Loading…
Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system
We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 1293 |
| container_issue | |
| container_start_page | 1288 |
| container_title | |
| container_volume | |
| creator | Brisimi, Theodora S. Ariafar, Setareh Yue Zhang Cassandras, Christos G. Paschalidis, Ioannis C. |
| description | We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones in need of immediate attention based on a proposed "anomaly index." We introduce appropriate regularization to the classification algorithms we employ, which has the effect of utilizing a sparse set of relevant features to perform the classification. Further, our novel "anomaly index" allows us to prioritize among actionable obstacles. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of our system in practice. |
| doi_str_mv | 10.1109/CoASE.2015.7294276 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_7294276</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7294276</ieee_id><sourcerecordid>7294276</sourcerecordid><originalsourceid>FETCH-LOGICAL-i208t-39ef54bcd91b6f6edc0b44b7955eaa6d181d18b5d4cea1ee18f5fbac2ce0e673</originalsourceid><addsrcrecordid>eNo9UMlKA0EUbEXBEPMDeukfmNivZ3oZb2GICwQ8JPfQyxvTMhvTHWT-3kSDh6KqDlVQRcgDsCUAK5-qfrVdLzkDsVS8LLiSV2RRKg2FVLkGnefXZMZBQqaZLm_-tWJ3ZBHjF2MMlFSCixk5bLGLofukpvPUNSbGUE9nP_bGf5uJ9jYm4xqMz3R3QBrTiJioPbbDKdK3ppmox4Quhb77LfHoQjybeByGfkw0TjFhe09ua9NEXFx4TnYv6131lm0-Xt-r1SYLnOmU5SXWorDOl2BlLdE7ZovCqlIINEZ60HCCFb5waAARdC1qaxx3yPA0f04e_2oDIu6HMbRmnPaXm_If0eldYg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system</title><source>IEEE Xplore All Conference Series</source><creator>Brisimi, Theodora S. ; Ariafar, Setareh ; Yue Zhang ; Cassandras, Christos G. ; Paschalidis, Ioannis C.</creator><creatorcontrib>Brisimi, Theodora S. ; Ariafar, Setareh ; Yue Zhang ; Cassandras, Christos G. ; Paschalidis, Ioannis C.</creatorcontrib><description>We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones in need of immediate attention based on a proposed "anomaly index." We introduce appropriate regularization to the classification algorithms we employ, which has the effect of utilizing a sparse set of relevant features to perform the classification. Further, our novel "anomaly index" allows us to prioritize among actionable obstacles. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of our system in practice.</description><identifier>ISSN: 2161-8070</identifier><identifier>EISSN: 2161-8089</identifier><identifier>EISBN: 9781467381833</identifier><identifier>EISBN: 1467381837</identifier><identifier>DOI: 10.1109/CoASE.2015.7294276</identifier><language>eng</language><publisher>IEEE</publisher><subject>anomaly detection ; Cities and towns ; Classification ; Decision support systems ; Entropy ; Indexes ; Logistics ; machine learning ; smart cities ; Support vector machines ; Vehicles</subject><ispartof>2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015, p.1288-1293</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7294276$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54553,54918,54930</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7294276$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Brisimi, Theodora S.</creatorcontrib><creatorcontrib>Ariafar, Setareh</creatorcontrib><creatorcontrib>Yue Zhang</creatorcontrib><creatorcontrib>Cassandras, Christos G.</creatorcontrib><creatorcontrib>Paschalidis, Ioannis C.</creatorcontrib><title>Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system</title><title>2015 IEEE International Conference on Automation Science and Engineering (CASE)</title><addtitle>CoASE</addtitle><description>We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones in need of immediate attention based on a proposed "anomaly index." We introduce appropriate regularization to the classification algorithms we employ, which has the effect of utilizing a sparse set of relevant features to perform the classification. Further, our novel "anomaly index" allows us to prioritize among actionable obstacles. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of our system in practice.</description><subject>anomaly detection</subject><subject>Cities and towns</subject><subject>Classification</subject><subject>Decision support systems</subject><subject>Entropy</subject><subject>Indexes</subject><subject>Logistics</subject><subject>machine learning</subject><subject>smart cities</subject><subject>Support vector machines</subject><subject>Vehicles</subject><issn>2161-8070</issn><issn>2161-8089</issn><isbn>9781467381833</isbn><isbn>1467381837</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2015</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9UMlKA0EUbEXBEPMDeukfmNivZ3oZb2GICwQ8JPfQyxvTMhvTHWT-3kSDh6KqDlVQRcgDsCUAK5-qfrVdLzkDsVS8LLiSV2RRKg2FVLkGnefXZMZBQqaZLm_-tWJ3ZBHjF2MMlFSCixk5bLGLofukpvPUNSbGUE9nP_bGf5uJ9jYm4xqMz3R3QBrTiJioPbbDKdK3ppmox4Quhb77LfHoQjybeByGfkw0TjFhe09ua9NEXFx4TnYv6131lm0-Xt-r1SYLnOmU5SXWorDOl2BlLdE7ZovCqlIINEZ60HCCFb5waAARdC1qaxx3yPA0f04e_2oDIu6HMbRmnPaXm_If0eldYg</recordid><startdate>20150801</startdate><enddate>20150801</enddate><creator>Brisimi, Theodora S.</creator><creator>Ariafar, Setareh</creator><creator>Yue Zhang</creator><creator>Cassandras, Christos G.</creator><creator>Paschalidis, Ioannis C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20150801</creationdate><title>Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system</title><author>Brisimi, Theodora S. ; Ariafar, Setareh ; Yue Zhang ; Cassandras, Christos G. ; Paschalidis, Ioannis C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i208t-39ef54bcd91b6f6edc0b44b7955eaa6d181d18b5d4cea1ee18f5fbac2ce0e673</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2015</creationdate><topic>anomaly detection</topic><topic>Cities and towns</topic><topic>Classification</topic><topic>Decision support systems</topic><topic>Entropy</topic><topic>Indexes</topic><topic>Logistics</topic><topic>machine learning</topic><topic>smart cities</topic><topic>Support vector machines</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Brisimi, Theodora S.</creatorcontrib><creatorcontrib>Ariafar, Setareh</creatorcontrib><creatorcontrib>Yue Zhang</creatorcontrib><creatorcontrib>Cassandras, Christos G.</creatorcontrib><creatorcontrib>Paschalidis, Ioannis C.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Brisimi, Theodora S.</au><au>Ariafar, Setareh</au><au>Yue Zhang</au><au>Cassandras, Christos G.</au><au>Paschalidis, Ioannis C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system</atitle><btitle>2015 IEEE International Conference on Automation Science and Engineering (CASE)</btitle><stitle>CoASE</stitle><date>2015-08-01</date><risdate>2015</risdate><spage>1288</spage><epage>1293</epage><pages>1288-1293</pages><issn>2161-8070</issn><eissn>2161-8089</eissn><eisbn>9781467381833</eisbn><eisbn>1467381837</eisbn><abstract>We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones in need of immediate attention based on a proposed "anomaly index." We introduce appropriate regularization to the classification algorithms we employ, which has the effect of utilizing a sparse set of relevant features to perform the classification. Further, our novel "anomaly index" allows us to prioritize among actionable obstacles. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of our system in practice.</abstract><pub>IEEE</pub><doi>10.1109/CoASE.2015.7294276</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2161-8070 |
| ispartof | 2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015, p.1288-1293 |
| issn | 2161-8070 2161-8089 |
| language | eng |
| recordid | cdi_ieee_primary_7294276 |
| source | IEEE Xplore All Conference Series |
| subjects | anomaly detection Cities and towns Classification Decision support systems Entropy Indexes Logistics machine learning smart cities Support vector machines Vehicles |
| title | Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T11%3A53%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Sensing%20and%20classifying%20roadway%20obstacles:%20The%20street%20bump%20anomaly%20detection%20and%20decision%20support%20system&rft.btitle=2015%20IEEE%20International%20Conference%20on%20Automation%20Science%20and%20Engineering%20(CASE)&rft.au=Brisimi,%20Theodora%20S.&rft.date=2015-08-01&rft.spage=1288&rft.epage=1293&rft.pages=1288-1293&rft.issn=2161-8070&rft.eissn=2161-8089&rft_id=info:doi/10.1109/CoASE.2015.7294276&rft.eisbn=9781467381833&rft.eisbn_list=1467381837&rft_dat=%3Cieee_CHZPO%3E7294276%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i208t-39ef54bcd91b6f6edc0b44b7955eaa6d181d18b5d4cea1ee18f5fbac2ce0e673%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7294276&rfr_iscdi=true |