Innovative travel data collection recommendations : final report.

Details

• Alternative Title:
  Project title: innovative travel data collection-planning for the next two decades
• Creators:
  Lawson, Catherine T.
• Corporate Creators:
  University at Albany. Albany Visualization and Informatics Lab
• Corporate Contributors:
  New York Metropolitan Transportation Council ; University Transportation Research Center
• Subject/TRT Terms:
  • Data collection
  • Travel behavior
  • Forecasting
  • Travel diaries
  • Travel surveys
• Publication/ Report Number:
  Z-14-04
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; New York Metropolitan Area
• Corporate Publisher:
  University Transportation Research Center
• Abstract:
  This study had the following objectives: ; 1. To identify and clarify these two emerging effects – real time data and changing culture, ; 2. To identify the shifts in data collection and transportation modeling that must take place to ; assist in identifying and forecasting travel behavior, and ; 3. To discuss the impacts of such operational shifts, both in cost and outcomes to provide NYMTC with the cost and efficacy impacts of incorporating these ; emerging tools.” ; To address these objectives, the research team at Albany Visualization and Informatics Lab (AVAIL), led by Dr. Catherine Lawson, PhD., from the University at ; Albany, conducted a literature review; a cost benefit analysis of current and emerging transportation data surveying and modeling ; methodologies; and produced a set of recommendations for the near-term and the longer-term. ; The literature review and cost benefit analysis revealed certain facts about the state of travel data collection in the United States. The paper travel diary remains the ; predominant instrument for collecting travel data despite its well-documented shortcomings and high cost. GPS devices have ; grown in popularity but are used primarily as a supplement for paper travel diaries. The value of travel surveying via smartphones is no longer a strictly academic ; question as numerous agencies have used the smartphone in a travel survey, either as the primary survey instrument or in a ; subsample (Indiana, Oregon, Singapore, Boulder, etc.). Origin-destination tables used in travel demand models can be constructed from social media posts and calldetail ; records though these datasets often lack valuable information (such as reliable trip purpose or mode), present ; incomplete pictures of travel (especially in the case of social media), are prohibitively aggregated, or are not representative. Finally, the research team found it is ; possible to develop and deploy a travel demand model that does not use travel survey data for inputs, opening the possibility of eliminating or reducing the size of ; travel surveys. ; This study introduces two categories to classify data collection efforts: ; 1. Active Data Collection – the use of self-report and surveying to generate data. ; 2. Passive Data Collection – the acquisition of existing data. ; The research suggests three orientations toward travel data collection, each with their own risks and advantages that could satisfy NYMTC’s modeling needs, while ; enabling future cost savings and/or increases in data quality. Two of these three orientations (or pathways) emphasize Active Data ; Collection strategies while the third emphasizes Passive Data Collection. These pathways are fluid and dynamic. They are not intended as a step-by-step guide to the ; future. Instead, they are intended to illuminate the data collection trajectory, highlighting opportunities and delineating the consequences, both positive and ; negative, of various data collection decisions. ; Briefly, these pathways are: ; 1. Paper and Online Diary Travel Survey (with GPS or Smartphone Supplement) ; 2. Smartphone Diary Travel Survey (with Online Supplement) ; 3. Passive Data Collection (with Smartphone Supplement)
  More +
• Format:
  PDF
• Funding:
  UTRC/RF Grant No: 49198-42-27, 55606-02-27
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/32332/dot_32332_DS1.pdf
• File Type:
  [PDF - 6.59 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:e74377c6aa28d4efc22bea9a110939991afc4404d6598f5e3e85f2ed18098b5d