Publication Information

{'id': 'https://openalex.org/W4387709413', 'doi': 'https://doi.org/10.1016/j.eswa.2023.122219', 'title': 'A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning', 'display_name': 'A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning', 'publication_year': 2023, 'publication_date': '2023-10-17', 'ids': {'openalex': 'https://openalex.org/W4387709413', 'doi': 'https://doi.org/10.1016/j.eswa.2023.122219'}, 'language': 'en', 'primary_location': {'is_oa': True, 'landing_page_url': 'https://doi.org/10.1016/j.eswa.2023.122219', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S13144211', 'display_name': 'Expert Systems with Applications', 'issn_l': '0957-4174', 'issn': ['0957-4174', '1873-6793'], 'is_oa': False, 'is_in_doaj': False, 'is_core': True, '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}, 'type': 'article', 'type_crossref': 'journal-article', 'indexed_in': ['crossref'], 'open_access': {'is_oa': True, 'oa_status': 'hybrid', 'oa_url': 'https://doi.org/10.1016/j.eswa.2023.122219', 'any_repository_has_fulltext': True}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5082971999', 'display_name': 'Yuyan Pan', 'orcid': 'https://orcid.org/0000-0003-1607-7179'}, 'institutions': [{'id': 'https://openalex.org/I37796252', 'display_name': 'Beijing University of Technology', 'ror': 'https://ror.org/037b1pp87', 'country_code': 'CN', 'type': 'education', 'lineage': ['https://openalex.org/I37796252']}], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Yuyan Annie Pan', 'raw_affiliation_strings': ['Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China'], 'affiliations': [{'raw_affiliation_string': 'Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China', 'institution_ids': ['https://openalex.org/I37796252']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5103102498', 'display_name': 'Jifu Guo', 'orcid': 'https://orcid.org/0000-0003-2329-5668'}, 'institutions': [], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Jifu Guo', 'raw_affiliation_strings': ['Beijing Transport Institute, Beijing, China'], 'affiliations': [{'raw_affiliation_string': 'Beijing Transport Institute, Beijing, China', 'institution_ids': []}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5100702787', 'display_name': 'Yanyan Chen', 'orcid': 'https://orcid.org/0000-0002-7068-4669'}, 'institutions': [{'id': 'https://openalex.org/I37796252', 'display_name': 'Beijing University of Technology', 'ror': 'https://ror.org/037b1pp87', 'country_code': 'CN', 'type': 'education', 'lineage': ['https://openalex.org/I37796252']}], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Yanyan Chen', 'raw_affiliation_strings': ['Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China'], 'affiliations': [{'raw_affiliation_string': 'Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China', 'institution_ids': ['https://openalex.org/I37796252']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5043567322', 'display_name': 'Qixiu Cheng', 'orcid': 'https://orcid.org/0000-0001-8772-7584'}, 'institutions': [{'id': 'https://openalex.org/I36234482', 'display_name': 'University of Bristol', 'ror': 'https://ror.org/0524sp257', 'country_code': 'GB', 'type': 'education', 'lineage': ['https://openalex.org/I36234482']}], 'countries': ['GB'], 'is_corresponding': True, 'raw_author_name': 'Qixiu Cheng', 'raw_affiliation_strings': ['University of Bristol Business School, University of Bristol, Bristol, UK'], 'affiliations': [{'raw_affiliation_string': 'University of Bristol Business School, University of Bristol, Bristol, UK', 'institution_ids': ['https://openalex.org/I36234482']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5100362633', 'display_name': 'Wenhao Li', 'orcid': 'https://orcid.org/0000-0003-1420-8163'}, 'institutions': [{'id': 'https://openalex.org/I37796252', 'display_name': 'Beijing University of Technology', 'ror': 'https://ror.org/037b1pp87', 'country_code': 'CN', 'type': 'education', 'lineage': ['https://openalex.org/I37796252']}], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Wenhao Li', 'raw_affiliation_strings': ['Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China'], 'affiliations': [{'raw_affiliation_string': 'Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China', 'institution_ids': ['https://openalex.org/I37796252']}]}, {'author_position': 'last', 'author': {'id': 'https://openalex.org/A5061699202', 'display_name': 'Yanyue Liu', 'orcid': None}, 'institutions': [{'id': 'https://openalex.org/I4210139553', 'display_name': 'Research Institute of Highway', 'ror': 'https://ror.org/0335kqk33', 'country_code': 'CN', 'type': 'government', 'lineage': ['https://openalex.org/I4210127216', 'https://openalex.org/I4210139553']}, {'id': 'https://openalex.org/I4210127216', 'display_name': 'Ministry of Transport', 'ror': 'https://ror.org/031wq1t38', 'country_code': 'CN', 'type': 'government', 'lineage': ['https://openalex.org/I4210127216']}], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Yanyue Liu', 'raw_affiliation_strings': ['ITS Center, Research Institute of Highway Ministry of Transport, Beijing, China'], 'affiliations': [{'raw_affiliation_string': 'ITS Center, Research Institute of Highway Ministry of Transport, Beijing, China', 'institution_ids': ['https://openalex.org/I4210139553', 'https://openalex.org/I4210127216']}]}], 'institution_assertions': [], 'countries_distinct_count': 2, 'institutions_distinct_count': 4, 'corresponding_author_ids': ['https://openalex.org/A5043567322'], 'corresponding_institution_ids': ['https://openalex.org/I36234482'], 'apc_list': {'value': 3220, 'currency': 'USD', 'value_usd': 3220, 'provenance': 'doaj'}, 'apc_paid': {'value': 3220, 'currency': 'USD', 'value_usd': 3220, 'provenance': 'doaj'}, 'fwci': 4.498, 'has_fulltext': True, 'fulltext_origin': 'pdf', 'cited_by_count': 16, 'citation_normalized_percentile': {'value': 0.999922, 'is_in_top_1_percent': True, 'is_in_top_10_percent': True}, 'cited_by_percentile_year': {'min': 97, 'max': 98}, 'biblio': {'volume': '238', 'issue': None, 'first_page': '122219', 'last_page': '122219'}, 'is_retracted': False, 'is_paratext': False, 'primary_topic': {'id': 'https://openalex.org/T11344', 'display_name': 'Traffic Prediction and Management Techniques', 'score': 1.0, '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'}}, 'topics': [{'id': 'https://openalex.org/T11344', 'display_name': 'Traffic Prediction and Management Techniques', 'score': 1.0, '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/T10524', 'display_name': 'Traffic control and management', 'score': 0.9991, 'subfield': {'id': 'https://openalex.org/subfields/2207', 'display_name': 'Control and Systems Engineering'}, '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/T10698', 'display_name': 'Transportation Planning and Optimization', 'score': 0.9975, '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/benchmark', 'display_name': 'Benchmark (surveying)', 'score': 0.45359462}], 'concepts': [{'id': 'https://openalex.org/C41008148', 'wikidata': 'https://www.wikidata.org/wiki/Q21198', 'display_name': 'Computer science', 'level': 0, 'score': 0.77222806}, {'id': 'https://openalex.org/C98763669', 'wikidata': 'https://www.wikidata.org/wiki/Q176645', 'display_name': 'Markov chain', 'level': 2, 'score': 0.6689732}, {'id': 'https://openalex.org/C163836022', 'wikidata': 'https://www.wikidata.org/wiki/Q6771326', 'display_name': 'Markov model', 'level': 3, 'score': 0.49171856}, {'id': 'https://openalex.org/C159886148', 'wikidata': 'https://www.wikidata.org/wiki/Q176645', 'display_name': 'Markov process', 'level': 2, 'score': 0.46687692}, {'id': 'https://openalex.org/C73555534', 'wikidata': 'https://www.wikidata.org/wiki/Q622825', 'display_name': 'Cluster analysis', 'level': 2, 'score': 0.4613822}, {'id': 'https://openalex.org/C185798385', 'wikidata': 'https://www.wikidata.org/wiki/Q1161707', 'display_name': 'Benchmark (surveying)', 'level': 2, 'score': 0.45359462}, {'id': 'https://openalex.org/C119857082', 'wikidata': 'https://www.wikidata.org/wiki/Q2539', 'display_name': 'Machine learning', 'level': 1, 'score': 0.44107997}, {'id': 'https://openalex.org/C24338571', 'wikidata': 'https://www.wikidata.org/wiki/Q2566298', 'display_name': 'Autoregressive integrated moving average', 'level': 3, 'score': 0.4297049}, {'id': 'https://openalex.org/C54907487', 'wikidata': 'https://www.wikidata.org/wiki/Q7915688', 'display_name': 'Variable-order Markov model', 'level': 4, 'score': 0.429485}, {'id': 'https://openalex.org/C154945302', 'wikidata': 'https://www.wikidata.org/wiki/Q11660', 'display_name': 'Artificial intelligence', 'level': 1, 'score': 0.42076507}, {'id': 'https://openalex.org/C124101348', 'wikidata': 'https://www.wikidata.org/wiki/Q172491', 'display_name': 'Data mining', 'level': 1, 'score': 0.4158873}, {'id': 'https://openalex.org/C11413529', 'wikidata': 'https://www.wikidata.org/wiki/Q8366', 'display_name': 'Algorithm', 'level': 1, 'score': 0.35324842}, {'id': 'https://openalex.org/C151406439', 'wikidata': 'https://www.wikidata.org/wiki/Q186588', 'display_name': 'Time series', 'level': 2, 'score': 0.31218734}, {'id': 'https://openalex.org/C105795698', 'wikidata': 'https://www.wikidata.org/wiki/Q12483', 'display_name': 'Statistics', 'level': 1, 'score': 0.1699849}, {'id': 'https://openalex.org/C33923547', 'wikidata': 'https://www.wikidata.org/wiki/Q395', 'display_name': 'Mathematics', 'level': 0, 'score': 0.14532712}, {'id': 'https://openalex.org/C13280743', 'wikidata': 'https://www.wikidata.org/wiki/Q131089', 'display_name': 'Geodesy', 'level': 1, 'score': 0.0}, {'id': 'https://openalex.org/C205649164', 'wikidata': 'https://www.wikidata.org/wiki/Q1071', 'display_name': 'Geography', 'level': 0, 'score': 0.0}], 'mesh': [], 'locations_count': 3, 'locations': [{'is_oa': True, 'landing_page_url': 'https://doi.org/10.1016/j.eswa.2023.122219', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S13144211', 'display_name': 'Expert Systems with Applications', 'issn_l': '0957-4174', 'issn': ['0957-4174', '1873-6793'], 'is_oa': False, 'is_in_doaj': False, 'is_core': True, '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}, {'is_oa': True, 'landing_page_url': 'https://hdl.handle.net/1983/b9efbdc7-5605-44fe-b5e4-c61867f35d6b', 'pdf_url': 'https://research-information.bris.ac.uk/files/380917893/Pan-2024-A_fundamental_diagram_based_hybrid_framework_for_traffic_flow_estimation_and_prediction_by_combining_a_Markovian_model_with_deep_learn.pdf', 'source': {'id': 'https://openalex.org/S4306400895', 'display_name': 'Bristol Research (University of Bristol)', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I36234482', 'host_organization_name': 'University of Bristol', 'host_organization_lineage': ['https://openalex.org/I36234482'], 'host_organization_lineage_names': ['University of Bristol'], 'type': 'repository'}, 'license': 'cc-by', 'license_id': 'https://openalex.org/licenses/cc-by', 'version': 'publishedVersion', 'is_accepted': True, 'is_published': True}, {'is_oa': True, 'landing_page_url': 'https://research-information.bris.ac.uk/ws/files/380917893/Pan-2024-A_fundamental_diagram_based_hybrid_framework_for_traffic_flow_estimation_and_prediction_by_combining_a_Markovian_model_with_deep_learn.pdf', 'pdf_url': 'https://research-information.bris.ac.uk/ws/files/380917893/Pan-2024-A_fundamental_diagram_based_hybrid_framework_for_traffic_flow_estimation_and_prediction_by_combining_a_Markovian_model_with_deep_learn.pdf', 'source': {'id': 'https://openalex.org/S4306400895', 'display_name': 'Bristol Research (University of Bristol)', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I36234482', 'host_organization_name': 'University of Bristol', 'host_organization_lineage': ['https://openalex.org/I36234482'], 'host_organization_lineage_names': ['University of Bristol'], 'type': 'repository'}, 'license': 'cc-by', 'license_id': 'https://openalex.org/licenses/cc-by', 'version': 'publishedVersion', 'is_accepted': True, 'is_published': True}], 'best_oa_location': {'is_oa': True, 'landing_page_url': 'https://doi.org/10.1016/j.eswa.2023.122219', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S13144211', 'display_name': 'Expert Systems with Applications', 'issn_l': '0957-4174', 'issn': ['0957-4174', '1873-6793'], 'is_oa': False, 'is_in_doaj': False, 'is_core': True, '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}, 'sustainable_development_goals': [{'display_name': 'Sustainable cities and communities', 'id': 'https://metadata.un.org/sdg/11', 'score': 0.7}], 'grants': [], 'datasets': [], 'versions': [], 'referenced_works_count': 66, 'referenced_works': ['https://openalex.org/W1579224301', 'https://openalex.org/W1974157418', 'https://openalex.org/W1999975133', 'https://openalex.org/W2002033255', 'https://openalex.org/W2004353783', 'https://openalex.org/W2029436115', 'https://openalex.org/W2049952439', 'https://openalex.org/W2075278989', 'https://openalex.org/W2097792015', 'https://openalex.org/W2103312443', 'https://openalex.org/W2111991989', 'https://openalex.org/W2115868680', 'https://openalex.org/W2131739422', 'https://openalex.org/W2149866111', 'https://openalex.org/W2171612762', 'https://openalex.org/W2190353863', 'https://openalex.org/W2337353068', 'https://openalex.org/W2535177756', 'https://openalex.org/W2553942547', 'https://openalex.org/W2745110207', 'https://openalex.org/W2761646508', 'https://openalex.org/W2785024182', 'https://openalex.org/W2795276038', 'https://openalex.org/W2883697400', 'https://openalex.org/W2884415573', 'https://openalex.org/W2892986943', 'https://openalex.org/W2896827527', 'https://openalex.org/W2900489907', 'https://openalex.org/W2914743966', 'https://openalex.org/W2936721453', 'https://openalex.org/W2937032869', 'https://openalex.org/W2942406509', 'https://openalex.org/W2958321630', 'https://openalex.org/W2972303719', 'https://openalex.org/W2985685076', 'https://openalex.org/W2996366953', 'https://openalex.org/W2999712306', 'https://openalex.org/W3014532303', 'https://openalex.org/W3033654738', 'https://openalex.org/W3035338169', 'https://openalex.org/W3085866332', 'https://openalex.org/W3114222433', 'https://openalex.org/W3117049966', 'https://openalex.org/W3124050193', 'https://openalex.org/W3151697167', 'https://openalex.org/W3157443152', 'https://openalex.org/W3184493524', 'https://openalex.org/W3196516259', 'https://openalex.org/W3198805468', 'https://openalex.org/W3206563773', 'https://openalex.org/W32452329', 'https://openalex.org/W4221091928', 'https://openalex.org/W4223897150', 'https://openalex.org/W4241043349', 'https://openalex.org/W4241648823', 'https://openalex.org/W4308354692', 'https://openalex.org/W4308580987', 'https://openalex.org/W4313049033', 'https://openalex.org/W4320932567', 'https://openalex.org/W4360616077', 'https://openalex.org/W4366779109', 'https://openalex.org/W4367301163', 'https://openalex.org/W4383312016', 'https://openalex.org/W4385287316', 'https://openalex.org/W4386263530', 'https://openalex.org/W4386747882'], 'related_works': ['https://openalex.org/W4386839846', 'https://openalex.org/W4312561791', 'https://openalex.org/W3175321409', 'https://openalex.org/W3126873283', 'https://openalex.org/W3022014775', 'https://openalex.org/W2540690809', 'https://openalex.org/W2393621008', 'https://openalex.org/W2353273130', 'https://openalex.org/W2129435535', 'https://openalex.org/W2100055350'], 'abstract_inverted_index': {'Accurate': [0], 'traffic': [1, 30, 109, 130, 182, 270], 'congestion': [2, 31], 'estimation': [3, 32, 170], 'and': [4, 14, 33, 42, 50, 70, 97, 107, 118, 132, 171, 180, 196, 225, 246], 'prediction': [5, 34, 172], 'are': [6], 'critical': [7], 'building': [8], 'blocks': [9], 'for': [10], 'smart': [11], 'trip': [12], 'planning': [13], 'rerouting': [15], 'decisions': [16], 'in': [17, 115, 178, 194, 205, 236, 241, 248], 'transportation': [18], 'systems.': [19], 'Over': [20], 'the': [21, 65, 72, 82, 91, 125, 134, 139, 147, 153, 159, 169, 214, 228, 258, 262], 'decades,': [22], 'there': [23, 55], 'have': [24], 'been': [25], 'many': [26], 'studies': [27, 192], 'focusing': [28], 'on': [29], 'with': [35], 'different': [36, 68, 75], 'statistical': [37, 113], 'approaches': [38], '(e.g.,': [39, 46], 'Markov': [40, 95, 135, 160, 215], 'chain)': [41], 'machine': [43], 'learning': [44], 'models': [45, 69, 211], 'clustering,': [47], 'Bayesian': [48], 'networks,': [49], 'artificial': [51], 'neural': [52], 'networks).': [53], 'However,': [54], 'is': [56, 104, 184], 'a': [57, 60, 85, 164, 202], 'lack': [58], 'of': [59, 67, 74, 233, 269], 'unified': [61], 'framework': [62], 'to': [63, 105, 127, 137, 150, 167, 208], 'address': [64], 'mechanisms': [66], 'integrate': [71], 'advantages': [73], 'methods': [76], 'through': [77], 'combinations.': [78], 'This': [79], 'paper': [80], 'introduces': [81], 'FD-Markov-LSTM': [83, 122, 229, 259], 'model,': [84, 216, 218, 221, 224], 'hybrid': [86], 'interpretable': [87], 'approach': [88], 'that': [89, 257], 'combines': [90], 'fundamental': [92], 'diagram': [93], '(FD),': [94], 'chain,': [96], 'long': [98], 'short-term': [99], 'memory': [100], '(LSTM).': [101], 'The': [102, 121, 174, 199], 'aim': [103], 'estimate': [106], 'predict': [108], 'states': [110, 131], 'by': [111, 158], 'integrating': [112], 'data': [114, 188], 'both': [116], 'congested': [117], 'uncongested': [119], 'scenarios.': [120], 'model': [123, 149, 162, 230, 260], 'leverages': [124], 'FD': [126], 'identify': [128], 'hierarchical': [129], 'utilizes': [133], 'process': [136], 'capture': [138, 152], 'probabilistic': [140], 'transitions': [141], 'between': [142], 'these': [143], 'states.': [144], 'We': [145], 'employ': [146], 'LSTM': [148], 'further': [151], 'residual': [154], 'time': [155], 'series': [156], 'produced': [157], 'chain': [161], '(assuming': [163], 'memoryless': [165], 'property)': [166], 'enhance': [168], 'performance.': [173], 'proposed': [175], "model's": [176], 'accuracy': [177, 206], 'estimating': [179], 'predicting': [181], 'flow': [183], 'evaluated': [185], 'using': [186], 'empirical': [187], 'from': [189], 'three': [190], 'case': [191], 'conducted': [193], 'Beijing': [195], 'Los': [197], 'Angeles.': [198], 'results': [200, 254], 'highlight': [201], 'significant': [203], 'improvement': [204], 'compared': [207], 'classical': [209], 'benchmark': [210, 263], 'such': [212], 'as': [213], 'ARIMA': [217], 'k-Nearest': [219], 'Neighbor': [220], 'Random': [222], 'Forest': [223], 'LSTM.': [226], 'Specifically,': [227], 'achieves': [231], 'reductions': [232], 'over': [234], '39%': [235], 'mean': [237, 243, 249], 'absolute': [238, 250], 'error,': [239, 245], '35%': [240], 'root': [242], 'squared': [244], '7.4%': [247], 'percentage': [251], 'error.': [252], 'These': [253], 'clearly': [255], 'demonstrate': [256], 'outperforms': [261], 'models,': [264], 'enabling': [265], 'more': [266], 'precise': [267], 'predictions': [268], 'flow.': [271]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W4387709413', 'counts_by_year': [{'year': 2024, 'cited_by_count': 15}, {'year': 2023, 'cited_by_count': 1}], 'updated_date': '2025-01-05T11:10:50.323581', 'created_date': '2023-10-18'}