Innovative travel data collection recommendations : final report.
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Innovative travel data collection recommendations : final report.

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      Project title: innovative travel data collection-planning for the next two decades
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    • 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)
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