Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. The authors' research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.

• Record URL:
  • https://doi.org/10.1016/j.trc.2018.10.017
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18304455
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Cui, Yu
  • Meng, Chuishi
  • He, Qing
  • 0000-0003-2596-4984
  • Gao, Jing
• Publication Date: 2018-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 159-174
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 97
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Application programming interfaces; Bayes' theorem; Forecasting; Neural networks; Social media; Trip purpose
• Identifier Terms: Google Places
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01689114
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 19 2018 4:52PM