Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems
Identifieur interne : 000562 ( Pmc/Curation ); précédent : 000561; suivant : 000563Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems
Auteurs : Alicia Rodriguez-Carrion ; Carlos Garcia-Rubio ; Celeste Campo ; Alberto Cortés-Martín ; Estrella Garcia-Lozano ; Patricia Noriega-VivasSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2012.
Abstract
Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.
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DOI: 10.3390/s120607496
PubMed: 22969357
PubMed Central: 3435986
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems</title><author><name sortKey="Rodriguez Carrion, Alicia" sort="Rodriguez Carrion, Alicia" uniqKey="Rodriguez Carrion A" first="Alicia" last="Rodriguez-Carrion">Alicia Rodriguez-Carrion</name></author><author><name sortKey="Garcia Rubio, Carlos" sort="Garcia Rubio, Carlos" uniqKey="Garcia Rubio C" first="Carlos" last="Garcia-Rubio">Carlos Garcia-Rubio</name></author><author><name sortKey="Campo, Celeste" sort="Campo, Celeste" uniqKey="Campo C" first="Celeste" last="Campo">Celeste Campo</name></author><author><name sortKey="Cortes Martin, Alberto" sort="Cortes Martin, Alberto" uniqKey="Cortes Martin A" first="Alberto" last="Cortés-Martín">Alberto Cortés-Martín</name></author><author><name sortKey="Garcia Lozano, Estrella" sort="Garcia Lozano, Estrella" uniqKey="Garcia Lozano E" first="Estrella" last="Garcia-Lozano">Estrella Garcia-Lozano</name></author><author><name sortKey="Noriega Vivas, Patricia" sort="Noriega Vivas, Patricia" uniqKey="Noriega Vivas P" first="Patricia" last="Noriega-Vivas">Patricia Noriega-Vivas</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22969357</idno><idno type="pmc">3435986</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3435986</idno><idno type="RBID">PMC:3435986</idno><idno type="doi">10.3390/s120607496</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000562</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000562</idno><idno type="wicri:Area/Pmc/Curation">000562</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000562</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems</title><author><name sortKey="Rodriguez Carrion, Alicia" sort="Rodriguez Carrion, Alicia" uniqKey="Rodriguez Carrion A" first="Alicia" last="Rodriguez-Carrion">Alicia Rodriguez-Carrion</name></author><author><name sortKey="Garcia Rubio, Carlos" sort="Garcia Rubio, Carlos" uniqKey="Garcia Rubio C" first="Carlos" last="Garcia-Rubio">Carlos Garcia-Rubio</name></author><author><name sortKey="Campo, Celeste" sort="Campo, Celeste" uniqKey="Campo C" first="Celeste" last="Campo">Celeste Campo</name></author><author><name sortKey="Cortes Martin, Alberto" sort="Cortes Martin, Alberto" uniqKey="Cortes Martin A" first="Alberto" last="Cortés-Martín">Alberto Cortés-Martín</name></author><author><name sortKey="Garcia Lozano, Estrella" sort="Garcia Lozano, Estrella" uniqKey="Garcia Lozano E" first="Estrella" last="Garcia-Lozano">Estrella Garcia-Lozano</name></author><author><name sortKey="Noriega Vivas, Patricia" sort="Noriega Vivas, Patricia" uniqKey="Noriega Vivas P" first="Patricia" last="Noriega-Vivas">Patricia Noriega-Vivas</name></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Cook, D J" uniqKey="Cook D">D.J. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id><journal-id journal-id-type="iso-abbrev">Sensors (Basel)</journal-id><journal-title-group><journal-title>Sensors (Basel, Switzerland)</journal-title></journal-title-group><issn pub-type="epub">1424-8220</issn><publisher><publisher-name>Molecular Diversity Preservation International (MDPI)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22969357</article-id><article-id pub-id-type="pmc">3435986</article-id><article-id pub-id-type="doi">10.3390/s120607496</article-id><article-id pub-id-type="publisher-id">sensors-12-07496</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Rodriguez-Carrion</surname><given-names>Alicia</given-names></name><xref ref-type="corresp" rid="c1-sensors-12-07496"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Garcia-Rubio</surname><given-names>Carlos</given-names></name></contrib><contrib contrib-type="author"><name><surname>Campo</surname><given-names>Celeste</given-names></name></contrib><contrib contrib-type="author"><name><surname>Cortés-Martín</surname><given-names>Alberto</given-names></name></contrib><contrib contrib-type="author"><name><surname>Garcia-Lozano</surname><given-names>Estrella</given-names></name></contrib><contrib contrib-type="author"><name><surname>Noriega-Vivas</surname><given-names>Patricia</given-names></name></contrib><aff id="af1-sensors-12-07496">Department of Telematic Engineering, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain; E-Mails:<email>cgr@it.uc3m.es</email> (C.G.-R.);<email>celeste@it.uc3m.es</email> (C.C.);<email>alcortes@it.uc3m.es</email> (A.C.-M.);<email>emglozan@it.uc3m.es</email> (E.G.-L.);<email>pnoriega@it.uc3m.es</email> (P.N.-V.)</aff></contrib-group><author-notes><corresp id="c1-sensors-12-07496"><label>*</label>Author to whom correspondence should be addressed; E-Mail: <email>arcarrio@it.uc3m.es</email>; Tel: +34-916-248-437; Fax: +34-916-248-749.</corresp></author-notes><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>04</day><month>6</month><year>2012</year></pub-date><volume>12</volume><issue>6</issue><fpage>7496</fpage><lpage>7517</lpage><history><date date-type="received"><day>20</day><month>4</month><year>2012</year></date><date date-type="rev-recd"><day>21</day><month>5</month><year>2012</year></date><date date-type="accepted"><day>30</day><month>5</month><year>2012</year></date></history><permissions><copyright-statement>© 2012 by the authors; licensee MDPI, Basel, Switzerland.</copyright-statement><copyright-year>2012</copyright-year><license><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link>).</license-p></license></permissions><abstract><p>Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.</p></abstract><kwd-group><kwd>GSM-based location</kwd><kwd>prediction</kwd><kwd>LZ</kwd><kwd>LeZi Update</kwd><kwd>Active LeZi</kwd><kwd>recommender system</kwd><kwd>ambient intelligence</kwd><kwd>ubiquitous computing</kwd></kwd-group></article-meta></front><floats-group><fig id="f1-sensors-12-07496" position="float"><label>Figure 1.</label><caption><p>LZ tree after parsing the example movement history <italic>L</italic> = <italic>abababcdcbdab</italic>.</p></caption><graphic xlink:href="sensors-12-07496f1"></graphic></fig><fig id="f2-sensors-12-07496" position="float"><label>Figure 2.</label><caption><p>Combinations of the two independent stages.</p></caption><graphic xlink:href="sensors-12-07496f2"></graphic></fig><fig id="f3-sensors-12-07496" position="float"><label>Figure 3.</label><caption><p>Comparison of hit rate attained when fixing the tree updating scheme and varying the probability calculation method.</p></caption><graphic xlink:href="sensors-12-07496f3"></graphic></fig><fig id="f4-sensors-12-07496" position="float"><label>Figure 4.</label><caption><p>Hit rate evolution when processing the 4 days trace with Active LeZi updating scheme combined with each probability calculation method.</p></caption><graphic xlink:href="sensors-12-07496f4"></graphic></fig><fig id="f5-sensors-12-07496" position="float"><label>Figure 5.</label><caption><p>ALZ tree after parsing the example movement history.</p></caption><graphic xlink:href="sensors-12-07496f5"></graphic></fig><fig id="f6-sensors-12-07496" position="float"><label>Figure 6.</label><caption><p>Comparison of hit rate attained when fixing the probability calculation method and varying the tree updating scheme.</p></caption><graphic xlink:href="sensors-12-07496f6"></graphic></fig><fig id="f7-sensors-12-07496" position="float"><label>Figure 7.</label><caption><p>Node count of different trees (log scale).</p></caption><graphic xlink:href="sensors-12-07496f7"></graphic></fig><fig id="f8-sensors-12-07496" position="float"><label>Figure 8.</label><caption><p>Accumulated processing time needed by Active LeZi updating scheme combined with each probability calculation method (log scale).</p></caption><graphic xlink:href="sensors-12-07496f8"></graphic></fig><fig id="f9-sensors-12-07496" position="float"><label>Figure 9.</label><caption><p>Processing time spent by a mobile phone for processing each new cell and estimating the most probable next location using Active LeZi and PPM without exclusion algorithm.</p></caption><graphic xlink:href="sensors-12-07496f9"></graphic></fig><fig id="f10-sensors-12-07496" position="float"><label>Figure 10.</label><caption><p>Power consumption of a mobile phone for processing each new cell and estimating the most probable next location using Active LeZi and PPM without exclusion algorithm.</p></caption><graphic xlink:href="sensors-12-07496f10"></graphic></fig><fig id="f11-sensors-12-07496" position="float"><label>Figure 11.</label><caption><p>Hit rate attained by Active LeZi+PPM without exclusion when different number of symbols are used as prediction.</p></caption><graphic xlink:href="sensors-12-07496f11"></graphic></fig><fig id="f12-sensors-12-07496" position="float"><label>Figure 12.</label><caption><p>Data model corresponding to the database with the bus lines and corresponding cell identifiers information.</p></caption><graphic xlink:href="sensors-12-07496f12"></graphic></fig><fig id="f13-sensors-12-07496" position="float"><label>Figure 13.</label><caption><p>Block diagram of the recommender application.</p></caption><graphic xlink:href="sensors-12-07496f13"></graphic></fig><fig id="f14-sensors-12-07496" position="float"><label>Figure 14.</label><caption><p>Test scenario. Paths marked in red and green are the bus lines considered. Blue point is the starting location of the user. Yellow dots are the places where the terminal switches from one cell to another and makes the prediction about the next location.</p></caption><graphic xlink:href="sensors-12-07496f14"></graphic></fig><table-wrap id="t1-sensors-12-07496" position="float"><label>Table 1.</label><caption><p>Frequency of substrings following the current context for ALZ tree.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" rowspan="1" colspan="1"><bold><italic>l</italic><sub>2</sub> = <italic>ab</italic></bold></th><th colspan="2" align="center" valign="top" rowspan="1"><bold><italic>l</italic><sub>1</sub></bold>= <italic>b</italic></th><th colspan="5" align="center" valign="top" rowspan="1"><bold><italic>l</italic><sub>0</sub> = <italic>γ</italic></bold></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">c:l</td><td align="left" valign="top" rowspan="1" colspan="1">a:l</td><td align="left" valign="top" rowspan="1" colspan="1">da:l</td><td align="left" valign="top" rowspan="1" colspan="1">a:l</td><td align="left" valign="top" rowspan="1" colspan="1">ba:l</td><td align="left" valign="top" rowspan="1" colspan="1">bda:l</td><td align="left" valign="top" rowspan="1" colspan="1">cd: 1</td><td align="left" valign="top" rowspan="1" colspan="1">dab:l</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">esc:2</td><td align="left" valign="top" rowspan="1" colspan="1">c:0</td><td align="left" valign="top" rowspan="1" colspan="1">esc:0</td><td align="left" valign="top" rowspan="1" colspan="1">ab:2</td><td align="left" valign="top" rowspan="1" colspan="1">bc:0</td><td align="left" valign="top" rowspan="1" colspan="1">c:0</td><td align="left" valign="top" rowspan="1" colspan="1">cdc:l</td><td align="left" valign="top" rowspan="1" colspan="1">dc:0</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"></td><td align="left" valign="top" rowspan="1" colspan="1">cd:l</td><td align="left" valign="top" rowspan="1" colspan="1"></td><td align="left" valign="top" rowspan="1" colspan="1">abc:l</td><td align="left" valign="top" rowspan="1" colspan="1">bcd:l</td><td align="left" valign="top" rowspan="1" colspan="1">cb:0</td><td align="left" valign="top" rowspan="1" colspan="1">d:0</td><td align="left" valign="top" rowspan="1" colspan="1">dcb:l</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"></td><td align="left" valign="top" rowspan="1" colspan="1">d:0</td><td align="left" valign="top" rowspan="1" colspan="1"></td><td align="left" valign="top" rowspan="1" colspan="1">b:3</td><td align="left" valign="top" rowspan="1" colspan="1">bd:0</td><td align="left" valign="top" rowspan="1" colspan="1">cbd:l</td><td align="left" valign="top" rowspan="1" colspan="1">da:0</td><td align="left" valign="top" rowspan="1" colspan="1">esc:0</td></tr></tbody></table></table-wrap><table-wrap id="t2-sensors-12-07496" position="float"><label>Table 2.</label><caption><p>Example of the mapping from the bus line (sequence of bus stops) to the cells covering each bus stop.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1"><bold>Bus stop ID</bold></th><th colspan="4" align="center" valign="top" rowspan="1"><bold>Cells ID</bold></th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1"><bold>a</bold></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>o</italic></td><td align="center" valign="top" rowspan="1" colspan="1"></td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">2</td><td align="center" valign="top" rowspan="1" colspan="1"><italic>a</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><bold>b</bold></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>p</italic></td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">3</td><td align="center" valign="top" rowspan="1" colspan="1"><italic>b</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><bold>c</bold></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>r</italic></td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">4</td><td align="center" valign="top" rowspan="1" colspan="1"><bold>d</bold></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>e</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>f</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>t</italic></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">5</td><td align="center" valign="top" rowspan="1" colspan="1"><italic>e</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>f</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>g</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>u</italic></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">6</td><td align="center" valign="top" rowspan="1" colspan="1"><italic>h</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>i</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>m</italic></td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">7</td><td align="center" valign="top" rowspan="1" colspan="1"><italic>j</italic></td><td align="center" valign="top" rowspan="1" colspan="1"><italic>l</italic></td><td align="center" valign="top" rowspan="1" colspan="1"></td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr></tbody></table></table-wrap></floats-group></pmc></record>Pour manipuler ce 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