• RTS: road topology-based scheme for traffic condition estimation via vehicular crowdsensing

      Shao, Lu; Wang, Cheng; Liu, Lu; Jiang, Changjun; Tongji University; University of Derby; Department of Computer Science and Technology; Tongji University; Shanghai China; Department of Computer Science and Technology; Tongji University; Shanghai China; School of Computing and Mathematics; University of Derby; Derby UK; Department of Computer Science and Technology; Tongji University; Shanghai China (Wiley, 2016-01-28)
      Urban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle-sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net into road sections and junction areas. Based on this division, we introduce a two-phased data collection and processing scheme named road topology-based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data.