A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes


  • Yu Yan




Twin Data, Data Collection, Urban Transportation, Data Fusion, Data Analysis


As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.


Lee E A. Cyber physical systems: Design challenges [C]//2008 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC). IEEE, 2008: 363-369.

Grieves M W. Product lifecycle management: the new paradigm for enterprises [J]. International Journal of Product Development, 2005, 2(1-2): 71-84.

Glaessgen E, Stargel D. The digital twin paradigm for future NASA and US Air Force vehicles [C]//53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA. 2012: 1818.

Kim Y, Kim C M, Han Y H, et al. An efficient strategy of nonuniform sensor deployment in cyber physical systems [J]. The Journal of Supercomputing, 2013, 66(1): 70-80.

Mandolla C, Petruzzelli A M, Percoco G, et al. Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry [J]. Computers in industry, 2019, 109: 134-152.

Tuegel E J, Ingraffea A R, Eason T G, et al. Reengineering aircraft structural life prediction using a digital twin [J]. International Journal of Aerospace Engineering, 2011, 2011.

Korth B, Schwede C, Zajac M. Simulation-ready digital twin for realtime management of logistics systems [C]//2018 IEEE international conference on big data (big data). IEEE, 2018: 4194-4201.

Schluse M, Rossmann J. From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems [C]//2016 IEEE International Symposium on Systems Engineering (ISSE). IEEE, 2016: 1-6.

Kharlamov E, Martin-Recuerda F, Perry B, et al. Towards semantically enhanced digital twins [C]//2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018: 4189-4193.

Merkle L, Segura A S, Grummel J T, et al. Architecture of a digital twin for enabling digital services for battery systems [C]//2019 IEEE international conference on industrial cyber physical systems (ICPS). IEEE, 2019: 155-160.

Soni R, Bhatia M. Digital twin: intersection of mind and machine [J]. International Journal of Computational Intelligence & IoT, 2019, 2(3).

Haag S, Anderl R. Digital twin–Proof of concept [J]. Manufacturing letters, 2018, 15: 64-66.

Schroeder G N, Steinmetz C, Pereira C E, et al. Digital twin data modeling with automationml and a communication methodology for data exchange [J]. IFAC-PapersOnLine, 2016, 49(30): 12-17.

Schleich B, Anwer N, Mathieu L, et al. Shaping the digital twin for design and production engineering [J]. CIRP annals, 2017, 66(1): 141-144.

Desert island. Intelligent road transportation in the 5G era [M]. BEIJING BOOK CO. INC., 2020.

Duan H, Shen Y, Ding P. Research on construction technology of real digital twin traffic scene based on edge-cloud collaboration [C]//Third International Conference on Computer Science and Communication Technology (ICCSCT 2022). SPIE, 2022, 12506: 1119-1124.

Wang S, Zhang F, Qin T. Research on the construction of highway traffic digital twin system based on 3D GIS technology [C]//Journal of Physics: Conference Series. IOP Publishing, 2021, 1802(4): 042045.

Li Y, Zhang W. Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion [J]. Scientific reports, 2023, 13(1): 1-15.

Ivanov S, Nikolskaya K, Radchenko G, et al. Digital twin of city: Concept overview [C]//2020 Global Smart Industry Conference (GloSIC). IEEE, 2020: 178-186.

Wang Z, Yu G, Zhou B, et al. A train positioning method based-on vision and millimeter-wave radar data fusion [J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(5): 4603-4613.

Cai H, Zhu J, Zhang W. Quality deviation control for aircraft using digital twin [J]. Journal of Computing and Information Science in Engineering, 2021, 21(3).

Yumnam A S, Sreeram Y C, Naeem S A. Overview: Weblog mining, privacy issues and application of Web Log mining [C]//2014 International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2014: 638-641.

El Saddik A. Digital twins: The convergence of multimedia technologies [J]. IEEE multimedia, 2018, 25(2): 87-92.

Karadeniz A M, Arif İ, Kanak A, et al. Digital twin of eGastronomic things: A case study for ice cream machines [C]//2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2019: 1-4.

Pargmann H, Euhausen D, Faber R. Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: A quantitative approach [C]//2018 IEEE 3rd international conference on cloud computing and big data analysis (ICCCBDA). IEEE, 2018: 233-237.

Steger S, Mair V, Kofler C, et al. Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling–Benefits of exploring landslide data collection effects [J]. Science of the total environment, 2021, 776: 145935. [32]

Kai Liu, Meng-Ying Cui, Peng Cao, Jiang-Bo Wang. Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data [J]. PLOS ONE, 2016, 11(6).

Nantes A, Ngoduy D, Bhaskar A, et al. Real-time traffic state estimation in urban corridors from heterogeneous data [J]. Transportation Research Part C: Emerging Technologies, 2016, 66: 99-118.

Ibrahim M, Rjabtšikov V, Gilbert R. Overview of digital twin platforms for EV applications [J]. Sensors, 2023, 23(3): 1414.

Semeraro C, Lezoche M, Panetto H, et al. Data-driven invariant modelling patterns for digital twin design [J]. Journal of Industrial Information Integration, 2023, 31: 100424.

Yi yang Yao, Yingjie Xia, Zhenyu Shan, Zhengguang Liu. Learning for Traffic State Estimation on Large Scale of Incomplete Data [P]. International Conference on Multimedia Retrieval, 2016.

Li Q, Chen L, Li M, et al. A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios [J]. IEEE Transactions on Vehicular Technology, 2013, 63(2): 540-555.

Chen Q, Ma X, Tang S, et al. F-cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds [C]//Proceedings of the 4th ACM/IEEE Symposium on Edge Computing. 2019: 88-100.

Sun W, Zhang X, Peeta S, et al. A real-time fatigue driving recognition method incorporating contextual features and two fusion levels [J]. IEEE transactions on intelligent transportation systems, 2017, 18(12): 3408-3420.

Keechoo Choi, YounShik Chung. A Data Fusion Algorithm for Estimating Link Travel Time [J]. Journal of Intelligent Transportation Systems, 2002, 7(3-4).

Faouzi N. Data-driven aggregative schemes for multisource estimation fusion: a road travel timeapplication [J]. Proceedings of Spie the International Society for Optical Engineering, 2004, 5(1) :351-359.

Zeng Y, Lan J, Ran B, et al. A Novel Multisensor Traffic State Assessment System Based on Incomplete Data [J]. The Scientific World Journal, 2014, 2014: 1-13.

Javaid M, Haleem A, Suman R. Digital twin applications toward industry 4.0: a review [J]. Cognitive Robotics, 2023, 3: 71-92.

Liu X, Jiang D, Tao B, et al. A systematic review of digital twin about physical entities, virtual models, twin data, and applications [J]. Advanced Engineering Informatics, 2023, 55: 101876.

Li X, Feng M, Ran Y, et al. Big Data in Earth system science and progress towards a digital twin [J]. Nature Reviews Earth & Environment, 2023, 4(5): 319-332.







How to Cite

Yan, Y. (2024). A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes. International Journal of Computer Science and Information Technology, 3(1), 117-126. https://doi.org/10.62051/ijcsit.v3n1.15

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