Crowdsensing-based transportation services — An analysis from business model and sustainability viewpoints

Traffic and transportation are ongoing digitalisation. Travellers always carry smartphones everywhere they go. Smartphone-based crowdsensing can be used to collect and aggregate traffic information for services that contribute to smoother and more sustainable transportation and traffic — but only if the business model is profitable in the long-term. The authors analyse two existing crowdsensing services in traffic and transportation context (Waze, Moovit) and one being developed (TrafficSense) using findings from business model (two-sided markets; data use), crowdsensing (technical overview, participant incentives), and transportation (efficiency, sustainable urban transportation) literature. Waze may alleviate traffic congestion by helping its millions of users to avoid traffic jams. Moovit makes public transport more attractive by making it easier and smoother to use for travellers. TrafficSense service is developed in a research project. It uses crowdsensing to learn regular, multimodal routes of travellers. The information can be used to predict the general traffic and congestion levels based on the predicted intents of the crowd of travellers. The authors' contribution is to combine distinct but complementary viewpoints from two-sided markets, business models, crowdsensing, and transportation research to analyse the potential business and sustainability impacts of the emerging crowdsensing-based smart transportation services.

• Record URL:
  https://doi.org/10.1016/j.rtbm.2016.03.006
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2210539516300116
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22105395
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Heiskala, Mikko
  • Jokinen, Jani-Pekka
  • Tinnilä, Markku
• Publication Date: 2016-3

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 38-48
• Serial:
  • Research in Transportation Business & Management
  • Volume: 18
  • Publisher: Elsevier
  • ISSN: 2210-5395
  • Serial URL: http://www.sciencedirect.com/science/journal/22105395

Subject/Index Terms

• TRT Terms: Intelligent transportation systems; Sensors; Smartphones; Sustainable development; Traffic congestion; Travelers
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01600062
• Record Type: Publication
• Files: TRIS
• Created Date: May 24 2016 5:11PM