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PRESS: A Novel Framework of Trajectory Compression in Road
Networks
Renchu Song, Weiwei Sun, Fudan University
Baihua Zheng, Singapore Management University Yu Zheng, Microsoft Research, Beijing
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Background• Big Data
– Huge volume of spatial trajectories cause heavy burden to data storage and data process
– Trajectories contain redundant parts that contribute very limited to spatial and temporal information
Solution: Trajectory Compression
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PRESS: Paralleled Road-Network-based Trajectory Compression
Map matcher
Trajectoryre-formatter
Queryprocessor
Temporal compressor
Spatialcompressor
Spatial pathTemporal sequence
Compressedspatial path
Compressedtemporal sequence
Map trajectory
GPS trajectory
PRESS
Location-based services
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PRESS (cont’d)
• Key highlights– Separate the spatial path from the temporal information
when presenting a trajectory– Propose a lossless spatial compression algorithm HSC – Propose an error-bounded temporal compression algorithm
BTC– Support multiple popular location-based services without
fully decompressing the trajectories
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Trajectory Representation
• Traditional representation– (x1, y1, t1), (x2, y2, t1) …
• Spatial path– The sequence of road
segments passed by a trajectory
• Temporal sequence– The sequence of (di, ti)
vectors• di refers to the distance
travelled from the start of the trajectory until time stamp ti
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HSC: Spatial Compression
• Hybrid Spatial Compression (HSC) is lossless, and it consists of two stages
STAGE 1Shortest Path Compression
STAGE 2Frequent Sub-Tra.
Compression
o Input: spatial path (consecutive edge sequence)
o Output: non-consecutive edge sequence
o Input: non-consecutive edge sequence
o Output binary code
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HSC Stage 1: Shortest Path Compression• Observation: given a source s and a destination d,
most of the time we take the shortest path between s and d if all the edges roughly share the similar traffic condition
• Given an edge sequence– If the sequence refers to the shortest path from to ,
we will replace the sequence with–
‹ei, e2, e3, e4, e5, e6, ej›ejei
‹ei, ej›‹e1, e2, e3, e4, e5, e6, e7› ‹e1, e7›
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HSC Stage 2: Frequent Sub-trajectory Compression• Observation: certain road segments are much more
popular than others• Basic idea: We can treat the sequence of edges as a
string, and can employ suitable coding techniques to use fewer bits to represent more common sub-strings
• Main approach– Identify the frequent sub-trajectories (FSTs) using a
training set– Decompose a trajectory into a sequence of FSTs– Use Huffman coding to represent the decomposed
trajectory
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HSC Stage 2: Frequent Sub-trajectory Compression (cont’d)
{ Ts1=‹e1, e5, e8, e6, e3›, Ts2=‹e1, e5, e2, e1, e4, e8›, Ts3=‹e2, e1, e4, e6›}
Training Trajectory Set All the sub-trajectories with length {‹e1, e5, e8›, ‹e5, e8, e6›, ‹e8, e6, e3›, ‹e6, e3›, ‹e3›, ‹e1, e5, e2›, ‹e5, e2, e1›, ‹e2, e1, e4›, ‹e1, e4, e8›, ‹e4, e8›, ‹e8›, ‹e2, e1, e4›, ‹e1, e4, e6›, ‹e4, e6›, ‹e6›}
Trie: capture sub-trajectories and their frequency
Aho-Corasick Automaton: facilitate trajectory decomposition
Huffman tree: code each node in Trie
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HSC Stage 2: Frequent Sub-trajectory Compression (cont’d)
Aho-Corasick Automaton: facilitate trajectory decomposition
Huffman tree: code each node in Trie
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BTC: Temporal Compression
• Temporal info:
• TSND (Time Synchronized Network Distance): Given a trajectory T and its compressed one T′, TSND measures the maximum difference between the distance object travels via trajectory T and that via trajectory T′ at any time slot with TSND(T, T′) = Maxtx(|Dis(T, tx)−Dis(T′, tx)|).
• NSTD (Network Synchronized Time Difference) defines the maximum time difference between a trajectory T and its compressed form T′ while traveling any same distance with NSTD(T, T′) = Maxdx (|Tim(T, dx)− Tim(T′, dx)|).
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Experiments
• The experiments are based on real trajectory data from one major taxi company in Singapore. Each taxi has installed GPS, and it reports its locations regularly. In our studies, we use the trajectories reported within January 2011, in total 465,000 trajectories generated by about 15,000 taxis. The original storage cost of this dataset is 13.2GB.
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Experiment (cont’d)
• Compression ratio of HSC (spatial compression algorithm)
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Experiment (cont’d)
• Compression ratio of BTC (temporal compression algorithm)
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Experiment (cont’d)
• Compression ratio of PRESS framework
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Experiment (cont’d)• Comparison of PRESS and its competitors (note both
competitors are not bounded by TSND and NSTD but TSED only)– MMTC: Georgios Kellaris, Nikos Pelekis, and Yannis Theodoridis. Map-
matched trajectory compression. JSS, 86(6):1566–1579, 2013.
– Nonmaterial: Hu Cao and Ouri Wolfson. Nonmaterialized motion information in transport networks. In ICDT’05, pages 173–188, 2005.
• Compression ratio of commercial compressors– RAR: 3.78– ZIP: 2.09
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Q & A