Examining Walk Travel Behavior and Land Use in Utah

300 South & 300 West Protected Bicycle Intersection, Salt Lake City | Photo credit: Wasatch Front Regional Council

by Jonathan Larsen, PE, Andy Li, Ph.D., and Callie New

While interest in fostering walkable communities in Utah has grown in recent years, the longer-term trend is showing that people are walking less. In the greater Salt Lake City region, there was a drop in the percent of walk trips from 9 percent to 7 percent from 1993 to 2012 (11, 10). This trend signals a need to implement strategies to support non-auto dependent travel behavior and to sculpt walkable environments; understanding the factors that support these goals is salient. This article describes an analysis of the factors that impact walking. The principal data source for this analysis was the 2012 Utah Household Travel Survey (UTHS).

About the study

This study is unique as it utilizes travel data that is specific to Utah to test whether or not land use principles that may be more prevalently found in dense areas such as San Francisco are impactful to walk behavior in areas with lower densities and more homogenous and separated land uses. This study utilizes data from northern Utah, which, similar to comparative studies conducted in San Francisco, has a strong mix of both suburban and urban areas (7). The Wasatch Front Regional Council (WFRC), the Metropolitan Planning Organization (MPO) for the Salt Lake City-West Valley and Ogden-Layton urbanized areas, is the public agency responsible for long-range regional transportation planning in the rapidly developing urban area of northern Utah. This study may be of use to other mid-sized American cities experiencing similar travel behavior and land use trends as seen in the Wasatch Front region.

The following questions guided the research:

  • What factors influence walk trip-making decisions?
  • What factors influence the frequency of walk trips?
  • What factors  influence the length of walk trips?

To answer these questions, this analysis utilized two analysis types for predicting the three dependent variables. A zero-inflated binomial regression model was used for trip choice (Model 1) and walk frequency (Model 2) while walk distance (Model 3) was predicted using a linear regression model. (Please inquire with authors for further information related to equations, data processing, and study design.) 

Several authors have tested the significance between travel behavior and factors related to the household and natural and built environments. The most applicable to this analysis was conducted by Kockelman (7), which utilized an ordinary least-squares and logit model to understand the relationship between travel behavior and accessibility, land use mix and balance in the San Francisco Bay Area. Many of the variables tested in San Francisco were applied to this study of the Wasatch Front.

Research generally agrees that pedestrian safety amenities such as sidewalks and crossing signals, certain destinations that are accessible via foot such as banks or restaurants, and high levels of transit service encourage pedestrian mode choice. These and additional variables are discussed below and grouped into the following categories: the built environment, the natural environment, and household characteristics.  (While data regarding variables related to perceptions and attitudes were collected through the 2012 Household Travel Survey, these were not included in this study. This analysis aimed to analyze the relationship between the built and natural environment while taking into account personal characteristics such as household structure; variables related to perceptions and attitudes will be used for future analyses.)

Built Environment

The influence of the built environment on travel behavior has been heavily researched (3, 7, 8). The built environment categories include land use, building concentration, and urban design.

Data related to the built environment were provided from several sources, including street data from Utah’s Automated Geographic Reference Center (2015), bike lane infrastructure from County GIS departments (2015), parcel data from County Assessor’s offices (2013), employment data and firm locations from Utah’s Department of Workforce Services (2013), and residential locations from the 2012 Household Travel Survey, U.S. Census (2010) and County Assessor’s offices (2013).

These elements have come to be categorized as the “Ds” – or:

  • Diversity
  • Density
  • Design
  • Distance
  • Destination access

Each “D” contains quantifiable variables that were used in this study (3, 7, 8).

Diversity is the variety and proportional balance of land uses within an area (3, 7, 8). Variables such as jobs-housing balance and land use mix (quantified as a proportion) are typically used to measure diversity. The jobs-housing balance identifies the quantity of workforce population opportunities to local households, which may reduce the overall commuting distance to work and/or increase one’s propensity to walk. Similarly, a high land use mix score implies that an area has a varied number of destinations within close proximity and may encourage pedestrian travel and/or trip chaining without the use of an automobile to make each individual trip.

  • Diversity was measured as the jobs-housing balance and processed by calculating the total jobs per household of Traffic Analysis Zones (TAZ) within a 0-, 1-, 2-, 3-, 4-, and 5-mile radius around the centroid of TAZ. 
  • The mix of uses was measured by an entropy score, as developed by Lawrence Frank et al (4, 5). The entropy score was measured at the TAZ level and accounts for the proportion of uses (square feet) within the unit of analysis. Scores range from 0, indicating homogeneous use, to 1, indicating equal mixes of categories present in the equation.
  • Employment density was measured as an aggregate of total jobs and also separated by sector (retail, restaurant, and hospitality, office, etc.) located within a 0.5, 1-, 2-, 3-, 4- and 5-mile radius of a Traffic Analysis Zone (TAZ) centroid.
  • Population density was measured using the same unit of analysis as employment.

Design is measured through elements such as street connectivity, street directness, and block size are of (2). Short blocks and four-way intersections allow for more efficient, safe and frequent street crossings and have been found to encourage walk trips. Neighborhoods with short blocks and densely spaced intersections could have a psychological effect on travelers, as good street connectivity makes things appear to be closer by foot, while also suggesting that densely spaced intersections could lead to traffic delays when traveling by car.

Street design variables such as the presence of sidewalks and bicycle infrastructure have been found to influence non-motorized travel behavior (2), as they provide a safe barrier between the traveler and motorized vehicles.

Similarly, neighborhood traffic volumes and speeds have been used to explain some of the crash incidents for residential areas as well as used as a factor related to perceived pedestrian and bicycle safety (1).

  • Street connectivity was measured by intersection density per acre
  • Presence of bike lane
  • Presence of sidewalk was measured as density per acre at the TAZ level
  • Average arterial traffic volumes and speeds were processed within TAZs

While measuring the impact of site design, interesting architecture and/or landscaping on non-motorized behavior would prove valuable and was considered for the analysis, this variable is not easily quantifiable at a regional scale and perhaps would be most useful in comparing the travel behavior of individual sites. This variable was therefore excluded from the analysis.

Distance and destination access are used to measure the overall nexus between land use and the transportation system. The ability of residents to reach jobs or destinations in a reasonable amount of time can be thought of as the basic purpose of transportation and may be seen as the main impetus of utilitarian travel.  Proximity to transit service is also considered to encourage pedestrian travel behavior, both in terms of accessing stations and overall trip mode choice (7, 8).

  • This variable was processed as the total aggregate jobs or sector-specific jobs that a household can reach via the street network within 5, 10, 15 or 20 minutes by auto. While the study analyzed walk behavior, this variable can be seen as a proxy for the efficiency of the street network, and one’s ability to reach a certain destination within a reasonable amount of time.
  • Transit accessibility was measured as the total jobs or sector-specific jobs that a household can reach through riding on the transit network within 5, 10, 15 or 20 minutes.

400 South & TRAX Red Line, Salt Lake City | Photo credit: Wasatch Front Regional Council

Natural Environment

Research recognizes the impact of the natural environment – or topography, weather patterns, temperature, and climate – on walk propensity (2, 8). Research suggests that while these variables might have an impact on travel behavior during a specific extreme climatic event, these occurrences do not have a sustained impact, and normal behavior will return when typical weather conditions resume (8). Topographic conditions are believed to influence travel behavior in areas with varied terrain; however, this may be more of a factor related to route choice rather than mode choice.

  • The Wasatch Front has varied topography and hosts neighborhoods that are both flat and steep; therefore, slope data was provided via Utah’s Automated Geographic Reference Center and measured as an average per TAZ.
  • Weather patterns were not included in this analysis due to the previously referenced study outcomes.
View of Wasatch Range | Photo credit: Wasatch Front Regional Council

View of Wasatch Range | Photo credit: Wasatch Front Regional Council

Household Characteristics

Also relevant to the discussion are a number of sociodemographic characteristics, which attempt to analyze if and how certain characteristics are associated with certain travel behavior (6). Understanding these patterns assists in evaluating both the estimated demand for non-motorized facilities as well as the type of factors that are significant within specific populations. Examples of this include age, race/ethnicity, sex, number of non-working adults, number of school-aged children, income and vehicle or bike ownership.

It is difficult to separate these characteristics and examine their influence individually because travel behavior is often a combination of many factors. For instance, while utilitarian walk trips have been seen to decline with an individual’s rise in income, this behavior might better be described by trends in American suburbanization, where people have tended to settle outward as their wealth increases, in areas with more homogenous uses where opportunities to reach destinations by foot are less frequent and/or active transportation infrastructure is less prevalent. Therefore, it is important to consider these relationships carefully, and in relation to other factors of the built and natural environment.

All household characteristics data were drawn from the 2012 UHTS. Variables included were:

  • Household size (1, 2, 3…10, 11+)
  • Number of adults in household (1, 2, 3, 4, 5, 6+)
  • Number of children in household (1, 2, 3…8, 9+)
  • Household annual income (ten categories)
  • Household life cycle (Household with no children or retirees, Household with children but no retirees, Household with retirees)
  • Number of adult workers in household (0, 1, 2, 3+)
  • Number of motorized vehicles in household (0, 1, 2, 3, 4, 5+)
  • Number of adult bikes
  • Number of youth bikes

Study findings

The results obtained provide valuable insight into the underlying relationship between household characteristics, land use, and walk behavior, and are presented individually for each model.

Model 1: Trip Choice

Model 1 examined the factors that influence whether people choose to walk versus all other means of transportation.

The following variables are positively correlated with walk behavior:

  • A strong jobs/housing mix
  • High intersection density
  • Larger households
  • Households with working adults and no children
  • Bicycle ownership

These results infer that living in a neighborhood with a variety of destinations close by and a mix of homes, jobs, and services, along with a well-connected street network, will result in people choosing to walk for at least some of their trips.

The only variable in this analysis which correlates to a reduction of walk trips is vehicle ownership. That is, as vehicle ownership increases, the likelihood of people choosing to walk reduces.

Grant Ave & 20th Street, Ogden | Photo credit: Wasatch Front Regional Council

Grant Ave & 20th Street, Ogden | Photo credit: Wasatch Front Regional Council

Model 2: Trip Frequency

Model 2 examined factors that influence the frequency of walk trips. The following are some observations based on the results of this analysis:

  • The presence of children in a household correlates positively with walking trips.
  • The number of workers in household is negatively correlated with walk trips. It is possible that busier schedules leave less time for walking.
  • The number of vehicles in a household has the strongest negative correlation, implying that auto ownership is a disincentive to walking.
  • The number of manufacturing jobs within a five-minute automobile trip is negatively correlated, likely due to manufacturing jobs not traditionally located in walkable areas.
  • The number of retail jobs within a TAZ has a negative correlation with walk trip frequency. This may be due to the fact that most of the retail establishments in the region are auto-oriented big-box and strip mall shopping centers which are buffered with large parking lots and not designed for pedestrian access. The desire to carry and transport goods can also be a deterrent for walking to certain retail establishments.
  • The number of restaurant/hospitality and government jobs within a TAZ accounts for a positive correlation with walk trip frequency, again signaling that a greater number of nearby destinations encourage pedestrian activity.

Model 3: Walk Distance

Model 3 examined factors that influence the length of walk trips. A negative correlation to walk trip distance means a shorter trip distance. The following variables resulted in shorter – and therefore likely more desirable – walk trips.

  • The presence of a strong jobs/housing balance, population density, and land use mix within a one-mile radius all correlate negatively with walk trip distance, suggesting that the presence of a variety of destinations close by results in more frequent, but shorter walk trips.
  • Population density within a two-mile radius has a positive correlation with trip distance, while the population density within a one-mile radius has a negative correlation. This is perhaps related to recreational trips occurring at the fringes of the urbanized area.
  • The number of government/education and healthcare jobs within a one-mile radius has a positive correlation with trip distance. This finding suggests that one will walk substantial distance, at least one mile, in order to access necessary services. This finding is reiterated when compared with the negative correlation associated with population density within the same distance, meaning people will walk further to services than to other residential areas.

Based on this analysis, the environments that foster walkability in Northern Utah are those that are denser, have good street connectivity and contain land uses that support destination access.

Utility of analysis and next steps

Through this research, the Wasatch Front Regional Council has become more informed in making land use and street design recommendations that improve the use, safety, and viability of our pedestrian transportation system. Ultimately, this will enhance the agency’s ability to make recommendations regarding the planning and programming of active transportation investments at a regional scale and support local planning processes.

Understanding the factors of the built environment that contribute to creating walkable built environments and an increase in walk behavior, such as strong street connectivity, a mix of land uses, and access to destinations are valuable takeaways from this research.


Jonathan Larsen, PE, is the manager of the Modeling, Forecasting, and Information Services group at WFRC. This group is in charge of model development and application, data management, and geographic information services (GIS). This work provides the technical base for nearly all of the work performed by WFRC. Prior to joining WFRC, Jon worked as a consultant where he managed projects and performed travel demand modeling, traffic engineering, and transportation planning.

Andy Li, Ph.D.,  has worked at Wasatch Front Regional Council for the last thirteen years. Prior to joining WFRC Andy completed a Ph.D. from New Jersey Institute of Technology. Andy develops, calibrates, and implements regional land use and transportation models. His expertise includes land use modeling and simulation, travel behavior analysis, travel demand forecasting, and transportation and land use integration.

Callie New is a Transportation Planner for the Wasatch Front Regional Council (WFRC), where she works on the Regional Transportation Plan, spatial analysis related to accessibility, and technical support for the Transportation Land Use Connection program. Prior to joining WFRC, Callie obtained a M.S. in Urban Planning from Columbia University, where she studied planning implications of rapid growth and decline. Callie's favorite research topics involve examining the nexus between the built environment and pedestrian travel behavior.


REFERENCES

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Published February 2017

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