Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

How Do People Choose a Travel Mode? Factors Associated with Routine Walking & Bicycling

627 views

Published on

Robert Schneider, UC Berkeley

  • Writing good research paper is quite easy and very difficult simultaneously. It depends on the individual skill set also. You can get help from research paper writing. Check out, please ⇒ www.HelpWriting.net ⇐
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • The 3 Secrets To Your Bulimia Recovery ▲▲▲ http://ishbv.com/bulimiarec/pdf
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Being in recovery can be very stressful, when there is difficulty, you can come on here, post how you feel, or read the stories of other people that have gone through exactly the same thing and get tips & drive to push through. It also keeps you on track with recovery so you dont get overwhelmed or feel alone. There are others going through the same thing and feeling the same way ●●● http://t.cn/A6Pq6OB6
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • I recovered from bulimia. You can too! learn more... ♥♥♥ http://ishbv.com/bulimiarec/pdf
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

How Do People Choose a Travel Mode? Factors Associated with Routine Walking & Bicycling

  1. 1. How Do People Choose a Travel Mode? Factors Associated with Routine Walking & Bicycling Robert Schneider, Ph.D. Candidate UC Berkeley Department of City and Regional Planning May 2011
  2. 2. Outline • Background & research question • Core quantitative & qualitative research • Contributions to the planning field
  3. 3. US Communities with Complete Streets Policies (Data Source: Complete Streets Coalition, January 2011)
  4. 4. 30% 25% 20% 15% 10% 5% 0% Pedestrian & Bicycle Mode Shares in Selected Countries Sources: Bassett, Pucher, Buehler, Thompson, and Crouter (2008); NHTS (2009) Walk Bicycle Source: Basset et al. “Walking, Cycling, and Obesity Rates in Europe, North America, and Australia(2008)
  5. 5. U.S. Shopping Trip Mode Share (2009) (Home-Based Shopping Trips) Source: Federal Highway Administration, National Household Travel Survey, 2009. Hayward Pedestrian 8% Bicycle 1% Transit 2% Automobile 89%
  6. 6. How do you travel to the store? Mission Street, SF San Carlos
  7. 7. Influences on Walking & Bicycling Carrying Things No Sidewalks, No Bike Lanes Physical limitations Social Interaction Bad Weather Exercise Inexpensive Fun Easy Altruistic Work Responsibilities Takes too Long Too Far Family Responsibilities Traveling with Other People Crime Risk Crash Risk Habit No Emissions Fast Traffic Too Much Traffic
  8. 8. Research Question What factors are associated with walking and bicycling for routine travel? Mission Street, SF
  9. 9. Core Research Components 1) Factors associated with walking and bicycling on routine tours 2) Characteristics of shopping districts that encourage walking rather than driving 3) Theory of mode choice decision process
  10. 10. Conceptual Framework for Data Collection & Analysis
  11. 11. Travel Characteristics (distance, time, trip-chain, bags) Mode Choice Walk Bicycle Transit Automobile Socioeconomic Characteristics (gender, age, income) Attitude Characteristics (enjoy walking, environmentalist) Perception Characteristics (crash risk, crime risk) Shopping District Characteristics (density, mix, facilities, parking)
  12. 12. Methodology
  13. 13. Study Area: San Francisco Bay Area • 20 shopping districts • Cluster analysis identified four types of shopping districts – Urban Core (3) – Suburban Main Street (8) – Suburban Thoroughfare (7) – Suburban Shopping Center (2)
  14. 14. Retail Pharmacy Store Customer Survey • Intercept survey • 20 stores in SF Bay Area – Same national chain – Different neighborhoods – ~50 customers per store • Weekday: 4-6 p.m. • Saturday: afternoon • 8/29/09 to 12/9/09 Daly City
  15. 15. Survey Instrument (front side)
  16. 16. Survey Instrument (back side)
  17. 17. Survey Participants • 4,585 customers invited to participate • 1,003 survey participants (22%) • Participant characteristics – 59% female – 31% age 18-34, 13% age 65+ – 73% shopped alone, 7% in groups of 3 or more – 9% took Spanish-language survey Mission Street, SF
  18. 18. Important Travel Definitions • Routine: Travel involving stopping at activity locations (e.g., not purely exercise/recreation) • Trip: A movement between a pair of activity locations, or stops (e.g., store to office). • Stage: A movement made using a single mode of transportation. All trips have at least one stage. • Tour (Trip-Chain): The set of all trips that a person makes from leaving home until returning home.
  19. 19. Example: Tour Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  20. 20. Example: Trips Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  21. 21. Example: Stages Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  22. 22. Example Tour: San Mateo shopping district
  23. 23. Example Tour: San Mateo shopping district
  24. 24. Example Tour: San Mateo shopping district
  25. 25. Example Tour: San Mateo shopping district Home Store
  26. 26. Example Tour: San Mateo shopping district Google Earth Image Home Store
  27. 27. Example Tour: San Mateo shopping district Google Earth Image Home Store
  28. 28. Example Tour: San Mateo shopping district Google Earth Image Home Store
  29. 29. Example Tour: San Mateo shopping district Google Earth Image Home Store
  30. 30. Example Tour: San Mateo shopping district Google Earth Image Home Store
  31. 31. Example Tour: San Mateo shopping district Google Earth Image Home Store
  32. 32. Example Tour: San Mateo shopping district Google Earth Image Home Store
  33. 33. Example Tour: San Mateo shopping district Google Earth Image Home Store
  34. 34. Example Tour: San Mateo shopping district Google Earth Image Home Store
  35. 35. Example Tour: San Mateo shopping district Google Earth Image Home Store
  36. 36. Example Tour: San Mateo shopping district Google Earth Image Home Store
  37. 37. Example Tour: San Mateo shopping district Google Earth Image Home Store
  38. 38. Example Tour: San Mateo shopping district
  39. 39. Primary Tour Mode (N=959) Pedestrian 21.3% Bicycle 2.2% Transit 9.9% Automobile 66.6%
  40. 40. Primary Tour Mode Share by Shopping District Mode Pedestrian Bicycle Transit Automobile Pop. Density (2000)
  41. 41. 1) Factors associated with walking & bicycling on shopping district tours Market Street, SF
  42. 42. Potential Explanatory Variables • Travel factors (11) • Socioeconomic factors (21) • Attitude & perception factors (10) • Shopping district factors (26) Base Map Source: Google Maps, 2010
  43. 43. The utility of a respondent choosing each mode (i = 1, 2, 3) to travel to and from a particular store was expressed in the following equations: 1 = 1 + 1 1 + 1
  44. 44. 1 + 12 + 1 (1) 2 = 2 + 2 2 + 2
  45. 45. 2 + 12 + 2 (2) 3 = 3 + 3 3 + 3
  46. 46. 3 + 3 (3) Mixed logit model: Utility equations
  47. 47. The utility of a respondent choosing each mode (i = 1, 2, 3) to travel to and from a particular store was expressed in the following equations: 1 = 1 + 1 1 + 1
  48. 48. 1 + 12 + 1 (1) 2 = 2 + 2 2 + 2
  49. 49. 2 + 12 + 2 (2) 3 = 3 + 3 3 + 3
  50. 50. 3 + 3 (3) Mixed logit model: Utility equations Mode 1 (Walk) Mode 2 (Transit) Mode 3 (Auto)
  51. 51. The utility of a respondent choosing each mode (i = 1, 2, 3) to travel to and from a particular store was expressed in the following equations: 1 = 1 + 1 1 + 1
  52. 52. 1 + 12 + 1 (1) 2 = 2 + 2 2 + 2
  53. 53. 2 + 12 + 2 (2) 3 = 3 + 3 3 + 3
  54. 54. 3 + 3 (3) Mixed logit model: Utility equations Explanatory variables and associated parameters
  55. 55. The utility of a respondent choosing each mode (i = 1, 2, 3) to travel to and from a particular store was expressed in the following equations: 1 = 1 + 1 1 + 1
  56. 56. 1 + 12 + 1 (1) 2 = 2 + 2 2 + 2
  57. 57. 2 + 12 + 2 (2) 3 = 3 + 3 3 + 3
  58. 58. 3 + 3 (3) Mixed logit model: Utility equations Unobserved correlated error between people who took survey at same store
  59. 59. The utility of a respondent choosing each mode (i = 1, 2, 3) to travel to and from a particular store was expressed in the following equations: 1 = 1 + 1 1 + 1
  60. 60. 1 + 12 + 1 (1) 2 = 2 + 2 2 + 2
  61. 61. 2 + 12 + 2 (2) 3 = 3 + 3 3 + 3
  62. 62. 3 + 3 (3) Mixed logit model: Utility equations Correlated error between choice of Mode 1 Mode 2 (Mode 1 2 Nest)
  63. 63. Mixed Logit Model (N = 388) (Walk, Transit, Automobile)
  64. 64. Mixed logit model results: Factors associated with walking on tours to and from shopping districts (N = 388) Mode Choice Walk Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) number of stops***, no bags* (-) distance in shop. dist.*, time*** Socioeconomic (+) group house***, Spanish-speaker*, student***, low-income*** (-) physical disability*
  65. 65. Mixed logit model results: Factors associated with walking on tours to and from shopping districts (N = 388) Mode Choice Walk Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) number of stops***, no bags* (-) distance in shop. dist.*, time*** Socioeconomic (+) group house***, Spanish-speaker*, student***, low-income*** (-) physical disability* Attitude (+) enjoy walking* Perception (+) perceive crash risk***
  66. 66. Mixed logit model results: Factors associated with walking on tours to and from shopping districts (N = 388) Mode Choice Walk Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) number of stops***, no bags* (-) distance in shop. dist.*, time*** Socioeconomic (+) group house***, Spanish-speaker*, student***, low-income*** (-) physical disability* Attitude (+) enjoy walking* Perception (+) perceive crash risk***
  67. 67. Mixed logit model results: Factors associated with walking on tours to and from shopping districts (N = 388) Mode Choice Walk Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) number of stops***, no bags* (-) distance in shop. dist.*, time*** Socioeconomic (+) group house***, Spanish-speaker*, student***, low-income*** (-) physical disability* Attitude (+) enjoy walking* Perception (+) perceive crash risk*** Shopping District (+) population density*, employment density***, tree canopy*** (-) survey store parking spaces***
  68. 68. Travel (+) number of stops***, no bags* (-) distance in shop. dist.*, time*** Mode Choice Walk Transit Automobile Socioeconomic (+) group house***, Spanish-speaker*, student***, low-income*** (-) physical disability* Attitude (+) enjoy walking* Perception (+) perceive crash risk*** Shopping District (+) population density*, employment density***, tree canopy*** (-) survey store parking spaces*** Mixed logit model results: Factors associated with walking on tours to and from shopping districts (N = 388) Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20)
  69. 69. Mixed Logit Model (N = 959) (Walk, Bike, Transit, Automobile)
  70. 70. Mixed logit model results: Factors associated with bicycling on tours to and from shopping districts (N = 959) Mode Choice Walk Bicycle Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (-) distance***, Saturday*** Socioeconomic (+) Spanish-speaker*, student***, no auto***, no children* (-) female***, physical disability*
  71. 71. Mixed logit model results: Factors associated with bicycling on tours to and from shopping districts (N = 959) Mode Choice Walk Bicycle Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) 1) All bicycle respondents enjoyed bicycling. Travel (-) distance***, Saturday*** Socioeconomic (+) Spanish-speaker*, student***, no auto***, no children* (-) female***, physical disability* Attitude (+) enjoy bicycling1 Perception (-) perceive crime risk*
  72. 72. Mixed logit model results: Factors associated with bicycling on tours to and from shopping districts (N = 959) Mode Choice Walk Bicycle Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) 1) All bicycle respondents enjoyed bicycling. Travel (-) distance***, Saturday*** Socioeconomic (+) Spanish-speaker*, student***, no auto***, no children* (-) female***, physical disability* Attitude (+) enjoy bicycling1 Perception (-) perceive crime risk*
  73. 73. Mixed logit model results: Factors associated with bicycling on tours to and from shopping districts (N = 959) Mode Choice Walk Bicycle Transit Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) 1) All bicycle respondents enjoyed bicycling. Travel (-) distance***, Saturday*** Socioeconomic (+) Spanish-speaker*, student***, no auto***, no children* (-) female***, physical disability* Attitude (+) enjoy bicycling1 Perception (-) perceive crime risk* Shopping District (+) bicycle facilities**, bike parking spaces *, employment density***, metered parking* (-) survey store parking spaces***
  74. 74. Travel (-) distance***, Saturday*** Mode Choice Walk Bicycle Transit Automobile Socioeconomic (+) Spanish-speaker*, student***, no auto***, no children* (-) female***, physical disability* Attitude (+) enjoy bicycling1 Perception (-) perceive crime risk* Shopping District (+) bicycle facilities**, bike parking spaces *, employment density***, metered parking* (-) survey store parking spaces*** Mixed logit model results: Factors associated with bicycling on tours to and from shopping districts (N = 959) Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) 1) All bicycle respondents enjoyed bicycling.
  75. 75. 2) Walk vs. Drive within store area? San Mateo
  76. 76. Mixed Logit Model (N = 286) (Walk, Automobile)
  77. 77. Travel (+) shopping alone* (-) time***, 2+ bags* Socioeconomic (-) physical disability Mixed logit model results: Factors associated with walking within shopping districts (N = 286) Mode Choice Walk vs. Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20)
  78. 78. Attitude Perception Mixed logit model results: Factors associated with walking within shopping districts (N = 286) Mode Choice Walk vs. Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) shopping alone* (-) time***, 2+ bags* Socioeconomic (-) physical disability
  79. 79. Attitude Perception Mixed logit model results: Factors associated with walking within shopping districts (N = 286) Mode Choice Walk vs. Automobile Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) shopping alone* (-) time***, 2+ bags* Socioeconomic (-) physical disability
  80. 80. Mode Choice Walk vs. Automobile Attitude Perception Mixed logit model results: Factors associated with walking within shopping districts (N = 286) Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) shopping alone* (-) time***, 2+ bags* Socioeconomic (-) physical disability Shopping District (+) multi-store shopping complex***, metered street parking* (-) driveway crossings***, commercial road speed limit*
  81. 81. Mode Choice Walk vs. Automobile Attitude Perception Shopping District (+) multi-store shopping complex***, metered street parking* (-) driveway crossings***, commercial road speed limit* Mixed logit model results: Factors associated with walking within shopping districts (N = 286) Statistical significance: *** (p 0.05) ** (0.05 p 0.10) * (0.10 p 0.20) Travel (+) shopping alone* (-) time***, 2+ bags* Socioeconomic (-) physical disability
  82. 82. 3) Mode Choice Decision Process El Cerrito
  83. 83. In-Depth Interviews • 26 of the survey participants • Spring and Summer 2010 • Conducted by phone • 30 to 60 minutes
  84. 84. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits)
  85. 85. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Socioeconomic Factors (Explain differences in how people respond to each step)
  86. 86. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Socioeconomic Factors (Explain differences in how people respond to each step)
  87. 87. Quotes: Awareness Availability • “He rides his bike because the cost of gas and he’s an environmental major…his attitude really did change when he became aware.” --Female, Age 52, San Carlos • “Working people that are driving…don’t have the mind to think, ‘Am I doing things right?’ You are just surviving.” --Male, Age 30-39, Berkeley • “So if one person starts cycling, and everyone starts seeing it, everyone will start cycling.” --Male, Age 40-49, Pleasanton • “I am unemployed and can’t afford to buy a bicycle.” --Female, Age 20-29, San Francisco Market Street
  88. 88. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Socioeconomic Factors (Explain differences in how people respond to each step)
  89. 89. Quotes: Basic Safety Security • “I can’t walk there because of the cars that are speeding on Bayshore…and it really bothers me because it’s the one little green open space that I could walk to…within 500 yards of my house, but I can’t get there because of the traffic.” --Female, Age 40-49, San Francisco Third St. • “If there was less traffic…I probably would walk even more.” --Male, Age 30, San Francisco Fillmore Street • “I’m not a skilled bicyclist…on the road, so I don’t really feel safe at all.” --Female, Age 30-39, Daly City • “Right now I wouldn’t bicycle. I had a neighbor who had a terrible accident on a bicycle…” --Female, Age 52, South San Francisco • “Bicycling itself…I would do it if I wasn’t right up next to cars.” --Female, Age 52, South San Francisco
  90. 90. Quotes: Basic Safety Security • “That’s how I got mugged, walking from my car to my house…I thought I might be walking more, but when I actually [moved] here, I realized that I couldn’t.” --Female, Age 40-49, San Francisco Third Street • “When you are walking in this neighborhood, there’s nobody else walking. You look like a target here.” --Female, Age 40-49, San Francisco Third Street • “We don’t live in a world that is as safe as it used to be…That’s why most parents don’t have their children biking around or walking out on the streets alone.” --Female, Age 40-49, Danville • “There’s sometimes a fear of being stranded and not being able to get back, and you are at a distance from your home…I think a car gives you a feeling of security.” --Male, Age 60-69, Brentwood
  91. 91. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Socioeconomic Factors (Explain differences in how people respond to each step)
  92. 92. 3) Convenience Cost Accessibility of Activity Locations Short distances to activities decrease and long distances to activities increase barriers to walking and bicycling: Availability and Price of Automobile Parking Limited automobile parking increases and plentiful parking decreases barriers to driving: • Planning time • Travel time • Physical effort • Packages • Bad weather • Hills • Lack of lighting • Traffic risk • Sterile streets • Planning time • Travel time (searching for spot walking from parking) • Price (limited parking is often expensive)
  93. 93. Interview Quotes about Accessibility Parking
  94. 94. Interview Quotes about Accessibility Parking The next grocery store is about 4 to 5 miles away, and I wouldn't think about walking or bicycling. --Female, Age 40-49, Pleasanton
  95. 95. “Everything for us is like almost walking distance of where we go. I never drive. --Male, Age 30, SF Fillmore Street Interview Quotes about Accessibility Parking
  96. 96. Living here in the suburbs...you get really used to parking not being an issue. Wherever you go, you can park. --Female, Age 60-69, South San Francisco Interview Quotes about Accessibility Parking
  97. 97. I travel less. Because I know coming home, there won't be parking. --Female, Age 40-49, SF Third Street Interview Quotes about Accessibility Parking
  98. 98. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Socioeconomic Factors (Explain differences in how people respond to each step)
  99. 99. Quotes: Enjoyment • “I have noticed that my stress level has gone down since I have walked and bussed more than I drive.” --Male, Age 30, Burlingame • “It’s a beautiful block with beautiful trees, and I love walking down that street. I wish every street had trees.” --Female, Age 40-49, San Francisco Third Street • People bicycle “for exercise, for convenience, and for fun.” --Female, Age 20-29, San Francisco Market Street • “[Bicycling is] a good way to get some exercise, and it’s less pollution…part of it may be that it’s kind of trendy.” --Male, Age 30, San Francisco Fillmore Street
  100. 100. Theory of Routine Mode Choice Decisions 5) Habit Pedestrian, Bicycle, Transit, or Automobile? (People who choose a particular mode regularly are more likely to consider it as an option in the future) 1) Awareness Availability (People must be aware of the mode and have it available as an option to travel to an activity) Situational Tradeoffs 2) Basic Safety Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Socioeconomic Factors (Explain differences in how people respond to each step)
  101. 101. Quotes: Habit • “I think that getting into the habit of [walking and bicycling] early makes one I think more likely to continue doing them into their later years.” --Male, Age 55, San Francisco Third Street • “I’m used to using a car. It’s easy. I can get in; I can park in my driveway at night. I get in, I go.” --Male, Age 55, San Francisco Third Street • “The younger ones—a lot of them drive their cars to high school or to college…That’s probably all they know, really.” --Male, Age 30, Burlingame • “In the United States actually, we tend to think about the car being the first and the only mode of transportation, and we need to get out of that mindset.” --Female, Age 40-49, Pleasanton
  102. 102. Strategies to Increase Walking Bicycling Pedestrian, Bicycle, Transit, or Automobile? 1) Awareness Availability (Institute individualized marketing programs, bicycle give-away programs, community-wide education campaigns, Bike to Work Day, Walk to School Week, and other encouragement programs) 5) Habit (Offer information to people who move change job locations; Explore roadway and parking pricing strategies) 2) Basic Safety Security (Construct sidewalks and bicycle paths; Improve pedestrian crossings; Designate roadway space for bicycles; Design roadways for slower automobile speeds; Educate pedestrians, bicyclists, and drivers on safe behaviors; Enforce traffic laws; Improve roadway lighting; Provide secure bicycle parking ) 3) Convenience Cost (Allow higher population and employment densities and a finer mix of land uses; Reduce building setbacks; Reduce automobile lanes; Reduce off-street parking and provide market-rate on-street parking) 4) Enjoyment (Plant street trees and landscaping; Zone for ground-level stores adjacent to sidewalks; Design public streets for slow-speed activities; Promote environmental social benefits of walking and bicycling)
  103. 103. 1) Awareness Availability Bike to Work Week; Bike Month Walk to School Day Photo Credit: City of Rockville, MD Bicycle Give-Aways Photo Credit: Jennifer Toole City of Portland, OR Individual Marketing
  104. 104. 1) Awareness Availability Wayfinding Signs; Walk Bicycle Maps Photo Credit: City of Seattle
  105. 105. 2) Basic Safety Security Designated Space for Bicycling Safer Street Intersection Facilities Pedestrian Crossing Safety Treatments
  106. 106. 2) Basic Safety Security Credit: DC, VA, MD Street Smart Driver, Pedestrian, Bicyclist Education
  107. 107. 2) Basic Safety Security Photo Credit: Ron Bloomquist, pedbikeimages.org Photo Credit: Dan Burden Enforcement of Crime Better Lighting at Night Traffic Violations
  108. 108. 3) Convenience Cost Photo Credit: Bing Maps Decrease distances to activities by changing land uses
  109. 109. 3) Convenience Cost Photo Credit: Bing Maps Decrease distances to activities by changing land uses
  110. 110. 3) Convenience Cost Photo Credit: Bing Maps Decrease distances to activities by changing land uses
  111. 111. 3) Convenience Cost
  112. 112. 3) Convenience Cost Provide bicycle parking
  113. 113. 3) Convenience Cost Provide bicycle parking Limit and price automobile parking
  114. 114. 4) Enjoyment Plant street trees Photo Credit: Cynthia Cluck Advertise social environmental Provide comfortable facilities benefits
  115. 115. 5) Habit Price changes; Home work changes Photo Credit: Associated Press Influence from family Safe Routes to School programs close friends
  116. 116. Survey Support for the Theory Relationship within Mode Choice Decision Process Supported? 1) Awareness Availability Bike available More likely to consider bicycle Yes*** Auto available More likely to consider automobile Yes*** 5) Habit Typically walkMore likely to walk Yes*** Typically bike More likely to bike Yes*** Difference between binomial proportions : *** indicates p 0.05; ** indicates 0.05 p 0.10; * indicates 0.10 p 0.20; (Yes) = Relationship is found in survey data, but not statistically-significant.
  117. 117. Four Contributions 1) Controlled for attitudes, perceptions, and local environment factors within model structure 2) Identified local environment characteristics associated with walking and bicycling 3) Documented analyzed short pedestrian trips 4) Developed theory to explain pedestrian bicycle mode choice process
  118. 118. Four Contributions 1) Controlled for attitudes, perceptions, and local environment factors within model structure 2) Identified local environment characteristics associated with walking and bicycling 3) Documented analyzed short pedestrian trips 4) Developed theory to explain pedestrian bicycle mode choice process
  119. 119. Travel Characteristics (distance, time, trip-chain, bags) Mode Choice Walk Bicycle Transit Automobile Socioeconomic Characteristics (gender, age, income)
  120. 120. Travel Characteristics (distance, time, trip-chain, bags) Mode Choice Walk Bicycle Transit Automobile Socioeconomic Characteristics (gender, age, income) Shopping District Characteristics (density, mix, facilities, parking)
  121. 121. Travel Characteristics (distance, time, trip-chain, bags) Mode Choice Walk Bicycle Transit Automobile Socioeconomic Characteristics (gender, age, income) Attitude Characteristics (enjoy walking, environmentalist) Perception Characteristics (crash risk, crime risk) Shopping District Characteristics (density, mix, facilities, parking)
  122. 122. Mode Share Forecast (N = 388) 70% 60% 50% 40% 30% 20% 10% 0% Walk Auto Less Multimodal Base More Multimodal Mode Share for Tours to and from Shopping Districts 1) Less Street Tree Canopy 2) Larger Parking Lots 3) Lower Population Employment Density 1) More Street Tree Canopy 2) Smaller Parking Lots 3) Higher Population Employment Denisty Transit
  123. 123. Mode Share Forecast (N = 286) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Walk Auto Less Multimodal Base More Multimodal Mode Share for Trips Within Shopping District 1) Free Street Parking 2) Faster Speed Limit 3) Separated Stores 4) More Driveway Crossings 1) Metered Street Parking 2) Slower Speed Limit 3) Stores w/ Shared Parking 4) Fewer Driveway Crossings
  124. 124. Mode Share Forecast (N = 959) 80% 70% 60% 50% 40% 30% 20% 10% 0% Auto Walk Transit Less Multimodal Base More Multimodal Mode Share for Trips Within Shopping District 1) Lower Population Employment Density 2) Larger Parking Lots 3) No Street Bicycle Facilities 4) No Bicycle Parking 5) Free Street Auto Parking 1) Higher Population Employment Density 2) Smaller Parking Lots 3) New Street Bicycle Facilities 4) New Bicycle Parking 5) Metered Auto Parking Bicycle
  125. 125. Four Contributions 1) Controlled for attitudes, perceptions, and local environment factors within model structure 2) Identified local environment characteristics associated with walking and bicycling 3) Documented analyzed short pedestrian trips 4) Developed theory to explain pedestrian bicycle mode choice process
  126. 126. Significant Local Environment Characteristics in Models Associated with Walking Associated with Bicycling Population Density (+) Employment Density (+) Employment Density (+) Tree Canopy (+) Multi-Store Shopping Complex (+) Metered Street Parking (+) Survey Store Parking Spaces (-) Driveway Crossings (-) Speed Limit (-) Bicycle Facilities (+) Bicycle Parking (+) Metered Street Parking (+) Survey Store Parking Spaces (-) Perceived Crime (-) Perceived Crime (-)
  127. 127. Significant Local Environment Characteristics in Models Associated with Walking Associated with Bicycling Population Density (+) Employment Density (+) Employment Density (+) Tree Canopy (+) Multi-Store Shopping Complex (+) Metered Street Parking (+) Survey Store Parking Spaces (-) Driveway Crossings (-) Speed Limit (-) Bicycle Facilities (+) Bicycle Parking (+) Metered Street Parking (+) Survey Store Parking Spaces (-) Perceived Crime (-) Perceived Crime (-)
  128. 128. Four Contributions 1) Controlled for attitudes, perceptions, and local environment factors within model structure 2) Identified local environment characteristics associated with walking and bicycling 3) Documented analyzed short pedestrian trips 4) Developed theory to explain pedestrian bicycle mode choice process
  129. 129. Mode Share Measures for All Survey Respondents
  130. 130. Mode Share Measures for All Survey Respondents
  131. 131. Respondent Pedestrian Path Density Urban Core Shopping District (Market Street, San Francisco)
  132. 132. Respondent Pedestrian Path Density Urban Core Shopping District (Mission Street, San Francisco)
  133. 133. Respondent Pedestrian Path Density Suburban Main Street Shopping District (Burlingame)
  134. 134. Respondent Pedestrian Path Density Suburban Main Street Shopping District (Richmond)
  135. 135. Respondent Pedestrian Path Density Suburban Thoroughfare Shopping District (Brentwood)
  136. 136. Respondent Pedestrian Path Density Suburban Thoroughfare Shopping District (El Cerrito)
  137. 137. Four Contributions 1) Controlled for attitudes, perceptions, and local environment factors within model structure 2) Identified local environment characteristics associated with walking and bicycling 3) Documented analyzed short pedestrian trips 4) Developed theory to explain pedestrian bicycle mode choice process
  138. 138. Theory of Routine Mode Choice Decisions Pedestrian, Bicycle, Transit, or Automobile? Socioeconomic Factors 5) Habit (People are more aware of the mode when they use it; People are less likely to consider modes that they do not use) 1) Awareness Availability (People are aware of the mode and have it available as an option to travel to an activity) 2) Basic Safety Security (People perceive that the mode will provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience Cost (People calculate that the mode will get them to an activity in an acceptable amount of time, cost, and effort) 4) Enjoyment (People derive satisfaction from their own physical, mental, or emotional response to the mode and/or its benefit to society) (Explain differences in how people respond to each step) Situational Tradeoffs
  139. 139. Important Considerations • Small bicycle sample size • Fair weather conditions only • Tour purpose not controlled in secondary model (N=959) • Exploratory-level statistical significance • Cross-sectional data • Mode choice theory based on San Francisco Bay Area subjects and applies to routine travel
  140. 140. Thank you • United States Environmental Protection Agency • University of California Transportation Center • Advisors: Robert Cervero, Elizabeth Deakin, Elizabeth Macdonald, Joan Walker • Research assistants: Carlos Velasquez Melissa Chinchilla • Walgreens Corporation: Bill Hose, Michael O’Brien
  141. 141. Questions?
  142. 142. Walgreens is Everywhere
  143. 143. Previous discrete choice modeling studies with pedestrian bicycle modes • Purvis (1997) • Bowman Ben-Akiva (2000) • Jonnalagadda et al. (2001) • Berrigan Troiano (2002) • Cervero Duncan (2003) • Walton Sunseri (2007) • Ryley (2008) • Kim Ulfarsson (2008) • Handy et al. (2010)
  144. 144. Value of Time (N=388) • Analysis of tours that had all stops within shopping district (½-mile of store) • Included travel time and out-of-pocket cost in model instead of tour distance, metered parking, zero vehicles bus pass • Value of time – Automobile tours = $14.41/hour – Public transit tours = $9.23/hour – BATS 1990 shopping trips in 2009 dollars = $10.90/hour
  145. 145. Bicycle Facility Tradeoffs (N=959) • Analysis of all respondent tours (not just to and from shopping district) • Value of bicycle facilities – Each additional mile of bicycle facilities in the shopping district was associated with respondents bicycling 1.6 miles further – Each additional bicycle parking space at the survey store was associated with respondents bicycling 0.5 miles further
  146. 146. Automobile Facility Tradeoffs (N=286) • Analysis of walk vs. drive within shopping districts • Value of automobile facilities – Shopping districts with metered parking were associated with respondents walking 2.5 minutes longer – 10 fewer commercial driveway crossings along the main shopping district roadway were associated with respondents walking 1.0 minutes longer
  147. 147. Auto Mode Share Under Different Scenarios 60% 55% 50% 45% 40% Double Parking Lot Automobile Spaces Less Multimodal Base More Multimodal Automobile Mode Share for Sample of Survey Respondents Double Population Employment Density Half Population Employment Density Half Parking Lot Automobile Spaces Half Street Tree Canopy Double Street Tree Canopy N = 388
  148. 148. Walk Mode Share Under Different Scenarios 55% 50% 45% 40% 35% Less Multimodal Base More Multimodal Walk Mode Share for Sample of Survey Respondents Double Population Employment Density Half Population Employment Density Double Parking Lot Automobile Spaces Half Parking Lot Automobile Spaces Half Street Tree Canopy Double Street Tree Canopy N = 388
  149. 149. Transit Mode Share Under Different Scenarios 20% 15% 10% Double Parking Lot Automobile Spaces 5% 0% Less Multimodal Base More Multimodal Transit Mode Share for Sample of Survey Respondents Double Population Employment Density Half Population Employment Density Half Parking Lot Automobile Spaces Half Street Tree Canopy Double Street Tree Canopy N = 388
  150. 150. Attitudes Perceptions Related to Walking Primary Tour Mode Pedestrian Bicycle Transit Automobile
  151. 151. Attitudes Perceptions Related to Bicycling Primary Tour Mode Pedestrian Bicycle Transit Automobile
  152. 152. Respondents who Perceived High Bicycle Crash Risk 33% 36% 22% 15% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Urban Core Suburban Main Street Suburban Thoroughfare Suburban Shopping Center Survey Respondents who Perceived Bicycling in Shopping District to have High Crash Risk
  153. 153. Respondents who Considered Walking or Bicycling to Store 80% 62% 35% 49% 12% 15% 21% 27% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Urban Core Suburban Main Street Suburban Thoroughfare Suburban Shopping Center Survey Respondents who Consiered Walking or Bicycling to Survey Store Considered Walking Considered Bicycling
  154. 154. Respondents who Enjoyed Walking or Bicycling 94% 87% 84% 84% 64% 63% 58% 55% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Urban Core Suburban Main Street Suburban Thoroughfare Suburban Shopping Center Survey Respondents who Reported Enjoying Walking or Bicycling Enjoyed Walking Enjoyed Bicycling

×