{"id":"https://openalex.org/W3212387777","doi":"https://doi.org/10.1109/tits.2021.3124221","title":"Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers\u2019 Route Choices","display_name":"Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers\u2019 Route Choices","publication_year":2021,"publication_date":"2021-11-10","ids":{"openalex":"https://openalex.org/W3212387777","doi":"https://doi.org/10.1109/tits.2021.3124221","mag":"3212387777"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3124221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3124221","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089367510","display_name":"Xiaoqiang Kong","orcid":"https://orcid.org/0000-0002-8120-0754"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaoqiang Kong","raw_affiliation_strings":["Department of Civil Engineering, Texas A&#x0040;M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-8120-0754","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Texas A&#x0040;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410611","display_name":"Yunlong Zhang","orcid":"https://orcid.org/0000-0002-6227-6956"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunlong Zhang","raw_affiliation_strings":["Department of Civil Engineering, Texas A&#x0040;M University, College Station, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Texas A&#x0040;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051689240","display_name":"William L. Eisele","orcid":"https://orcid.org/0000-0003-1884-9822"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"William L. Eisele","raw_affiliation_strings":["Mobility Division of Texas A&#x0026;M Transportation Institute, Bryan, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-1884-9822","affiliations":[{"raw_affiliation_string":"Mobility Division of Texas A&#x0026;M Transportation Institute, Bryan, TX, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100451720","display_name":"Xiao Xiao","orcid":"https://orcid.org/0000-0002-1911-3449"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Xiao","raw_affiliation_strings":["Department of Civil Engineering, Texas A&#x0040;M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-1911-3449","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Texas A&#x0040;M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5089367510"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":1.5533,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85064344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"23","issue":"8","first_page":"13419","last_page":"13428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/truck","display_name":"Truck","score":0.76927649974823},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.7657755613327026},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5455543398857117},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5395544767379761},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5315475463867188},{"id":"https://openalex.org/keywords/travel-time","display_name":"Travel time","score":0.42049944400787354},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4100441634654999},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3727475702762604},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.3613172471523285},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.33200937509536743},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.33117151260375977}],"concepts":[{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.76927649974823},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.7657755613327026},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5455543398857117},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5395544767379761},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5315475463867188},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.42049944400787354},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4100441634654999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3727475702762604},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3613172471523285},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.33200937509536743},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.33117151260375977},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2021.3124221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3124221","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W68266840","https://openalex.org/W223708877","https://openalex.org/W1581410764","https://openalex.org/W1766637835","https://openalex.org/W1936915774","https://openalex.org/W1940133876","https://openalex.org/W2008003976","https://openalex.org/W2012609663","https://openalex.org/W2023495176","https://openalex.org/W2026882253","https://openalex.org/W2033904300","https://openalex.org/W2043181301","https://openalex.org/W2047533111","https://openalex.org/W2052876748","https://openalex.org/W2053563278","https://openalex.org/W2063714432","https://openalex.org/W2064954028","https://openalex.org/W2070348058","https://openalex.org/W2092313169","https://openalex.org/W2112199864","https://openalex.org/W2148503729","https://openalex.org/W2160544196","https://openalex.org/W2182073633","https://openalex.org/W2187778886","https://openalex.org/W2295598076","https://openalex.org/W2587886417","https://openalex.org/W2623456051","https://openalex.org/W2768361919","https://openalex.org/W2785409760","https://openalex.org/W2805789240","https://openalex.org/W2903122944","https://openalex.org/W3011820231","https://openalex.org/W3016190669","https://openalex.org/W3021507086","https://openalex.org/W3036845693","https://openalex.org/W3037671731","https://openalex.org/W4229927390","https://openalex.org/W6602728629","https://openalex.org/W6610476224","https://openalex.org/W6634594660","https://openalex.org/W6686389106","https://openalex.org/W6737947904","https://openalex.org/W6757042052","https://openalex.org/W6776642205"],"related_works":["https://openalex.org/W632098868","https://openalex.org/W565848107","https://openalex.org/W627617993","https://openalex.org/W2169149137","https://openalex.org/W1984646413","https://openalex.org/W2416264873","https://openalex.org/W3181632587","https://openalex.org/W2049970759","https://openalex.org/W2014007647","https://openalex.org/W2081745456"],"abstract_inverted_index":{"This":[0],"paper":[1],"explores":[2],"the":[3,63,68,73,90,108,114,127,130,159,173,194,204],"relationships":[4],"between":[5],"travel":[6,22,197],"time":[7,11,23,141,178,198],"index":[8,181],"and":[9,21,25,98,121,135,138,155,163,179],"planning":[10],"index,":[12],"two":[13],"proven":[14],"indices":[15,137,165],"used":[16],"to":[17,66,151,189],"measure":[18],"real-time":[19,152,190],"congestion":[20,153,191],"reliability,":[24],"their":[26],"impacts":[27],"on":[28,40,166,176],"truck":[29,85,184],"drivers\u2019":[30],"route":[31,144],"choices":[32],"through":[33,83],"training":[34],"of":[35,93,203],"a":[36,41,84,200],"predictive":[37,64,79],"model":[38,65,80,104,116],"based":[39],"machine":[42,54,74],"learning":[43,55,75],"algorithm\u2014eXtreme":[44],"Gradient":[45],"Boost":[46],"(XGBoost).":[47],"Moreover,":[48,172],"this":[49],"study":[50,175],"adopts":[51],"an":[52],"interpretable":[53],"framework":[56],"called":[57],"SHapley":[58],"Additive":[59],"ExPlanation":[60],"(SHAP)":[61],"in":[62,161],"reveal":[67],"insights":[69],"usually":[70],"hidden":[71],"inside":[72],"\u201cblack":[76],"box.\u201d":[77],"The":[78,100,111],"is":[81,105,199],"trained":[82],"trajectory":[86],"dataset":[87],"provided":[88],"by":[89],"Maryland":[91],"Department":[92],"Transportation":[94],"State":[95],"Highway":[96],"Administration":[97],"INRIX.":[99],"classical":[101],"logistic":[102],"regression":[103],"adopted":[106],"as":[107],"baseline":[109],"model.":[110],"results":[112,131],"show":[113],"XGBoost":[115],"can":[117],"better":[118],"handle":[119],"nonlinearity":[120],"provide":[122],"more":[123,149,187],"reliable":[124],"predictions.":[125],"Through":[126],"SHAP":[128],"framework,":[129],"indicate":[132],"that":[133,183],"mobility":[134,162,180],"reliability":[136,156,164],"total":[139,205],"trip":[140,177,206],"nonlinearly":[142],"influence":[143],"choices.":[145],"Truck":[146],"drivers":[147,185],"are":[148,186],"sensitive":[150,188],"information":[154,157,192],"when":[158],"differences":[160],"candidate":[167,195],"routes":[168],"reach":[169],"certain":[170],"thresholds.":[171],"interaction":[174],"shows":[182],"if":[193],"routes\u2019":[196],"larger":[201],"portion":[202],"time.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
