Investigating Impact of Crowdsourcing on Smart Freight Mobility

Several smartphone applications related to traffic information and communications have seen a surge in recent times, however, with missing developments in artificial intelligence needed to cause forward looking solutions to smart transportation needs, particularly for freight. Social media and mobile applications provide valuable platform for transportation agencies in collection and sharing real time data on traffic congestion, incidents and weather impacts. Real time data sharing and collection is made possible due to the “crowdsourcing” nature of social media - which a recently published Federal Highway Administration (FHWA) report cites as an emerging strategy to connect smartphone users with traffic management agencies. Crowdsourcing refers to a ‘distributed problem-solving model’ soliciting solutions from crowd of undefined size. Crowdsourced data primarily comes from social media, however, in a raw format which need to be optimized in collection and dissemination for understanding the traveling public. With no roadway infrastructure needed for data collection in crowdsourcing, the technology is considered to be one of the top trends by Transportation Management Centers (TMCs) for coordinating their responses to traffic congestion and incidents in real time. Smart Freight Mobility has been the research spotlight under a joint modal ‘Smart Roadside’ program between the FHWA and Federal Motor Carrier Safety Administration (FMCSA). The program encompasses technologies for enhanced roadside condition and traffic information sharing with commercial vehicle for route planning and improved access to intermodal ports, urban pick-up, and delivery locations that are crucial to the missions of the U.S. Department of Transportation (USDOT). The vision underlined under this program is one in which commercial vehicles, highway facilities, enforcement resources, intermodal facilities, and other modes on the transportation system collect and share data seamlessly in order to improve freight’s operational efficiency and mobility – which this proposed research terms as the “smart freight”. The contribution of crowdsourcing in improving transportation efficiency in real time is evolving rapidly and qualitatively, creating the need to develop models that characterize smart freight mobility. Therefore, this research will develop such analytical models that leverage both crowdsourced data on traffic conditions and data such as commodity flows, fuel consumption etc. of conventional freight to design operations of a smart freight system. A sequence of four interrelated objectives aptly define the approach, which are as follows: (1) identifying sources of available conventional freight data – such as commodity flow, truck volume, air cargo volume etc. across all modes; (2) integrating crowdsourced data with conventional freight truck data for model building; (3) building stochastic models for mobility that characterize smart freight; and finally (4) estimating efficiency (in fuel consumption, ton-miles traveled etc.) from the predictive capabilities of the models for smart freight system


    • English


    • Status: Active
    • Contract Numbers:


    • Sponsor Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626

      National Center for Metropolitan Transportation Research

      University of Southern California
      650 Childs Way, RGL 107
      Los Angeles, CA  United States  90089-0626

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Feldman, Doug

    • Principal Investigators:

      Chandra, Shailesh

    • Start Date: 20170930
    • Expected Completion Date: 20180930
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01643002
    • Record Type: Research project
    • Source Agency: National Center for Metropolitan Transportation Research
    • Contract Numbers: 17-02
    • Files: UTC, RiP
    • Created Date: Aug 1 2017 3:07PM