{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T11:26:13Z","timestamp":1765538773192,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,18]]},"DOI":"10.1145\/3770501.3770531","type":"proceedings-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T11:20:16Z","timestamp":1765538416000},"page":"254-262","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Currus - A Compound AI Approach to Distributed Vehicle Trajectory Reconstruction in the Edge-Cloud"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1151-9150","authenticated-orcid":false,"given":"Manuel Can","family":"Kaya","sequence":"first","affiliation":[{"name":"TU Wien, Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9765-6310","authenticated-orcid":false,"given":"Thomas Werner","family":"Pusztai","sequence":"additional","affiliation":[{"name":"TU Wien, Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2120-1142","authenticated-orcid":false,"given":"Andrija","family":"Stanisic","sequence":"additional","affiliation":[{"name":"TU Wien, Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0410-6315","authenticated-orcid":false,"given":"Stefan","family":"Nastic","sequence":"additional","affiliation":[{"name":"TU Wien, Vienna, Austria"}]}],"member":"320","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Theodoros Alexakis Nikolaos Peppes Konstantinos Demestichas and Evgenia Adamopoulou. 2022. A Distributed Big Data Analytics Architecture for Vehicle Sensor Data. Sensors 23 (12 2022) 357. 10.3390\/s23010357","DOI":"10.3390\/s23010357"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Rakan Alhwety and Nazar Elfadil. 2024. Vehicle Tracking System Approaches: A Systematic Literature Review. International Journal of Computer Science and Mobile Computing 13 (08 2024) 23\u201331. 10.47760\/ijcsmc.2024.v13i08.003","DOI":"10.47760\/ijcsmc.2024.v13i08.003"},{"key":"e_1_3_3_1_4_2","unstructured":"Max Bachmann David Necas Mikko Ohtamaa and Antti Haapala. 2010. Levenshtein. https:\/\/github.com\/rapidfuzz\/Levenshtein. Version 0.27.1."},{"key":"e_1_3_3_1_5_2","volume-title":"In IEEE Transactions on Pattern Analysis and Machine Intelligence","author":"Bai Yan","year":"2021","unstructured":"Yan Bai, Jun Liu, Yihang Lou, Ce Wang, and Ling-Yu Duan. 2021. Disentangled Feature Learning Network and a Comprehensive Benchmark for Vehicle Re-Identification. In In IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"Johan Barth\u00e9lemy Nicolas Verstaevel Hugh Forehead and Pascal Perez. 2019. Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors 19 9 (2019). 10.3390\/s19092048","DOI":"10.3390\/s19092048"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Zijian Cao Dong Zhao Hanxing Song Haitao Yuan Qiyue Wang Huadong Ma Jianjun Tong and Chang Tan. 2024. F33VeTrac: Enabling Fine-Grained Fully-Road-Covered and Fully-Individual- Penetrative Vehicle Trajectory Recovery. IEEE Transactions on Mobile Computing 23 5 (2024) 4975\u20134991. 10.1109\/TMC.2023.3301871","DOI":"10.1109\/TMC.2023.3301871"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Weiwei Chen Menghua Ji and Jianmin Wang. 2014. T-DBSCAN: A spatiotemporal density clustering for GPS trajectory segmentation. International Journal of Online and Biomedical Engineering 10 6 (2014).","DOI":"10.3991\/ijoe.v10i6.3881"},{"key":"e_1_3_3_1_9_2","unstructured":"Matthijs Douze Alexandr Guzhva Chengqi Deng Jeff Johnson Gergely Szilvasy Pierre-Emmanuel Mazar\u00e9 Maria Lomeli Lucas Hosseini and Herv\u00e9 J\u00e9gou. 2024. The Faiss library. (2024). arxiv:https:\/\/arXiv.org\/abs\/2401.08281\u00a0[cs.LG]"},{"key":"e_1_3_3_1_10_2","unstructured":"Zhouyu Fu Weiming Hu and Tieniu Tan. 2005. Hierarchical clustering framework to classify vehicle motion trajectories. Pattern Recognition (2005)."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Xuri Gong Zhou Huang Yaoli Wang Lun Wu and Yu Liu. 2020. High-performance spatiotemporal trajectory matching across heterogeneous data sources. Future Generation Computer Systems 105 (2020) 148\u2013161. 10.1016\/j.future.2019.11.027","DOI":"10.1016\/j.future.2019.11.027"},{"key":"e_1_3_3_1_12_2","unstructured":"Brian\u00a0E. Granger and Min Ragan-Kelley. 2009. PyZMQ. https:\/\/github.com\/zeromq\/pyzmq. Version 26.4.0."},{"key":"e_1_3_3_1_13_2","unstructured":"Milos Gravara Andrija Stanisic and Stefan Nastic. 2025. A Novel Compound AI Model for 6G Networks in 3D Continuum. arxiv:https:\/\/arXiv.org\/abs\/2505.15821\u00a0[cs.NI] https:\/\/arxiv.org\/abs\/2505.15821"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Zhuojun Jiang Lei Dong Lun Wu and Yu Liu. 2022. Quantifying navigation complexity in transportation networks. PNAS Nexus 1 3 (July 2022). 10.1093\/pnasnexus\/pgac126","DOI":"10.1093\/pnasnexus\/pgac126"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Jeff Johnson Matthijs Douze and Herv\u00e9 J\u00e9gou. 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data 7 3 (2019) 535\u2013547.","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Dheeraj Kumar Sutharshan Rajasegarar and Marimuthu Palaniswami. 2018. Fast and scalable big data trajectory clustering for understanding urban mobility. IEEE Transactions on Intelligent Transportation Systems 20 10 (2018) 3709\u20133722.","DOI":"10.1109\/TITS.2018.2854775"},{"key":"e_1_3_3_1_18_2","unstructured":"National Renewable\u00a0Energy Laboratory. 2022. mappymatch. https:\/\/github.com\/NREL\/mappymatch. Version 0.6.0."},{"key":"e_1_3_3_1_19_2","first-page":"707","volume-title":"Soviet physics doklady","author":"Levenshtein Vladimir\u00a0I","year":"1966","unstructured":"Vladimir\u00a0I Levenshtein et\u00a0al. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady , Vol.\u00a010. Soviet Union, 707\u2013710."},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Xiuquan Li Zheming Zhang Binbin Ma Dunyong Zheng Wentao Yang and Yingwei Yan. 2025. High spatio-temporal resolution estimation of urban road traffic carbon dioxide emissions and analysis of influencing factors using GPS trajectory data. Environmental Monitoring and Assessment 197 6 (2025) 665.","DOI":"10.1007\/s10661-025-14116-0"},{"key":"e_1_3_3_1_21_2","unstructured":"Zhishuai Li Ziyue Li Xiaoru Hu Guoqing Du Yunhao Nie Feng Zhu Lei Bai and Rui Zhao. 2023. VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network. arxiv:https:\/\/arXiv.org\/abs\/2312.06428\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2312.06428"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474717.3483987"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.238"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2016.7553002"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_53"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","unstructured":"Xinchen Liu Wu Liu Tao Mei and Huadong Ma. 2018. PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance. IEEE Transactions on Multimedia 20 3 (2018) 645\u2013658. 10.1109\/TMM.2017.2751966","DOI":"10.1109\/TMM.2017.2751966"},{"key":"e_1_3_3_1_27_2","unstructured":"Google LLC. 2008. Protocol Buffers. https:\/\/github.com\/protocolbuffers\/protobuf. Version 1.71.0."},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00335"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","unstructured":"Maximilian Maresch and Stefan Nastic. 2024. VATE: Edge-Cloud System for Object Detection in Real-Time Video Streams. 27\u201334. 10.1109\/ICFEC61590.2024.00017","DOI":"10.1109\/ICFEC61590.2024.00017"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","unstructured":"Douglas Peixoto Hung Nguyen Bolong Zheng and Xiaofang Zhou. 2019. A framework for parallel map-matching at scale using Spark. Distributed and Parallel Databases 37 (12 2019). 10.1007\/s10619-018-7254-0","DOI":"10.1007\/s10619-018-7254-0"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","unstructured":"Zhanhang Shi Dong Guo Lili Bian Yvbin Liu Bin Zhou and Feng Sun. 2025. Vehicle Trajectory Reconstruction Method for Urban Arterial Roads Based on Multi-Source Data Fusion. Sensors 25 (03 2025) 2102. 10.3390\/s25072102","DOI":"10.3390\/s25072102"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","unstructured":"Han Su Shuncheng Liu Bolong Zheng Xiaofang Zhou and Kai Zheng. 2019. A survey of trajectory distance measures and performance evaluation. The VLDB Journal 29 1 (Oct. 2019) 3\u201332. 10.1007\/s00778-019-00574-9","DOI":"10.1007\/s00778-019-00574-9"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00900"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448617"},{"key":"e_1_3_3_1_35_2","unstructured":"Ashab Uddin Ahmed\u00a0Hamdi Sakr and Ning Zhang. 2025. Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures. arxiv:https:\/\/arXiv.org\/abs\/2502.06963\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2502.06963"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Wei Wang Yujia Xie and Luliang Tang. 2023. Hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. Sensors 23 15 (2023) 6909.","DOI":"10.3390\/s23156909"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","unstructured":"Xingmin Wang Zachary Jerome Zihao Wang Chenhao Zhang Shengyin Shen Vivek Vijaya\u00a0Kumar Fan Bai Paul Krajewski Danielle Deneau Ahmad Jawad Rachel Jones Gary Piotrowicz and Henry Liu. 2024. Traffic light optimization with low penetration rate vehicle trajectory data. Nature Communications 15 (02 2024). 10.1038\/s41467-024-45427-4","DOI":"10.1038\/s41467-024-45427-4"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671558"},{"key":"e_1_3_3_1_39_2","doi-asserted-by":"publisher","unstructured":"Da Xu Mengfei Liu Xinpeng Yao and Nengchao Lyu. 2023. Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection. Sensors 23 (12 2023) 9800. 10.3390\/s23249800","DOI":"10.3390\/s23249800"},{"key":"e_1_3_3_1_40_2","doi-asserted-by":"publisher","unstructured":"Hao Yang Jiarui Cai Chenxi Liu Ruimin Ke and Y. Wang. 2023. Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning. Transportation Research Part C: Emerging Technologies 148 (03 2023) 103982. 10.1016\/j.trc.2022.103982","DOI":"10.1016\/j.trc.2022.103982"},{"key":"e_1_3_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539186"},{"key":"e_1_3_3_1_42_2","doi-asserted-by":"publisher","unstructured":"Fudan Yu Huan Yan Rui Chen Guozhen Zhang Yu Liu Meng Chen and Yong Li. 2023. City-scale Vehicle Trajectory Data from Traffic Camera Videos. Scientific Data 10 1 (17 Oct 2023) 711. 10.1038\/s41597-023-02589-y","DOI":"10.1038\/s41597-023-02589-y"},{"key":"e_1_3_3_1_43_2","doi-asserted-by":"publisher","unstructured":"Lei Zhu Jacob\u00a0R. Holden and Jeffrey\u00a0D. Gonder. 2017. Trajectory Segmentation Map-Matching Approach for Large-Scale High-Resolution GPS Data. Transportation Research Record 2645 1 (2017) 67\u201375. arXiv:10.3141\/2645-08","DOI":"10.3141\/2645-08"}],"event":{"name":"IOT 2025: The 15th International Conference on the Internet of Things","location":"Vienna Austria","acronym":"IOT 2025"},"container-title":["Proceedings of the 15th International Conference on the Internet of Things"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770501.3770531","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T11:22:39Z","timestamp":1765538559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770501.3770531"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":42,"alternative-id":["10.1145\/3770501.3770531","10.1145\/3770501"],"URL":"https:\/\/doi.org\/10.1145\/3770501.3770531","relation":{},"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"2025-12-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}