Transit agencies electronically publish transit schedules and route data to improve customer experience or as part of the open-data initiative. The release of such data poses several data management challenges because schedules for a single city can easily exceed storage requirements of several hundreds of megabytes. One way to deal with these challenges is data compression. The encoding of public transit schedules often follows the General Transit Feed Specification (GTFS), which cannot be compressed well out of the box. We propose GTFSCompress, which is an algorithm for compression of GTFS data, based on referential compression queues, which compress a column stream depending on previously seen items in this stream and other streams. Our evaluation on ten real-world data sets shows that GTFS feeds can be compressed by a factor of 100 and more. This is up to one order of magnitude better than using the best standard compressors.


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    Title :

    Lossless Compression of Public Transit Schedules


    Contributors:


    Publication date :

    2016-11-01


    Size :

    1179352 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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