Austin Commuter Survey: Findings and Recommendations

1. Dr. Chandra Bhat The University of Texas at Austin Austin Commuter Survey: Findings and Recommendations Note: This presentation is in slideshow mode. Please follow the and buttons.

2. THE CONTEXT • An average Austin area rush hour commuter spends 50 hours annually just sitting in traffic and takes 30% longer to get from point A to point B. • Traffic delay per rush hour traveler has risen by 250% in the past decade in Austin • Need to design and implement bold, creative, coordinated and proactive strategies

3. Congestion alleviation strategies may be broadly grouped into the following categories: • Increase supply/vehicular carrying capacity of roadways • Influence vehicular traffic patterns • Change commuter travel patterns • Accurate analysis of the potential effectiveness of these strategies is critical • This requires examination of commuter travel behavior – commute periods being the most congested times of the weekday

4. REPORT OBJECTIVES • Examine demographic, employment and overall travel characteristics of Austin area commuters and analyze how these characteristics impact commute travel choices and perceptions • Develop a framework for evaluating the effect of alternative strategies on commute mode choice to enable policy analysis • Highlight the need to identify and implement a coordinated, balanced, multi-modal, and integrated land use-transportation plan to control traffic

5. AUSTIN COMMUTER SURVEY (ACS) • Endorsed by Clean Air Force (CAF) of Central Texas and supported by NuStats Inc. • Web-based survey hosted by UT Austin • Publicity and recruitment • CAF email messages to Austin area employers • Radio and TV media • Austin Chamber of Commerce article in newsletter • Color posters at strategic public places • Posters handed out to individuals at public locations

6. SURVEY CONTENT Screening Introduction and Travel opinions Work-related characteristics Commute travel experience by: Bus Walk Bicycle Drive Share-ride Commute and midday stop-making Stated preference games Demographic data

7. DATA PREPARATION • Geo-coded home and work locations • Overlaid geo-coded locations with CAMPO’s zonal configuration to assign appropriate zones • Appended LOS attributes to each individual’s record – extracted from CAMPO’s network skims • Ensured consistency through several cleaning and screening steps • Final sample • 699 commuters who reside and work within 3-county area of Hays, Williamson and Travis • Weighted by race, income, gender, household size, household type and commute travel mode choice

8. DEMOGRAPHIC AND SOCIO-ECONOMIC CHARACTERISTICS • Household characteristics • Individual characteristics • Demographic characteristics • Socio-economic characteristics • Work characteristics

9. COMMUTE TRAVEL CHARACTERISTICS • Travel Perceptions • Commute Distance • Nonwork stops • Commute Mode • Commute Duration • Commute Time-of-Day

10. CONCLUSIONS The “Big Picture” Findings • Increasing diversity of household structures – increasing participation in nonwork activities during commute and midday • It is important to pursue an integrated and coordinated land-use and transportation plan to address congestion problems • Addressing traffic congestion problems requires a balanced and multimodal transportation plan – infeasible to even maintain today’s congestion levels into the future by focusing on only one strategy

11. CONCLUSIONS • Need to also focus attention on modifying work arrangements as a means to alleviating congestion – currently only 2.5% of the commuters telework on any given day • Reliability of travel time plays an important role in commute mode choice decisions – particularly for commuters with an inflexible work schedule • Overall, several Austin area employees do enjoy the routine of traveling to their work place

12. CONCLUSIONS Specific Findings on Commuter Rail and Tolls • Commuters have a more positive image of a potential CRT mode than the current bus mode • Percentage of commuters using a potential CRT system will be dependent upon the service characteristics; under assumptions that are not unreasonable, a new CRT mode is predicted to capture 1.5% of overall mode share if 10% of the commuter population have access to CRT and 4.1% of overall mode share if 25% of the commuter population have access to CRT • Within the group of individuals for whom CRT is an available alternative, CRT is predicted to capture about 15% of the mode share

13. CONCLUSIONS • Tolls on highways can be expected to lead to a drop of about 2.5% in the DA mode share on highways for each $1 toll • A $1 toll for the use of all the major highways in the Austin area would lead to a 1.5% reduction in DA mode share across the entire Austin metropolitan area • The average commuter is willing to pay $12 for an hour of commute time savings

14. CONCLUSIONS Other Findings about Austin Area Commuters • The household structures of Austin area commuters are rather diverse - only 13% of commuter households are “traditional” family households • The average household income ($65,700) is higher than the national average ($58,000) • A large number of commuters have internet access at home (84%) • Average motorized vehicle ownership level of 2 per household

15. CONCLUSIONS • Key facts about Austin area commuters : • 67% white, non-Hispanic; 16% Hispanic • 57% male • avg. personal income $44,650 • primarily full-time employed • start work 7-9 AM, end work 4-6 PM • 10% telework at least occasionally • 42% have inflexible work schedules in both arrival & departure; 30% have a flexible work schedule in both arrival & departure • majority of the commuters (72%) live within 15 miles from work • Net result of high incomes and car ownership, diverse household structures and increased commute/midday stop-making is high DA mode shares

16. THANK YOU!

17. Household size and structure 2-person hhs 3 and 4 person hhs Distribution of household size Distribution of household types

18. Household income Low income < $35,000 32% Medium income $35,000-$95,000 48% High income > $95,000 20%

19. Housing characteristics Distribution of housing tenure type Distribution of residence type

20. Residential location

21. Internet access from residence

22. Motorized vehicle ownership Auto-ownership of commuters Average vehicle ownership by residence zone population density Average vehicle ownership by income level

23. Motorized vehicle type and age Average age of vehicles by vehicle type Vehicle types used for commute Vehicle types owned by commuter households

24. Demographic characteristics Racial composition of the commute population Gender of the commute population Marital status of commuters Age distribution of commuters

25. Socio-economic characteristics Distribution of highest level of education Distribution of personal income

26. Work characteristics Employment status Length of time working in Austin Employer type

27. Work start time distribution Work start time distribution

28. Work end time distribution Work end time distribution

29. Work schedule flexibility Work start time flexibility Work end time flexibility

30. Teleworking percentages Flexible arrival and/or departure times Educational Instit. Part-time employed Inflexible arrival and/or departure times Non-educational Instit. Full-time employed

31. Travel perceptions Perception of level of congestion during commute Characterization of the commute trip

32. Perception of level of congestion by commute distance Highway not used Short Commute (≤7 miles) Medium Commute (7.01 – 15 miles) Long Commute (>15 miles) Highway used Short Commute (≤7 miles) Medium Commute (7.01 – 15 miles) Long Commute (>15 miles)

33. Characterization of commute trip by commute duration Highway not used Short Commute (≤7 miles) Medium Commute (7.01 – 15 miles) Long Commute (>15 miles) Highway used Short Commute (≤7 miles) Medium Commute (7.01 – 15 miles) Long Commute (>15 miles)

34. Travel perceptions Ease of travel to non-work activities around home

35. Commute distance Distribution of commute distance

36. Nonwork stops – weekly Distribution of weekly commute stop-making Distribution during evening commute Distribution during morning commute

37. Distribution of weekly midday stop-making Non-home trips Return home trips

38. Degree of stop-making during the week Commute stop-making Midday stop-making

39. Nonwork stops - daily Distribution of number of activity stops

40. Distribution of stop-making by purpose and time period

41. Commute mode Distribution of mode use over the week

42. Commute mode choice on most recent work day

43. Mode split by weekly commute stop-making propensity Mode split by weekly midday stop-making propensity

44. Important results from Bhat and Sardesai (2004) • The ability of auto-use disincentives and hov incentives to shift commuters away from driving to car/van-pooling and transit modes will be overestimated if the impact of commute and midday stop-making is ignored • Commuters are not only concerned about average travel time but also about the reliability of travel time • The average commuter is willing to pay $12 for an hour of commute savings • Commuters have a more positive image of a potential CRT mode than the current bus mode

45. Important results from Bhat and Sardesai (2004) contd… • The presence of a grocery store around potential CRT stations acts as an impetus for CRT mode use; however, the presence of a child care center does not provide any stimulation • A new CRT mode is predicted to capture 4.1% of the overall mode share (2.6% from DA) • Within the group of individuals for whom CRT is an available alternative, a shift of 15% from driving to CRT is projected • Tolls on highways can be expected to lead to a drop of about 2.5% in the DA mode share on the highways for each $1 toll

46. Commute duration Commute durations by mode

47. Commute Time-of-Day Distribution of the time of the morning commute Distribution of the time of the evening commute