{"id":"https://openalex.org/W4396221930","doi":"https://doi.org/10.3390/ijgi13050146","title":"Exploring the Pedestrian Route Choice Behaviors by Machine Learning Models","display_name":"Exploring the Pedestrian Route Choice Behaviors by Machine Learning Models","publication_year":2024,"publication_date":"2024-04-28","ids":{"openalex":"https://openalex.org/W4396221930","doi":"https://doi.org/10.3390/ijgi13050146"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi13050146","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi13050146","pdf_url":"https://www.mdpi.com/2220-9964/13/5/146/pdf?version=1714298892","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/13/5/146/pdf?version=1714298892","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068034212","display_name":"Cheng\u2010Jie Jin","orcid":"https://orcid.org/0000-0003-2723-5312"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cheng-Jie Jin","raw_affiliation_strings":["Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072439766","display_name":"Yuanwei Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanwei Luo","raw_affiliation_strings":["Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085628819","display_name":"Chenyang Wu","orcid":"https://orcid.org/0000-0003-1527-7865"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]},{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Chenyang Wu","raw_affiliation_strings":["School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China","Urban System Lab, Imperial College London, London SW7 2AZ, UK","School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Urban System Lab, Imperial College London, London SW7 2AZ, UK","institution_ids":["https://openalex.org/I47508984"]},{"raw_affiliation_string":"School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061611494","display_name":"Yuchen Song","orcid":"https://orcid.org/0000-0002-4356-1145"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Song","raw_affiliation_strings":["Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324955","display_name":"Dawei Li","orcid":"https://orcid.org/0000-0002-5936-2116"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Li","raw_affiliation_strings":["Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5068034212"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":7.5631,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.96637134,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":"5","first_page":"146","last_page":"146"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10841","display_name":"Economic and Environmental Valuation","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10841","display_name":"Economic and Environmental Valuation","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.996999979019165,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9965999722480774,"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/pedestrian","display_name":"Pedestrian","score":0.7861852049827576},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6615036129951477},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6457907557487488},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5768832564353943},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5625611543655396},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5489917993545532},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48962074518203735},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4819735884666443},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.4536624550819397},{"id":"https://openalex.org/keywords/mixed-logit","display_name":"Mixed logit","score":0.4496786594390869},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.13204899430274963},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11735883355140686}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7861852049827576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6615036129951477},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6457907557487488},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5768832564353943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5625611543655396},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5489917993545532},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48962074518203735},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4819735884666443},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.4536624550819397},{"id":"https://openalex.org/C95057490","wikidata":"https://www.wikidata.org/wiki/Q6883984","display_name":"Mixed logit","level":3,"score":0.4496786594390869},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.13204899430274963},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11735883355140686},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/ijgi13050146","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi13050146","pdf_url":"https://www.mdpi.com/2220-9964/13/5/146/pdf?version=1714298892","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:18133e602fa54ae884d2d9f8c012ed40","is_oa":true,"landing_page_url":"https://doaj.org/article/18133e602fa54ae884d2d9f8c012ed40","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information, Vol 13, Iss 5, p 146 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/ijgi13050146","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi13050146","pdf_url":"https://www.mdpi.com/2220-9964/13/5/146/pdf?version=1714298892","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G2331599461","display_name":null,"funder_award_id":"D5000230159","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3256434469","display_name":null,"funder_award_id":"52102389","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3547678403","display_name":null,"funder_award_id":"71971056","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3983838576","display_name":null,"funder_award_id":"71801036","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396221930.pdf"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1963703166","https://openalex.org/W1974590219","https://openalex.org/W2022242289","https://openalex.org/W2027739325","https://openalex.org/W2027870918","https://openalex.org/W2029807096","https://openalex.org/W2125847307","https://openalex.org/W2137344397","https://openalex.org/W2502026285","https://openalex.org/W2516809705","https://openalex.org/W2589339609","https://openalex.org/W2618851150","https://openalex.org/W2726842553","https://openalex.org/W2743943492","https://openalex.org/W2790382949","https://openalex.org/W2805987667","https://openalex.org/W2813294849","https://openalex.org/W2891462694","https://openalex.org/W2896425003","https://openalex.org/W2925926810","https://openalex.org/W2941854795","https://openalex.org/W2945187923","https://openalex.org/W2946120416","https://openalex.org/W2949941399","https://openalex.org/W2969484346","https://openalex.org/W2976176125","https://openalex.org/W2985697044","https://openalex.org/W2999615587","https://openalex.org/W3016190669","https://openalex.org/W3088818947","https://openalex.org/W3095007137","https://openalex.org/W3117288982","https://openalex.org/W3169880458","https://openalex.org/W3183781939","https://openalex.org/W3185153964","https://openalex.org/W3212387777","https://openalex.org/W4200129545","https://openalex.org/W4288046880","https://openalex.org/W4296226001","https://openalex.org/W4308796162","https://openalex.org/W4311401368","https://openalex.org/W4366814689","https://openalex.org/W4382681582","https://openalex.org/W6776642205","https://openalex.org/W6846817239","https://openalex.org/W6854630327"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"To":[0],"investigate":[1],"pedestrian":[2,42,131,154,160],"route":[3,132,149,165,187],"choice":[4,15,133],"mechanisms":[5],"from":[6,10,40,144],"a":[7,61,176,189],"perspective":[8],"distinct":[9],"that":[11,56,98,123,180],"employed":[12],"in":[13,36,72],"discrete":[14],"models":[16,23,87],"(DCMs),":[17],"this":[18,37],"study":[19],"utilizes":[20],"machine":[21,79,85],"learning":[22,80,86],"and":[24,103,114,156,171],"employs":[25],"SHapley":[26],"Additive":[27],"exPlanations":[28],"(SHAP)":[29],"for":[30,195],"model":[31],"interpretation.":[32],"The":[33],"data":[34],"used":[35],"paper":[38],"come":[39],"several":[41],"flow":[43],"experiments":[44],"with":[45],"two":[46],"routes,":[47],"which":[48],"were":[49],"recorded":[50],"by":[51,89,136],"UAV.":[52],"Our":[53],"findings":[54],"indicate":[55],"logistic":[57],"regression":[58],"(similar":[59],"to":[60,77,142,150,169],"binary":[62],"logit":[63],"model)":[64],"exhibits":[65,175],"good":[66],"computational":[67,115],"efficiency":[68],"but":[69],"falls":[70],"short":[71],"predictive":[73],"accuracy":[74,113],"when":[75],"compared":[76,168],"other":[78],"models.":[81],"Among":[82],"the":[83,91,109,126,137,145,148,151,159,163,182,185,196],"12":[84],"assessed,":[88],"calculating":[90],"new":[92],"indicator":[93],"named":[94],"OP,":[95],"we":[96],"find":[97],"eXtreme":[99],"Gradient":[100,105],"Boosting":[101,106],"(XGB)":[102],"Light":[104],"(LGB)":[107],"strike":[108],"best":[110],"balance":[111],"between":[112],"efficiency.":[116],"Regarding":[117],"feature":[118],"contribution,":[119],"our":[120],"analysis":[121],"reveals":[122],"bottlenecks":[124,170],"exert":[125],"most":[127,192],"significant":[128],"influence":[129],"on":[130],"behavior,":[134],"followed":[135],"time":[138],"it":[139,174],"takes":[140],"pedestrians":[141,193],"return":[143,172],"end":[146],"of":[147,162,184],"origin":[152],"(reflecting":[153],"characteristics":[155],"attitudes).":[157],"While":[158],"density":[161,183],"shorter":[164,186],"contributes":[166],"less":[167],"time,":[173],"threshold":[177],"effect,":[178],"meaning":[179],"once":[181],"surpasses":[188],"certain":[190],"threshold,":[191],"opt":[194],"longer":[197],"route.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":7}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}