{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:26:37Z","timestamp":1760059597629,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Provincial Natural Science Fund","award":["LH2023G002"],"award-info":[{"award-number":["LH2023G002"]}]},{"name":"Natural Science Foundation of Heilongjiang Province","award":["LH2023G002"],"award-info":[{"award-number":["LH2023G002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>As a crucial component of the Belt and Road Initiative (BRI), China Railway Express (CR Express) plays a pivotal role in enhancing regional connectivity and economic integration. However, the systematic evaluation of CR Express node cities remains understudied, hindering the optimization of logistics networks and sustainable development goals. This study pioneers a data-driven approach by integrating multi-source geospatial data and advanced machine learning algorithms to develop a comprehensive evaluation framework spanning five critical dimensions: economic vitality, ecological sustainability, logistics capacity, network connectivity, and policy support. By comparing the evaluation performance of six machine learning models, an optimal decision-making model is identified, and the evaluation indicators are rigorously screened to provide robust decision-support for the establishment of CR Express assembly centers. The Random Forest model outperformed comparative algorithms with 99.5% prediction accuracy (8.33% higher than conventional classification models), particularly in handling multi-dimensional interactions between urban development factors. Feature importance analysis identified 11 decisive indicators from node city evaluation empirical indicators, where CR Express trade volume (weight = 0.1269), logistics hub classification (weight = 0.1091), and operational frequency (weight = 0.0980) emerged as the top three predictors. Spatial predictions highlight five strategic cities (Changsha, Wuhan, Shenyang, Jinan, Hefei) as prime candidates for CR Express assembly centers, providing actionable insights for national logistics planning under the BRI framework.<\/jats:p>","DOI":"10.3390\/ijgi14070237","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T04:50:05Z","timestamp":1750654205000},"page":"237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9571-6007","authenticated-orcid":false,"given":"Chenglin","family":"Ma","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengwei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenchao","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"ref_1","first-page":"305","article-title":"Evaluation of Market Share of New International Land-Sea Trade Corridor in Different Periods of COVID-19","volume":"23","author":"Guo","year":"2023","journal-title":"JST"},{"doi-asserted-by":"crossref","unstructured":"Choi, K.S. (2021). The current status and challenges of China Railway Express (CRE) as a key sustainability policy component of the Belt and Road Initiative. Sustainability, 13.","key":"ref_2","DOI":"10.3390\/su13095017"},{"key":"ref_3","first-page":"70","article-title":"Status, Problems and Suggestions on Development of Sino-Europe Block Trains","volume":"37","author":"Wang","year":"2015","journal-title":"Compr. Transp."},{"key":"ref_4","first-page":"10","article-title":"Optimization of Site Selection for China Railway Express Assembly Center","volume":"45","author":"Liu","year":"2023","journal-title":"Railw. Transp. Econ."},{"key":"ref_5","first-page":"191","article-title":"Key Node Identification of China Railway Express Transportation Network Based on Multi-layer Complex Network","volume":"22","author":"Feng","year":"2023","journal-title":"JST"},{"key":"ref_6","first-page":"2841","article-title":"Optimization of international transportation network of China Railway Express under demand uncertainty","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Railw. Sci. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104130","DOI":"10.1016\/j.tra.2024.104130","article-title":"Resilience analysis of the integrated China-Europe freight transportation network under heterogeneous demands","volume":"186","author":"Zhou","year":"2024","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.tra.2020.07.003","article-title":"Importance rankings of nodes in the China Railway Express network under the Belt and Road Initiative","volume":"139","author":"Zhang","year":"2020","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126872","DOI":"10.1016\/j.jclepro.2021.126872","article-title":"Selection of consolidation center locations for China railway express to reduce greenhouse gas emission","volume":"305","author":"Cheng","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102630","DOI":"10.1016\/j.tre.2022.102630","article-title":"Transport service selection and routing with carbon emissions and inventory costs consideration in the context of the Belt and Road Initiative","volume":"159","author":"Qi","year":"2022","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.tre.2018.10.007","article-title":"Selection of China\u2019s imported grain distribution centers in the context of the Belt and Road initiative","volume":"120","author":"Li","year":"2018","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_12","first-page":"85","article-title":"A Reflection on the Establishment of the Quality Evaluation Index System of China Railway Express","volume":"41","author":"Zhang","year":"2019","journal-title":"Railw. Transp. Econ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/13675567.2019.1703917","article-title":"Selection of consolidation centers for China railway express","volume":"23","author":"Sun","year":"2020","journal-title":"Int. J. Logist. Res. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.tre.2018.10.002","article-title":"Hinterland patterns of China Railway (CR) express in China under the Belt and Road Initiative: A preliminary analysis","volume":"119","author":"Jiang","year":"2018","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_15","first-page":"40","article-title":"A Tentative Study on the Countermeasures for Developing China Railway Express Piggyback Transportation Under the Belt and Road Initiative","volume":"41","author":"Fan","year":"2019","journal-title":"Railw. Transp. Econ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.aap.2019.05.005","article-title":"A feature learning approach based on XGBoost for driving assessment and risk prediction","volume":"129","author":"Shi","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_17","first-page":"1079","article-title":"Construction of Logistics Competitiveness System and Level Measurement of Node Cities of China Railway Express Based on Text Mining","volume":"5","author":"Liu","year":"2025","journal-title":"Curr. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.tre.2017.09.007","article-title":"Evaluation of consolidation center cargo capacity and locations for China railway express","volume":"117","author":"Zhao","year":"2018","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.habitatint.2015.10.025","article-title":"An evaluation model for urban carrying capacity: A case study of China\u2019s mega-cities","volume":"53","author":"Wei","year":"2016","journal-title":"Habitat. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102646","DOI":"10.1016\/j.trc.2020.102646","article-title":"Predicting lane-changing risk level based on vehicles\u2019 space-series features: A pre-emptive learning approach","volume":"116","author":"Chen","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.trc.2019.04.026","article-title":"An ensemble prediction model for train delays","volume":"104","author":"Nair","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_22","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"Lv","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103993","DOI":"10.1016\/j.trc.2022.103993","article-title":"Identifying the rail operating features associated to intermodal freight rail operation delays","volume":"147","author":"Viti","year":"2023","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.aap.2018.10.015","article-title":"A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data","volume":"122","author":"Bao","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_25","first-page":"36","article-title":"Improved AHP and BP Neural Network Model for Construction Companies\u2019 Circular Economy Evaluation","volume":"27","author":"Cheng","year":"2015","journal-title":"Manag. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neucom.2022.05.010","article-title":"A new approach for evaluating node importance in complex networks via deep learning methods","volume":"497","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., Khattak, A., Matara, C.M., Hussain, A., and Farooq, A. (2022). Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents. PLoS ONE, 17.","key":"ref_28","DOI":"10.1371\/journal.pone.0262941"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"132626","DOI":"10.1016\/j.jhydrol.2024.132626","article-title":"City scale urban flooding risk assessment using multi-source data and machine learning approach","volume":"651","author":"Wei","year":"2025","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106185","DOI":"10.1016\/j.scs.2025.106185","article-title":"Quantifying contributions of geographical features to urban GDP outputs via interpretable machine learning","volume":"121","author":"Zhang","year":"2025","journal-title":"Sustain. Cities Soc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106219","DOI":"10.1016\/j.scs.2025.106219","article-title":"An Indicator-Based Framework of Circular Cities Focused on Sustainability Dimensions and Sustainable Development Goal 11 Obtained Using Machine Learning and Text Analytics","volume":"121","author":"Falah","year":"2025","journal-title":"Sustain. Cities Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104389","DOI":"10.1016\/j.trd.2024.104389","article-title":"Urban transport emission prediction analysis through machine learning and deep learning techniques","volume":"135","author":"Ji","year":"2024","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.trpro.2025.03.066","article-title":"Logistics hub location optimization: A k-means and p-median model hybrid approach using road network distances","volume":"84","author":"Rahman","year":"2025","journal-title":"Transp. Res. Procedia"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4145353","DOI":"10.1155\/2019\/4145353","article-title":"Spatiotemporal traffic flow prediction with KNN and LSTM","volume":"2019","author":"Luo","year":"2019","journal-title":"J. Adv. Transp."},{"key":"ref_35","first-page":"38","article-title":"Competitive Evolution Characteristics and Competitiveness Enhancement Countermeasures of Five Assembly Centers for China Railway Express","volume":"43","author":"Li","year":"2021","journal-title":"Railw. Transp. Econ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.landurbplan.2008.10.022","article-title":"Measurement indicators and an evaluation approach for assessing urban sustainable development: A case study for China\u2019s Jining City","volume":"90","author":"Li","year":"2009","journal-title":"Landsc. Urban Plan."},{"key":"ref_37","first-page":"10","article-title":"Operation Quality Evaluation of China Railway Express from the Perspective of \u201cDual Circulation\u201d","volume":"45","author":"Li","year":"2023","journal-title":"Railw. Transp. Econ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103373","DOI":"10.1016\/j.iref.2024.103373","article-title":"Synergistic industrial agglomeration, new quality productive forces and high-quality development of the manufacturing industry","volume":"94","author":"Liu","year":"2024","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/014416400295347","article-title":"An evaluation methodology for city logistics","volume":"20","author":"Taniguchi","year":"2000","journal-title":"Transp. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.tre.2019.08.007","article-title":"Analyzing the effect of government subsidy on shippers\u2019 mode switching behavior in the Belt and Road strategic context","volume":"129","author":"Kundu","year":"2019","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101110","DOI":"10.1016\/j.jth.2021.101110","article-title":"Travel time, trip frequency and motorised-vehicle ownership: A case study of travel behaviour of people with reduced mobility in Medell\u00edn","volume":"22","year":"2021","journal-title":"J. Transp. Health"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7145","DOI":"10.1109\/TITS.2020.3002419","article-title":"An automated machine learning (AutoML) method of risk prediction for decision-making of autonomous vehicles","volume":"22","author":"Shi","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Wahab, L., and Jiang, H. (2019). A comparative study on machine learning based algorithms for prediction of motorcycle crash severity. PLoS ONE, 14.","key":"ref_43","DOI":"10.1371\/journal.pone.0214966"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_45","first-page":"33","article-title":"A novel thresholding for prediction analytics with machine learning techniques","volume":"23","author":"Shakir","year":"2023","journal-title":"Int. J. Comput. Sci. Netw. Secur."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/7\/237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:56:37Z","timestamp":1760032597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/7\/237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["ijgi14070237"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14070237","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2025,6,22]]}}}