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

摘要

Urban traffic condition usually serves as basic information for some intelligent urban applications, for example,rnintelligent transportation system. The traditional acquisition of such information is often costly becausernof the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economicrnparadigm, can be utilized together with vehicular networks to efficiently gather vehicle-sensed datarnfor estimating the traffic condition. However, it has the problem of being lack of data uploading efficiencyrnand data usage effectiveness. In this paper, we take into account the topology of the road net to deal withrnthese problems. Specifically, we divide the road net into road sections and junction areas. Based on this division,rnwe introduce a two-phased data collection and processing scheme named road topology-based scheme.rnIt leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are firstrnprocessed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of thernsponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the localrndata to a server. By employing the inherent relations among roads, the server processes data and estimatesrntraffic condition for the road sections without vehicular data in a global vision. We conduct experimentsrnbased on real vehicle trace data. The results indicate that our design can commendably handle the problemsrnof efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data.