{"title":"智能交通系统的工业4.0:基础和应用","authors":"Wasim Ahmad , Sunawar Khan , Tehseen Mazhar , Tariq Shahzad , Weiwei Jiang , Habib Hamam","doi":"10.1016/j.teler.2025.100255","DOIUrl":null,"url":null,"abstract":"<div><div>This paper examines how Industry 4.0 technologies enable smart transport systems through energy-aware architectures that integrate vehicles with the smart grid. We provide a concise synthesis of application patterns across cyber-physical systems (CPS), Industrial IoT sensing, edge and cloud analytics, and secure data exchange, and present application-oriented cases spanning EV–grid interaction (V2G), predictive maintenance, and operational optimization. We map enabling components—data ingestion, model inference, decision support, and secure interoperability—to transport tasks and discuss implementation trade-offs observed in practice. While our analysis is grounded in Industry 4.0 foundations, we explain how these foundations support a measured transition toward Industry 5.0—prioritizing human-centric, resilient, and sustainability-aligned operations— with Industry 5.0 features and LLM-based interfaces treated as future work rather than scope-defining elements.</div></div>","PeriodicalId":101213,"journal":{"name":"Telematics and Informatics Reports","volume":"20 ","pages":"Article 100255"},"PeriodicalIF":4.7000,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Industry 4.0 for smart transport systems: Foundations and applications\",\"authors\":\"Wasim Ahmad , Sunawar Khan , Tehseen Mazhar , Tariq Shahzad , Weiwei Jiang , Habib Hamam\",\"doi\":\"10.1016/j.teler.2025.100255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper examines how Industry 4.0 technologies enable smart transport systems through energy-aware architectures that integrate vehicles with the smart grid. We provide a concise synthesis of application patterns across cyber-physical systems (CPS), Industrial IoT sensing, edge and cloud analytics, and secure data exchange, and present application-oriented cases spanning EV–grid interaction (V2G), predictive maintenance, and operational optimization. We map enabling components—data ingestion, model inference, decision support, and secure interoperability—to transport tasks and discuss implementation trade-offs observed in practice. While our analysis is grounded in Industry 4.0 foundations, we explain how these foundations support a measured transition toward Industry 5.0—prioritizing human-centric, resilient, and sustainability-aligned operations— with Industry 5.0 features and LLM-based interfaces treated as future work rather than scope-defining elements.</div></div>\",\"PeriodicalId\":101213,\"journal\":{\"name\":\"Telematics and Informatics Reports\",\"volume\":\"20 \",\"pages\":\"Article 100255\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Telematics and Informatics Reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772503025000696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Telematics and Informatics Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772503025000696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Industry 4.0 for smart transport systems: Foundations and applications
This paper examines how Industry 4.0 technologies enable smart transport systems through energy-aware architectures that integrate vehicles with the smart grid. We provide a concise synthesis of application patterns across cyber-physical systems (CPS), Industrial IoT sensing, edge and cloud analytics, and secure data exchange, and present application-oriented cases spanning EV–grid interaction (V2G), predictive maintenance, and operational optimization. We map enabling components—data ingestion, model inference, decision support, and secure interoperability—to transport tasks and discuss implementation trade-offs observed in practice. While our analysis is grounded in Industry 4.0 foundations, we explain how these foundations support a measured transition toward Industry 5.0—prioritizing human-centric, resilient, and sustainability-aligned operations— with Industry 5.0 features and LLM-based interfaces treated as future work rather than scope-defining elements.