Combining California Household Travel Survey Data with Harvested Social Media Information to Form a Self-Validating Statewide Origin-Destination Travel Prediction Method

016468122016Final Report, 4/15/2015 - 3/31/2016PDFTech ReportCA16-2874Contract 65A0529 Task 026Data fusionData miningOrigin and destinationSocial mediaSpatial analysisTravel behaviorTravel demandTravel surveysCalifornia Household Travel Survey records (CHTS)California Statewide Travel Demand Model (CSTDM)United StatesCaliforniaCalifornia. Dept. of TransportationGoulias, Konstadinos G.Lee, Jae HyunDavis, Adam W.McBride, ElizabethUniversity of California, Santa BarbaraCalifornia. Dept. of Transportation. Division of Research, Innovation, and System InformationUniversity Transportation Centers Program (U.S.)US Transportation CollectionLongitudinal data of persons and households is the best source of travel behavior information for assessing policy changes. However, this type of data is rarely available and difficult to collect due to administrative barriers and technical issues in survey design. Another empirical option which would allow estimation of induced demand is tested in this project. Data from multiple sources are used to produce a statewide inventory of travel patterns and an observatory to do this repeatedly for many years in the future. In order to combine social media harvested data with the California Household Travel Survey and data in the statewide travel model, we developed a step-wise conversion procedure including a Twitter trip extraction algorithm, a spatial aggregation technique, and statistical models to study the correlation among different databases. As a result, we were able to reproduce a list of Twitter trips, a trip generation table at the block group level, and an Origin-Destination matrix. We compared the list of Twitter trips with California Household Travel Survey records (CHTS), the trip generation table with synthetically generated population, and the OD matrix with California Statewide Travel Demand Model output. Twitter trips have longer distances and durations than CHTS trips, and there are not significant differences between weekday and weekend, and weekday and Thanksgiving day. In the comparison with synthetic population, we found positive correlation between Twitter trips and walking, bicycling, and single occupancy vehicle trips in both the total number of trips and sum of the trip lengths in block groups