With advancements in sensor technologies, intelligent transportation systems can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this paper, we analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, we propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, we first develop a low-dimensional model of the network. We then apply Huffman coding (HC) in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, we consider a large and heterogeneous test network comprising of more than 18 000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Near-Lossless Compression for Large Traffic Networks




    Publication date :

    2015-08-01


    Size :

    1245277 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Near-Lossless Compression for Large Traffic Networks

    Asif, Muhammad Tayyab / Srinivasan, Kannan / Mitrovic, Nikola et al. | DSpace@MIT | 2015

    Free access

    Near-Lossless Compression for Large Traffic Networks

    Asif, Muhammad Tayyab | Online Contents | 2015


    Near-lossless image compression techniques

    Ansari, R. / Memon, N. / Ceran, E. | British Library Online Contents | 1998


    Lossless and Near-Lossless Image Compression Based on Multiresolution analysis

    Arandiga, F. | British Library Conference Proceedings | 2005