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 TripGeneration.org, 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.

Minnesotans driving less, biking and walking more A study shows Minnesotans were traveling less in 2010 than in 2000. | MnDaily

Benjamin Farniok writes in the Minnesota Daily “Minnesotans driving less, biking and walking more A study shows Minnesotans were traveling less in 2010 than in 2000.”

Minnesotans aren’t traveling as much as in past years, according to a study by University of Minnesota researchers released earlier this month.

The findings may inform decisions regarding future transportation infrastructure and policy.

Overall, Minnesotans aren’t traveling as often because of demographic and economic changes, said David Levinson, a civil engineering professor and the project’s primary investigator.

The number of trips people took each day, including biking and walking, decreased from 11.6 million in 2000 to 9.8 million in 2010.

Levinson also said that more 16- to 18-year-olds are holding off getting their driver’s license, and in total there are fewer licensed drivers per household today than in 1970.

Bicycle trips also increased 13 percent between 2000 and 2010, according to the survey, although women do not bike as much as men.

“A common argument against this kind of research is, ‘Why does it matter that women are not biking as much as men?’ … We can see, from the fact that there is a difference, that our transportation system isn’t serving people’s needs in the same way,” said Jessica Schoner, a graduate student who worked on the project.

There were a number of issues in comparing the surveys, Schoner said. For example, the 2010 study surveyed more people and also asked different questions, she said, which could have changed how the data is presented.

This is about the Travel Behavior Over Time study, which is coming out “later this year”.

The forgotten discovery of gravity models and the inefficiency of early railway networks

Andrew Odlyzko finds the earliest use of gravity models for travel demand and spatial interaction in his new working paper “The forgotten discovery of gravity models and the inefficiency of early railway networks“, moving the clock a few years earlier.

Abstract. The routes of early railways around the world were generally inefficient because the prevailing doctrine of the time called for concentrating on provision of fast service between major cities and neglect of local traffic. Modern planners rely on methods such as the “gravity models of spatial interaction,” which show the costs of such faulty assumptions. Such models were not used in the 19th century.
The first formulation of gravity models is usually attributed to Henry Carey in 1858. This paper shows that a Belgian civil engineer, Henri-Guillaume Desart, discovered them earlier, in 1846, based on the study of a unique and extensive data set on passenger travel in his country. His work was published during the great Railway Mania in Britain. Had the validity and value of this contri- bution been recognized properly, the investment losses of that gigantic bubble could have been lessened, and more efficient rail systems in Britain and many other countries would almost surely have been built. This incident shows society’s early encounter with the “Big Data” of the day and the slow diffusion of economically significant information. The methods used in the study point to ways to apply methods of modern network science to analyze information dissemination in the 19th century.

Indifference Bands for Route Switching

Recent working paper:

Di, X, Liu, H, Zhu, S, and Levinson, D. (2014) Indifference Bands for Route Switching

Frequency of Switchers and Stayers vs. Travel Time Saving Percentage
Frequency of Switchers and Stayers vs. Travel Time Saving Percentage
  • Abstract: The replacement I-35W bridge in Minneapolis saw less traffic than the original bridge though it provided substantial travel time saving for many travelers. This observation cannot be explained by the classical route choice assumption that travelers always take the shortest path. Accordingly, a boundedly rational route switching model is proposed assuming that travelers will not switch to the new bridge unless travel time saving goes beyond a threshold or “indifference band”. To validate the boundedly rational route switching assumption, route choices of 78 subjects from a GPS travel behavior study were analyzed before and after the addition of the new I-35W bridge. Indifference bands are estimated for both commuters who were previously bridge users and those who never had the experience of using the old bridge. This study offers the first empirical estimation of bounded rationality parameters from GPS data and provides guidelines for traffic assignment.
    Keywords: Route Choice, Travel Demand Modeling, Bounded Rationality, Indifference Band, GPS Study, Travel Behavior, Networks

The happy commuter: A comparison of commuter satisfaction across modes

My colleagues at McGill just published: The happy commuter: A comparison of commuter satisfaction across modes

Publication date: September 2014
Source:Transportation Research Part F: Traffic Psychology and Behaviour, Volume 26, Part A
Author(s): Evelyne St-Louis , Kevin Manaugh , Dea van Lierop , Ahmed El-Geneidy (preprint)

 

Abstract: Understanding how levels of satisfaction differ across transportation modes can be helpful to encourage the use of active as well as public modes of transportation over the use of the automobile. This study uses a large-scale travel survey to compare commuter satisfaction across six modes of transportation (walking, bicycle, automobile, bus, metro, commuter train) and investigates how the determinants of commuter satisfaction differ across modes. The framework guiding this research assumes that external and internal factors influence satisfaction: personal, social, and attitudinal variables must be considered in addition to objective trip characteristics. Using ordinary least square regression technique, we develop six mode-specific models of trip satisfaction that include the same independent variables (trip and travel characteristics, personal characteristics, and travel and mode preferences). We find that pedestrians, train commuters and cyclists are significantly more satisfied than drivers, metro and bus users. We also establish that determinants of satisfaction vary considerably by mode, with modes that are more affected by external factors generally displaying lower levels of satisfaction. Mode preference (need/desire to use other modes) affects satisfaction, particularly for transit users. Perceptions that the commute has value other than arriving at a destination significantly increases satisfaction for all modes. Findings from this study provide a better understanding of determinants of trip satisfaction to transport professionals who are interested in this topic and working on increasing satisfaction among different mode users.

Differences Between Walking and Bicycling Over Time: Implications for Performance Measurement

Recent working paper:

Schoner, J., Lindsey, G., and Levinson, D. (2014) Differences Between Walking and Bicycling Over Time:  Implications for Performance Measurement

Walking and Biking Mode Shares in summer 2001 vs. 2010
Walking and Biking Mode Shares in summer 2001 vs. 2010
  • Transportation policies and plans encourage non-motorized transportation and the establishment of performance measures to assess progress towards multi-modal system goals. Challenges in fostering walking and bicycling include the lack of data for measuring rates of walking and bicycling over time and differences in pedestrians and bicyclists and the trips they make. This paper analyzes travel behavior inventories conducted by the Metropolitan Council in the Minneapolis-St. Paul Metropolitan Area in 2001 and 2010 to illuminate differences walking and bicycling over time and illustrate the implications for performance measurement. We focus on the who, what, where, when, and why of non-motorized transportation: who pedestrians and bicyclists are, where they go and why, when they travel, and what factors are associated with the trips they make. Measured by summer mode share, walking and bicycling both increased during the decade, but the differences between the modes overshadow their similarities. Using descriptive statistics, hypothesis testing, and multinomial logistic models, we show that walkers are different than bicyclists, that walking trips are shorter and made for different purposes, that walking and bicycling trips differ seasonally, and that different factors are associated with the likelihoods of walking or bicycling. While the increase in mode share was greater for walking than bicycling, the percentage increase relative to 2001 share was greater for bicycling than walking. Both walking and bicycling remain mainly urban transportation options. Older age reduces the likelihood of biking trips more than walking trips, and biking remains gendered while walking is not. These differences call into question the common practice of treating nonmotorized transportation as a single mode. Managers can use these results to develop performance measures for tracking progress towards system goals in a way that addresses the unique and different needs of pedestrians and bicyclists.

Accessibility and the Allocation of Time: Changes in Travel Behavior 1990-2010

Recent working paper:

Brosnan, M and Levinson, D. (2014)  Accessibility and the Allocation of Time: Changes in Travel Behavior 1990-2010

Network Distance to CBD
Network Distance to CBD
  • Using detailed travel surveys conducted by the Metropolitan Council of the Minneapolis-St. Paul (Twin Cities) Region in Minnesota for 1990, 2000-2001, and 2010-2011, this paper conducts a detailed analysis of journey-to-work times, activity allocation and accessibility.  This study corroborates previous studies showing that accessibility is a significant factor in commute durations. Adjusting land use patterns to increase  the number of workers in job-rich areas and the number of jobs in labor-rich areas is a reliable way of reducing auto commute durations. The finding that accessibility and commute duration have a large affect on the amount of time spent at work shows that activity patterns are influenced by transportation and the urban environment in very impactful ways. The descriptive results of this analysis show a measurable decline in the time people spend outside of their homes as well as the amount of time people spend in travel over the past decade. Although trip distances per trip are not getting any shorter, the willingness to make those trip is declining, and as a result fewer kilometers are being traveled and less time on average is being allocated to travel.