Residential Building Transportation Performance Monitoring Study

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Stephen is research director for Mobility Lab. He specializes in the latest research, best practices for urban planning, and analysis of current events in transportation.
October 10, 2013

Arlington Residential Buildings with High-Efficiency Transportation

The Residential Transportation Performance Monitoring Study is an aggregate analysis of 16 individual transportation performance monitoring studies conducted at high-density residential buildings in Arlington. These performance-monitoring studies are conducted in order to comply with legal conditions adopted during their development process.

Building-level studies provided information about travel and parking behaviors in residential buildings where transportation demand management (TDM) services are provided by Arlington County Commuter Services (ACCS). These studies aim to provide data and feedback to staff and decision-makers in order to evaluate Arlington’s parking and TDM policies and programs.

Residential Site Plan Aggregate Study Project Location Map

Residential Site Plan Aggregate Study Project Location Map

The aggregate study combined data from the 16 buildings into a larger data set that provided a greater level of confidence in the findings and protected the privacy of residents. These generalized findings can be used for public dissemination and discourse about the performance of residential buildings relative to County transportation objectives.

The aggregate study compiled information from the individual building studies to address the following topics:

  • Mode split and vehicle trip generation. How well are these buildings supporting countywide transportation goals and objectives? Are we moving more people without more traffic?
  • Parking provision and availability. How well are these buildings supplying the “right” amount of parking? Are minimum parking needs met?
  • Auto ownership. What is the relationship between auto ownership, travel behavior, and other local conditions?
  • Awareness/attitudes. Is there a correlation between the awareness and attitudes of residents with their observed travel behavior? Do travel assistance/services influence mode choice and trip generation?
  • Local trip generation data. How do Arlington sites compare to the Institute of Transportation Engineers (ITE) standards for trip generation? How accurate are the traffic impact analyses (TIA) that use ITE standards to estimate impacts of new developments?

KEY FINDINGS

Commute Travel

  • Study residents use transit more than Arlington residents overall (34 percent study vs. 27 percent) and significantly more than the regional average of 21 percent.
  • Study residents’ commute travel is similar to the travel patterns of commuters who live in the immediate neighborhood of the study buildings, but they ride transit slightly more.
  • Access to transit service at home and walkability of a residential area are both related to low drive-alone rates for commuting.
  • Parking is a powerful factor in commute decision-making, but parking availability/price at work is likely more important than parking at home.
  • Work location is a strong component of commute mode.

Non-Work Travel

  • Transit, walking, and biking account for 39 percent of the non-work trips made by study residents.
  • The non-work transit share is higher for study buildings than for their immediate neighborhoods.
  • Access to transit seems a less significant factor in non-work mode choice than for commuting.
  • The share of non-work walk trips is clearly related to the extent of services within walking distance.
  • The role of residential parking on non-work mode use is difficult to define – most likely it influences vehicle ownership, which in turn influences mode choice.

Vehicle Trip Generation

  • Peak hour and daily trips for buildings within Metrorail corridors for all days of the week were much lower than predicted trips based on appropriate Institute of Transportation Engineers (ITE) rates. Some trip generation rates for buildings outside the Metrorail corridors were also much lower than ITE rates.

    Peak Hour Trip Generation in Metrorail Corridors Compared with ITE Rates

    Peak Hour Trip Generation in Metrorail Corridors Compared with ITE Rates

  • The location – within or outside the Metrorail corridors – was the most significant factor affecting trip generation. Density of destinations (Walk Score), higher neighborhood intensity, and provision of a shuttle or free transit seemed to be associated with lower trip generation outside the corridors.
  • There was no noticeable difference in the trip generation of apartments and condominiums, or by average age of residents in the building.

Vehicle Ownership

  • Vehicle ownership increased with average household income.

    Vehicle Ownership in Site Plan Buildings

    Vehicle Ownership in Site Plan Buildings

  • Condominium owners owned more vehicles per adult than apartment residents.
  • There was a definite inverse relationship between vehicle ownership and transit access.
  • Ownership rates were lower in more walkable areas than in “car dependent” areas, but were about the same if the area was “somewhat,” “very,” or “extremely” walkable.
  • Vehicle ownership is strongly related to the cost of residential parking – particularly at a cost of $95 or more per month.
  • Parking occupancy and vehicle use seemed unrelated to the spaces per resident provided.
  • Overall parking occupancy within Metrorail corridors was similar for all weekdays. Weekend occupancy was higher. Sunday evening occupancy was similar to the occupancy on weekday evenings.

Influence of TDM Services

  • Respondents who knew of Arlington services used non-drive-alone transportation options, such as transit, bicycling and walking, at higher rates for commute and non-work trips than did respondents who were not aware of Arlington services. Respondents who had used the services had even higher use rates.
  • There was a strong relationship between the awareness/use of TDM services in the workplace and the use of non-drive-alone transportation options for commuting. There was a modest relationship of commute mode with home-based TDM.
  • 75 percent of respondents had TDM services at work.
  • 85 percent of respondents mentioned having at least one home-based TDM service, and 56 percent had used a service.
  • Home-based transit and bike/walk services seemed to influence the use of these modes for non-work trips.
  • Awareness of Arlington TDM services was the same as for the County overall, and 34 percent had used an Arlington service.

METHODOLOGY

  • Transportation performance monitoring studies were conducted at 16 residential sites between 2010 and 2012.
  • Vehicle trips were counted by tube (or hose) counts for 24 hours/day for seven consecutive days for each entrance/exit of parking facilities, i.e. garages or surface lots. Trips were aggregated into 15-minute intervals. Parking occupancy was calculated for the seven-day survey period based on a one-time manual count during the week. The counts were compared with ITE codes 221 (low-rise apt); 222 (high-rise apt); 232 (high rise condo/townhouse); 310 (hotel).
  • Data on commute and non-commute mode split, vehicle ownership, and demographic characteristics were collected through voluntary online and paper surveys. A total of 1,283 individuals completed the survey.
  • The property manager was also asked to send notifications and reminders over e-mail for a period of two to four weeks or until a response rate of at least 20 percent per building was reached.
  • Eleven sites were located within the two Metrorail corridors, i.e. Jefferson-Davis or yellow/blue line and Rosslyn-Ballston or orange line. Of the five remaining sites, one site was located near the East Falls Church Metrorail but was aggregated with sites outside the Metrorail corridors since many neighborhood and travel variables were similar to these sites.
  • Sites were selected as they came due for the compulsory studies included in their development conditions.
  • Building and neighborhood data were collected through an interview with the property manager as well as through secondary research of Arlington County data (including Planning, Research and Analysis Team data, Capital Bikeshare, and Department of Real Estate Assessments) and public websites (including Walkscore, and Google Maps).

DOCUMENTS FOR DOWNLOAD

Full Report with Appendices (Analysis Variable List and Survey Instrument) (PDF)

Full Slide Presentation of Topline Results (PDF)

Data Tables (Excel File)

Summary Slide Presentation (PDF)

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