappear). So there is a small academic movement to make the methods better. The most recent issue of JTLU 8(1) has a special section on Trip Generation, including several papers about how to adjust and improve ITE’s Trip Generation methods based on better data.

Part of the problem is that ITE is functionally a for-profit organization, and makes bank on selling the Trip Generation Manual and associated software (recognizing the fact that use of ITE Trip Generation rates is ensconced in law and regulation).

What has long been needed is an open source database of trip generation studies so that better fits to actual site conditions can be used in analysis. I recall in my youth some engineers in Montgomery County, Maryland trying to set something up, but this was well before the world wide web made that easy.

Fortunately that day is upon us. Mike Spack and company have set up, which is populated with open access trip generation studies (licensed under a Creative Commons license), and for which they hope to grow the data set. This is new, and I assume as it grows the data will get better and better, as will the methods for inputting and extracting data. Kudos to Mike, Nate, and others at Spack Consulting for getting this going. I look forward to seeing where this goes, as Big Data and new sensors make data collection increasingly ubiquitous.

Total time spent traveling per capita has declined 8 percent in the past decade.

with Kevin Krizek

The best source for reliable, recent, and aggregate statistics about time use for the US comes in the form of the American Time Use Survey. This data source, starting in 2003 and for every year thereafter, tallies the amount of time Americans spend in various activities, including travel by ten different purposes. Over the past decade, the amount of time spent in travel has declined six minutes: from 74.4 minutes to 68.4 minutes per day.

Travel Data from American Time Use Survey
Travel Data from American Time Use Survey

Multiday GPS Travel Behavior Data for Travel Analysis

I am pleased to report that a new FHWA-supported study: “Multiday GPS Travel Behavior Data for Travel Analysis” (PDF) is out. Our contribution is Chapter 4.0 An Empirical Study of the Deviation between Actual and Shortest-Travel-Time Paths. The report was coordinated by RSG, and the overview is below:

Introduction to Report

By Mark Bradley (RSG)

The use of GPS devices to collect trip-specific data as part of household travel surveys has increased steadily in recent years, and will likely become the main mode of travel survey data collection in the future as smartphone-based platforms for collecting travel data come into use. Compared to diary-based methods, the advantages of GPS data capture include the following:

  • The time and location of each trip end can be captured with more precision.
  • There is less potential for respondents to omit entire trips or activities from the survey.
  • The data can be used to trace the route traveled for any particular trip.
  • It becomes more cost-effective to capture multiple days of travel for each respondent.

These unique aspects of GPS data enable new types of behavioral analysis relative to those conducted with more traditional travel survey data. In particular, multiday data capture, in combination with more precise and complete travel data on each day, allows researchers to investigate day-to-day variability in travel behavior at the individual and household level. Such analyses can provide more insight into peoples’ travel patterns at a broader level, and guide future efforts in modeling and predicting travel behavior and designing transportation policies.

Large-sample, multiday GPS datasets from household travel surveys are still relatively limited in quantity, as is the expertise required to process point-by-point GPS trace data into trip-level data that can be used by most analysts. To address these issues, the US Department of Transportation and the National Renewable Energy Laboratory (NREL) have created the Transportation Secure Data Center (TSDC).[1] The TSDC allows researchers to access preprocessed data from almost one dozen different multiday GPS travel datasets from across the United States; it also allows researchers to analyze these data in a secure environment that ensures the protection of data privacy.

The two main objectives of this project are: 1) to provide new examples of the type of valuable research that can be done using multiday GPS travel survey data; and 2) to demonstrate that such research can be conducted in the TSDC research environment. Each of the following four chapters describes a research project that was funded and carried out as part of this project. The four research topics were originally specified by RSG, with input from FHWA, and then further refined by the authors during the course of their research.

In “The Effect of Day-to-Day Travel Time Variability on Auto Travel Choices,” Jennifer Dill, PhD, and Joseph Broach, PhD (candidate), of Portland State University address the important research topic of measuring the effect of auto network reliability on drivers’ choices. Using data from a 7-day vehicle-based GPS survey in the Atlanta region and a longer-duration vehicle-based GPS survey in the Seattle region, the authors identified several cases where respondents made multiple car trips between the same origin-destination (O-D) pairs during the survey period, and measured the actual experienced day-to-day travel time variation for those O-D pairs. The authors report several interesting analyses showing that such variability is related to trip and traveler characteristics, including trip purpose, distance, and household income.

In “Multiday Variation in Time Use and Destination Choice in the Bay Area Using the California Household Travel Survey,” Kate Deutsch-Burgner, PhD, of Data Perspectives Consulting, investigates day-to-day variation in the number, types, and level of dispersion (distance) of destinations visited during the specific days of a 3-day person-based GPS survey in the California Bay Area. Using the technique of latent class cluster analysis (LCCA), she is able to distinguish clearly different patterns of variability in terms of number of trips and type and dispersion of destinations. This analysis method shows promise for addressing the complexity of multiday travel data, and may become even more useful as future person-based (e.g., smartphone-based) GPS datasets include a greater number of travel days and a potentially wider variety of different patterns across the days.

In “Capturing Personal Modality Styles Using Multiday GPS Data-Findings from the San Francisco Bay Area,” Yanzhi “Ann” Xu, PhD, and Randall Guensler, PhD, of Trans/AQ, Inc., analyze the same multiday GPS dataset from the Bay Area that was used for the analysis described in the preceding chapter. In this analysis, however, the focus is on day-to-day variation in mode choice-research for which person-based, rather than vehicle-based, GPS data collection is clearly necessary. The authors were able to identify distinct groups of individuals in terms of whether they always used the same mode or used a variety of modes, and in terms of whether auto or alternative modes were used more often. They were also successful in relating these groupings to different person and household characteristics. The propensity to use multiple modes would benefit standard travel modeling methods, as someone who usually uses auto but also uses transit one or two days per week may be more likely to increase his or her transit use in response to service changes, as compared to someone who never uses transit at all.

Finally, in “An Empirical Study of the Deviation between Actual and Shortest-Travel-Time Paths,” Wenyun Tang, PhD (candidate), and David Levinson, PhD, of the University of Minnesota, use multiday person-based GPS data from the Minneapolis region Travel Behavior Inventory (TBI) to determine how often drivers use the shortest path for their home-to-work trip, and look at patterns in the deviation in travel time between the shortest path and the actual path. In terms of day-to-day variability, the authors were not able to identify many cases where respondents made the same direct home-to-work auto trip on multiple days. This outcome indicates that analyses that measure travel behavior across multiple days (rather than simply treating them as separate single days) will tend to require large sample sizes, particularly when the analysis focuses on a specific type of behavior (e.g., direct home-to-work auto trips).

The research presented in the following four chapters provides interesting findings in their own right, and insights into the types of research designs and methods that will be valuable in analyzing multiday GPS data as it becomes more ubiquitous and accessible in the future. The authors generally recognize that their methods could benefit from larger sample sizes, in terms of the number of respondents, and particularly in terms of the number of days per respondent. (For example, use of 7-day GPS data capture periods would allow analysis of patterns, including both weekdays and weekends.) The authors also note the critical importance of how the GPS trace data are processed into trip-level data, and the need for evolving practices and standards in GPS data processing. Finally, the authors describe the value of the TSDC in making these unique datasets available while providing a secure and productive research environment.