{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:24:45Z","timestamp":1773955485011,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oper Res Int J"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s12351-024-00851-8","type":"journal-article","created":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T15:02:06Z","timestamp":1726412526000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Vehicle routing Problem for cold chain logistics based on data fusion technology to predict travel time"],"prefix":"10.1007","volume":"24","author":[{"given":"Qinyang","family":"Bai","sequence":"first","affiliation":[]},{"given":"Yuxiang","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Xueqin","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Zhili","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,15]]},"reference":[{"key":"851_CR1","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s00291-010-0223-2","volume":"32","author":"R Akkerman","year":"2010","unstructured":"Akkerman R, Farahani P, Grunow M (2010) Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. OR Spectr 32:863\u2013904","journal-title":"OR Spectr"},{"key":"851_CR2","doi-asserted-by":"publisher","first-page":"106341","DOI":"10.1016\/j.cie.2020.106341","volume":"142","author":"N Al Theeb","year":"2020","unstructured":"Al Theeb N, Smadi HJ, Al-Hawari TH, Aljarrah MH (2020) Optimization of vehicle routing with inventory allocation problems in cold supply chain logistics. Comput Ind Eng 142:106341","journal-title":"Comput Ind Eng"},{"key":"851_CR3","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.cie.2013.11.006","volume":"67","author":"P Amorim","year":"2014","unstructured":"Amorim P, Almada-Lobo B (2014) The impact of food perishability issues in the vehicle routing problem. Comput Ind Eng 67:223\u2013233","journal-title":"Comput Ind Eng"},{"key":"851_CR4","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1108\/IMDS-06-2020-0345","volume":"122","author":"Q Bai","year":"2022","unstructured":"Bai Q, Yin X, Lim MK, Dong C (2022) Low-carbon vrp for cold chain logistics considering real-time traffic conditions in the road network. Ind Manag Data Syst 122:521\u2013543","journal-title":"Ind Manag Data Syst"},{"key":"851_CR5","doi-asserted-by":"publisher","first-page":"107663","DOI":"10.1016\/j.cie.2021.107663","volume":"161","author":"J Chen","year":"2021","unstructured":"Chen J, Liao W, Yu C (2021) Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission. Comput Ind Eng 161:107663","journal-title":"Comput Ind Eng"},{"key":"851_CR6","unstructured":"Cheu RL, Lee DH, Xie C (2001) An arterial speed estimation model fusing data from stationary and mobile sensors, In: ITSC 2001, 2001 IEEE intelligent transportation systems, proceedings (Cat. No. 01TH8585). IEEE, pp 573\u2013578"},{"key":"851_CR7","unstructured":"Cheu RL, Lee DH, Xie C (2002) An arterial speed estimation model fusing data from stationary and mobile sensors. In: Intelligent transportation systems, IEEE"},{"key":"851_CR8","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ijpe.2018.09.018","volume":"212","author":"A Diabat","year":"2019","unstructured":"Diabat A, Jabbarzadeh A, Khosrojerdi A (2019) A perishable product supply chain network design problem with reliability and disruption considerations. Int J Prod Econ 212:125\u2013138","journal-title":"Int J Prod Econ"},{"key":"851_CR9","doi-asserted-by":"publisher","DOI":"10.3963\/j.issn.1674-4861.2004.04.011","author":"Z Gao","year":"2004","unstructured":"Gao Z, Zhu J, Huang C, Dong D (2004) A method of travel time survey and prediction. J Transp Inf Saf. https:\/\/doi.org\/10.3963\/j.issn.1674-4861.2004.04.011","journal-title":"J Transp Inf Saf"},{"key":"851_CR10","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.cie.2017.02.002","volume":"106","author":"J Guo","year":"2017","unstructured":"Guo J, Wang X, Fan S, Gen M (2017) Forward and reverse logistics network and route planning under the environment of low-carbon emissions: A case study of shanghai fresh food e-commerce enterprises. Comput Ind Eng 106:351\u2013360","journal-title":"Comput Ind Eng"},{"key":"851_CR11","doi-asserted-by":"publisher","first-page":"103332","DOI":"10.1016\/j.tre.2023.103332","volume":"180","author":"X Guo","year":"2023","unstructured":"Guo X, He J, Yu H, Liu M (2023) Carbon peak simulation and peak pathway analysis for hub-and-spoke container intermodal network. Transp Res Part E Logist Transp Rev 180:103332","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"851_CR12","first-page":"259","volume":"55","author":"K Kang","year":"2019","unstructured":"Kang K, Han J, Pu W, Ma Y (2019) Optimization research on cold chain distribution routes considering carbon emissions for fresh agricultural products. Comput Eng Appl 55:259\u2013265","journal-title":"Comput Eng Appl"},{"key":"851_CR13","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1080\/15568318.2018.1471555","volume":"13","author":"HW Kim","year":"2019","unstructured":"Kim HW, Joo GH, Lee DH (2019) Multi-period heterogeneous vehicle routing considering carbon emission trading. Int J Sustain Transp 13:340\u2013349","journal-title":"Int J Sustain Transp"},{"key":"851_CR14","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.eswa.2015.11.005","volume":"46","author":"K Kim","year":"2016","unstructured":"Kim K, Kim H, Kim SK, Jung JY (2016) i-rm: an intelligent risk management framework for context-aware ubiquitous cold chain logistics. Expert Syst Appl 46:463\u2013473","journal-title":"Expert Syst Appl"},{"key":"851_CR15","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.trc.2017.04.002","volume":"79","author":"BA Kumar","year":"2017","unstructured":"Kumar BA, Vanajakshi L, Subramanian SC (2017) Bus travel time prediction using a time-space discretization approach. Transp Res Part C Emerg Technol 79:308\u2013332","journal-title":"Transp Res Part C Emerg Technol"},{"key":"851_CR16","doi-asserted-by":"publisher","first-page":"358","DOI":"10.3846\/16484142.2015.1100676","volume":"32","author":"SV Kumar","year":"2017","unstructured":"Kumar SV, Chaitanya Dogiparthi K, Vanajakshi L, Subramanian SC (2017) Integration of exponential smoothing with state space formulation for bus travel time and arrival time prediction. Transport 32:358\u2013367","journal-title":"Transport"},{"key":"851_CR17","doi-asserted-by":"publisher","first-page":"119777","DOI":"10.1016\/j.eswa.2023.119777","volume":"221","author":"R Kuo","year":"2023","unstructured":"Kuo R, Edbert E, Zulvia FE, Lu SH (2023) Applying NSGA-ii to vehicle routing problem with drones considering makespan and carbon emission. Expert Syst Appl 221:119777","journal-title":"Expert Syst Appl"},{"key":"851_CR18","doi-asserted-by":"crossref","unstructured":"Li N, Li G (2022) Hybrid partheno-genetic algorithm for multi-depot perishable food delivery problem with mixed time windows. Annals of Operations Research , 1\u201332","DOI":"10.1007\/s10479-022-04747-8"},{"key":"851_CR19","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1108\/IMDS-07-2018-0314","volume":"119","author":"Y Li","year":"2019","unstructured":"Li Y, Lim MK, Tseng ML (2019) A green vehicle routing model based on modified particle swarm optimization for cold chain logistics. Ind Manag Data Syst 119:473\u2013494","journal-title":"Ind Manag Data Syst"},{"key":"851_CR20","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1108\/IMDS-10-2021-0607","volume":"122","author":"MK Lim","year":"2022","unstructured":"Lim MK, Li Y, Wang C, Tseng ML (2022) Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine. Ind Manag Data Syst 122:819\u2013840","journal-title":"Ind Manag Data Syst"},{"key":"851_CR21","doi-asserted-by":"publisher","first-page":"118319","DOI":"10.1016\/j.eswa.2022.118319","volume":"209","author":"W Lin","year":"2022","unstructured":"Lin W, Wei H, Nian D (2022) Integrated Ann-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm. Expert Syst Appl 209:118319","journal-title":"Expert Syst Appl"},{"key":"851_CR22","doi-asserted-by":"publisher","first-page":"104813","DOI":"10.1016\/j.knosys.2019.06.021","volume":"188","author":"C Liu","year":"2020","unstructured":"Liu C, Kou G, Zhou X, Peng Y, Sheng H, Alsaadi FE (2020) Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach. Knowl-Based Syst 188:104813","journal-title":"Knowl-Based Syst"},{"key":"851_CR23","first-page":"1824","volume":"28","author":"X Ma","year":"2016","unstructured":"Ma X, Liu T, Yang P, Jiang R (2016) Vehicle routing optimization model of cold chain logistics based on stochastic demand. J Syst Simul 28:1824","journal-title":"J Syst Simul"},{"key":"851_CR24","doi-asserted-by":"publisher","first-page":"109601","DOI":"10.1016\/j.cie.2023.109601","volume":"185","author":"F Menares","year":"2023","unstructured":"Menares F, Montero E, Paredes-Belmar G, Bronfman A (2023) A bi-objective time-dependent vehicle routing problem with delivery failure probabilities. Comput Ind Eng 185:109601","journal-title":"Comput Ind Eng"},{"key":"851_CR25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.foreco.2013.06.010","volume":"317","author":"RD Ottmar","year":"2014","unstructured":"Ottmar RD (2014) Wildland fire emissions, carbon, and climate: modeling fuel consumption. For Ecol Manag 317:41\u201350","journal-title":"For Ecol Manag"},{"key":"851_CR26","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1109\/TITS.2004.833765","volume":"5","author":"J Rice","year":"2004","unstructured":"Rice J, Van Zwet E (2004) A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst 5:200\u2013207","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"851_CR27","unstructured":"Robinson S (2005) The development and application of an urban link travel time model using data derived from inductive loop detectors. Department of Civil and Environmental Engineering, Imperial College London"},{"key":"851_CR28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.jfoodeng.2015.08.027","volume":"169","author":"BD Song","year":"2016","unstructured":"Song BD, Ko YD (2016) A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery. J Food Eng 169:61\u201371","journal-title":"J Food Eng"},{"key":"851_CR29","doi-asserted-by":"publisher","first-page":"107849","DOI":"10.1016\/j.ijpe.2020.107849","volume":"231","author":"HM Stellingwerf","year":"2021","unstructured":"Stellingwerf HM, Groeneveld LH, Laporte G, Kanellopoulos A, Bloemhof JM, Behdani B (2021) The quality-driven vehicle routing problem: Model and application to a case of cooperative logistics. Int J Prod Econ 231:107849","journal-title":"Int J Prod Econ"},{"key":"851_CR30","first-page":"118","volume":"29","author":"H Tang","year":"2021","unstructured":"Tang H, Tang H, Zhu X (2021) Research on low-carbon vehicle routing problem based on modified ant colony algorithm. Chin J Manag Sci 29:118\u201327","journal-title":"Chin J Manag Sci"},{"key":"851_CR31","doi-asserted-by":"publisher","first-page":"04020039","DOI":"10.1061\/JTEPBS.0000359","volume":"146","author":"Q Tang","year":"2020","unstructured":"Tang Q, Hu X (2020) Modeling individual travel time with back propagation neural network approach for advanced traveler information systems. J Transp Eng Part A Syst 146:04020039","journal-title":"J Transp Eng Part A Syst"},{"key":"851_CR32","doi-asserted-by":"publisher","first-page":"86","DOI":"10.3390\/ijerph15010086","volume":"15","author":"S Wang","year":"2018","unstructured":"Wang S, Tao F, Shi Y (2018) Optimization of location-routing problem for cold chain logistics considering carbon footprint. Int J Environ Res Public Health 15:86","journal-title":"Int J Environ Res Public Health"},{"key":"851_CR33","unstructured":"Wu Q (2015) Travel time estimation and prediction for urban road networks. Zhejiang University, Zhejiang"},{"key":"851_CR34","first-page":"172","volume":"37","author":"Y Wu","year":"2017","unstructured":"Wu Y, Ma Z (2017) Time-dependent production-delivery problem with time windows for perishable foods. Syst Eng Theory Pract 37:172\u2013181","journal-title":"Syst Eng Theory Pract"},{"key":"851_CR35","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.tre.2016.01.011","volume":"88","author":"Y Xiao","year":"2016","unstructured":"Xiao Y, Konak A (2016) The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transp Res Part E Logist Transp Rev 88:146\u2013166","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"851_CR36","doi-asserted-by":"publisher","first-page":"16810","DOI":"10.1109\/TITS.2021.3095095","volume":"23","author":"L Xing","year":"2021","unstructured":"Xing L, Liu W (2021) A data fusion powered bi-directional long short term memory model for predicting multi-lane short term traffic flow. IEEE Trans Intell Transp Syst 23:16810\u201316819","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"851_CR37","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/s10479-017-2567-3","volume":"269","author":"S Zhang","year":"2018","unstructured":"Zhang S, Gajpal Y, Appadoo S (2018) A meta-heuristic for capacitated green vehicle routing problem. Ann Oper Res 269:753\u2013771","journal-title":"Ann Oper Res"},{"key":"851_CR38","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.trc.2017.10.010","volume":"85","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Wang Y, Chen P, He Z, Yu G (2017) Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns. Transp Res Part C Emerg Technol 85:476\u2013493","journal-title":"Transp Res Part C Emerg Technol"},{"key":"851_CR39","first-page":"224","volume":"56","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Li X, Zhou X, Liu C (2020) Research on green vehicle routing problem of cold chain distribution: considering traffic congestion. Comput Eng Appl 56:224\u2013231","journal-title":"Comput Eng Appl"},{"key":"851_CR40","doi-asserted-by":"publisher","first-page":"6579","DOI":"10.1080\/00207543.2020.1821118","volume":"59","author":"F Zheng","year":"2021","unstructured":"Zheng F, Pang Y, Xu Y, Liu M (2021) Heuristic algorithms for truck scheduling of cross-docking operations in cold-chain logistics. Int J Prod Res 59:6579\u20136600","journal-title":"Int J Prod Res"},{"key":"851_CR41","doi-asserted-by":"crossref","unstructured":"Zheng Y, Xie M, Wang X (2019) Research on passenger flow forecast of hangzhou metro based on lstm-svr, In: 2019 international conference on artificial intelligence and advanced manufacturing (AIAM), IEEE. pp 273\u2013276","DOI":"10.1109\/AIAM48774.2019.00061"},{"key":"851_CR42","doi-asserted-by":"publisher","first-page":"6115","DOI":"10.1080\/00207543.2018.1533260","volume":"57","author":"L Zhu","year":"2019","unstructured":"Zhu L, Hu D (2019) Study on the vehicle routing problem considering congestion and emission factors. Int J Prod Res 57:6115\u20136129","journal-title":"Int J Prod Res"}],"container-title":["Operational Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12351-024-00851-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12351-024-00851-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12351-024-00851-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T15:02:23Z","timestamp":1734102143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12351-024-00851-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,15]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["851"],"URL":"https:\/\/doi.org\/10.1007\/s12351-024-00851-8","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1038\/s41598-025-06946-2","asserted-by":"object"}]},"ISSN":["1109-2858","1866-1505"],"issn-type":[{"value":"1109-2858","type":"print"},{"value":"1866-1505","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,15]]},"assertion":[{"value":"24 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article. They did not receive support from any organization for the submitted work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that this study does not involve human or animal participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"55"}}