{"id":"https://openalex.org/W4416662002","doi":"https://doi.org/10.1016/j.ject.2025.11.001","title":"Evaluating machine learning performance using python for neural network models in urban transportation in New York city case study","display_name":"Evaluating machine learning performance using python for neural network models in urban transportation in New York city case study","publication_year":2025,"publication_date":"2025-11-26","ids":{"openalex":"https://openalex.org/W4416662002","doi":"https://doi.org/10.1016/j.ject.2025.11.001"},"language":"en","primary_location":{"id":"doi:10.1016/j.ject.2025.11.001","doi":"https://doi.org/10.1016/j.ject.2025.11.001","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ject.2025.11.001","pdf_url":null,"source":{"id":"https://openalex.org/S4387289263","display_name":"Journal of Economy and Technology","issn_l":"2949-9488","issn":["2949-9488"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Economy and Technology","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W4416662002"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.ject.2025.11.001","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116142020","display_name":"Mohsen Mohammadagha","orcid":"https://orcid.org/0009-0007-0394-353X","observed_orcids":["https://orcid.org/0009-0007-0394-353X"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohsen Mohammadagha","raw_affiliation_strings":["Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA"],"raw_orcid":"https://orcid.org/0009-0007-0394-353X","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109848592","display_name":"Saeed Asadi","orcid":"https://orcid.org/0009-0005-0964-6582","observed_orcids":["https://orcid.org/0009-0005-0964-6582"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saeed Asadi","raw_affiliation_strings":["Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114424258","display_name":"Hajar Kazemi Naeini","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hajar Kazemi Naeini","raw_affiliation_strings":["Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5116142020"],"corresponding_institution_ids":["https://openalex.org/I189196454"],"apc_list":{"value":0,"currency":"USD","value_usd":0},"apc_paid":null,"fwci":4.4057,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.94626849,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"4","issue":null,"first_page":"266","last_page":"283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9926999807357788,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9926999807357788,"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":0.0013000000035390258,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reproducibility","display_name":"reproducibility","score":0.6869999766349792},{"id":"https://openalex.org/keywords/data-loss-prevention","display_name":"data loss prevention","score":0.8669999837875366},{"id":"https://openalex.org/keywords/model-architecture-optimization","display_name":"model structure optimization","score":0.7049999833106995},{"id":"https://openalex.org/keywords/random-forest-comparison","display_name":"random forest comparison","score":0.7639999985694885},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.6840000152587891},{"id":"https://openalex.org/keywords/urban-transport","display_name":"urban transport","score":0.6830000281333923},{"id":"https://openalex.org/keywords/new-york-city-taxi-data","display_name":"New York City taxi data","score":0.8130000233650208},{"id":"https://openalex.org/keywords/estimated-time-of-arrival","display_name":"estimated time of arrival","score":0.531000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7059000134468079},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6600000262260437},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6516000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6420999765396118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6294000148773193},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6097999811172485},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.602400004863739},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C106251023","wikidata":"https://www.wikidata.org/wiki/Q851989","display_name":"Porting","level":3,"score":0.38769999146461487},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3874000012874603},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C2991744967","wikidata":"https://www.wikidata.org/wiki/Q1124670","display_name":"Chinese city","level":3,"score":0.2639000117778778},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C114563136","wikidata":"https://www.wikidata.org/wiki/Q19725982","display_name":"Network planning and design","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1016/j.ject.2025.11.001","doi":"https://doi.org/10.1016/j.ject.2025.11.001","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ject.2025.11.001","pdf_url":null,"source":{"id":"https://openalex.org/S4387289263","display_name":"Journal of Economy and Technology","issn_l":"2949-9488","issn":["2949-9488"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Economy and Technology","raw_type":"journal-article"},{"id":"doi:10.36227/techrxiv.174239957.76698574/v1","doi":"https://doi.org/10.36227/techrxiv.174239957.76698574/v1","is_oa":true,"landing_page_url":"https://doi.org/10.36227/techrxiv.174239957.76698574/v1","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},{"id":"pmh:oai:HAL:hal-05031543v1","doi":null,"is_oa":true,"landing_page_url":"https://hal.science/hal-05031543v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2025","raw_type":"info:eu-repo/semantics/preprint"},{"id":"pmh:oai:doaj.org/article:b0a6bfc7ebbe43e28d7d388e052a4aaf","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/b0a6bfc7ebbe43e28d7d388e052a4aaf","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,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Economy and Technology, Vol 4, Iss , Pp 266-283","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.ject.2025.11.001","doi":"https://doi.org/10.1016/j.ject.2025.11.001","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ject.2025.11.001","pdf_url":null,"source":{"id":"https://openalex.org/S4387289263","display_name":"Journal of Economy and Technology","issn_l":"2949-9488","issn":["2949-9488"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Economy and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.875},{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1999822211","https://openalex.org/W2060224594","https://openalex.org/W2590038026","https://openalex.org/W2911968972","https://openalex.org/W2922146383","https://openalex.org/W2956123709","https://openalex.org/W2995201943","https://openalex.org/W2997604048","https://openalex.org/W2998002701","https://openalex.org/W3000470572","https://openalex.org/W3003730808","https://openalex.org/W3016108562","https://openalex.org/W3049596687","https://openalex.org/W3081566540","https://openalex.org/W3083228182","https://openalex.org/W3092371431","https://openalex.org/W3126489268","https://openalex.org/W4221015417","https://openalex.org/W4283791586","https://openalex.org/W4285186567","https://openalex.org/W4295814235","https://openalex.org/W4321170165","https://openalex.org/W4389462109","https://openalex.org/W4391160762","https://openalex.org/W4391692748","https://openalex.org/W4391708122","https://openalex.org/W4392553889","https://openalex.org/W4393409607","https://openalex.org/W4401937191","https://openalex.org/W4404732516","https://openalex.org/W4406330295","https://openalex.org/W4409796583","https://openalex.org/W6941539926"],"related_works":["https://openalex.org/W4405845391","https://openalex.org/W7117577872","https://openalex.org/W4415516043","https://openalex.org/W4213182439","https://openalex.org/W7215177158","https://openalex.org/W4411207910","https://openalex.org/W4383647335","https://openalex.org/W1942925989","https://openalex.org/W4403498865","https://openalex.org/W7161950002"],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"neural":[3,65],"network":[4,66],"performance":[5,130],"optimization":[6,105,111],"for":[7,188,199],"New":[8],"York":[9],"City":[10],"taxi":[11,101,190],"trip":[12],"duration":[13,191],"prediction":[14,192],"to":[15,54,126],"address":[16],"critical":[17],"gaps":[18],"in":[19,36],"transportation":[20],"machine":[21],"learning,":[22],"where":[23],"reproducibility,":[24],"comprehensive":[25,171],"diagnostics,":[26,173],"and":[27,79,109,146,160,177,204],"computational":[28,178],"efficiency":[29],"remain":[30],"underreported.":[31],"The":[32,51,168],"research":[33],"addresses":[34],"limitations":[35],"prior":[37],"literature":[38],"that":[39],"emphasize":[40],"accuracy":[41],"without":[42],"standardized":[43],"preprocessing,":[44],"leakage":[45],"prevention,":[46],"or":[47],"systematic":[48,103],"cost-performance":[49],"analysis.":[50],"objective":[52],"was":[53],"develop":[55],"a":[56,195],"unified,":[57],"reproducible":[58],"framework":[59,169,198],"combining":[60],"an":[61],"auditable,":[62],"from-scratch":[63],"NumPy":[64],"with":[67,180],"production-grade":[68],"Keras":[69,165],"MLPs,":[70],"systematically":[71],"benchmarked":[72],"against":[73,131],"classical":[74],"models":[75],"under":[76],"identical":[77],"preprocessing":[78],"data":[80],"splits.":[81],"Methodology":[82],"encompasses":[83],"four":[84],"independent":[85],"phases:":[86],"theoretical":[87],"validation":[88,121],"using":[89],"XOR":[90,119],"classification,":[91],"statistical":[92,181],"benchmarking":[93],"through":[94],"rigorous":[95],"cross-validation":[96],"on":[97],"1.3":[98],"million":[99],"NYC":[100,189],"records,":[102],"architecture":[104,142],"across":[106],"small/medium/large":[107],"configurations,":[108],"advanced":[110],"achieving":[112,143],"state-of-the-art":[113],"performance.":[114],"Key":[115],"results":[116],"demonstrate":[117],"perfect":[118],"convergence":[120],"(loss":[122],"reduction":[123],"from":[124],"0.7065":[125],"0.0198),":[127],"competitive":[128],"baseline":[129],"Random":[132,157],"Forest":[133,158],"(93.3":[134],"%\u00b10.013":[135],"vs":[136],"90.5":[137],"%\u00b10.044":[138],"accuracy),":[139],"optimal":[140],"medium":[141],"0.459":[144],"RMSLE,":[145],"final":[147],"proposed":[148],"model":[149],"reaching":[150],"0.3092":[151],"RMSLE\u2014representing":[152],"31.8":[153],"%":[154,162],"improvement":[155],"over":[156,163],"(0.4536)":[159],"27.4":[161],"enhanced":[164],"baselines":[166],"(0.4261).":[167],"incorporates":[170],"residual":[172],"feature":[174],"importance":[175],"analysis,":[176],"profiling":[179],"significance":[182],"testing.":[183],"Results":[184],"establish":[185],"new":[186],"benchmarks":[187],"while":[193],"providing":[194],"methodologically":[196],"replicable":[197],"future":[200],"urban":[201],"mobility":[202],"analytics":[203],"operational":[205],"ETA":[206],"systems.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8}],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2025-11-27T00:00:00"}