Identifying critical road sections using the weighted periodicity mined from trajectory data for efficient urban transportation

Critical road sections (CRS) are part of links of the road network, which have an obvious influence on urban transportation systems. Identifying CRS would contribute to improving efficient traffic management. However, existing studies pay less attention to the influence of traffic temporal dynamism on CRS and the spatial disparity of CRS in different temporal scenarios. We propose a method to identify CRS in urban road network. The new method takes sparse tensor decomposition and reconstruction for the imputation of driving speed that is calculated from trajectory data. Then, empirical mode decomposition is applied to calculate the weighted periodicity for each time series of driving speed. Finally, CRS are determined according to local spatial autocorrelation of the weighted periodicity. Taking the urban area of Xi'an City, China, as a case study, the result show that the new method could effectively achieve the imputation of speed information (R-2 >0.67). The weighted periodicity could characterize the temporal dynamism of driving speed with considering the aliasing effect of traffic modes. The CRS reflect the multi-center characteristics of urban transportation systems, and show obvious spatial disparity in holiday and workday. The CRS identified by the proposed method could be applied to improving urban traffic management and maintaining efficient urban transportation.