{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:43:23Z","timestamp":1781797403883,"version":"3.54.5"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p>With the widespread use of GPS-enabled devices and services, trajectory data fuels services in a variety of fields, such as transportation and smart cities. However, trajectory data often contains errors stemming from inaccurate GPS measurements, low sampling rates, and transmission interruptions, yielding low-quality trajectory data with negative effects on downstream services. Therefore, a crucial yet tedious endeavor is to assess the quality of trajectory data, serving as a guide for subsequent data cleaning and analyses. Despite some studies addressing general-purpose data quality assessment, no studies exist that are tailored specifically for trajectory data.<\/jats:p>\n          <jats:p>To more effectively diagnose the quality of trajectory data, we propose T-Assess, an automated trajectory data quality assessment system. T-Assess is built on three fundamental principles: i) extensive coverage, ii) versatility, and iii) efficiency. To achieve comprehensive coverage, we propose assessment criteria spanning validity, completeness, consistency, and fairness. To provide high versatility, T-Assess supports both offline and online evaluations for full-batch trajectory datasets as well as real-time trajectory streams. In addition, we incorporate an evaluation optimization strategy to achieve assessment efficiency. Extensive experiments on four real-life benchmark datasets offer insight into the effectiveness of T-Assess at quantifying trajectory data quality beyond the capabilities of state-of-the-art data quality systems.<\/jats:p>","DOI":"10.14778\/3712221.3712233","type":"journal-article","created":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T18:03:04Z","timestamp":1744048984000},"page":"666-674","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data"],"prefix":"10.14778","volume":"18","author":[{"given":"Junhao","family":"Zhu","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danlei","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziquan","family":"Fang","sequence":"additional","affiliation":[{"name":"Zhejiang University &amp; Zhejiang Key Laboratory of Big Data Intelligent Computing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University &amp; Zhejiang Key Laboratory of Big Data Intelligent Computing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University &amp; Zhejiang Key Laboratory of Big Data Intelligent Computing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Li","sequence":"additional","affiliation":[{"name":"Aalborg University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian S.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Aalborg University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2012. Apache Kafka. https:\/\/kafka.apache.org."},{"key":"e_1_2_1_2_1","unstructured":"2014. Apache Flink. http:\/\/flink.apache.org."},{"key":"e_1_2_1_3_1","unstructured":"2020. AIS Project. https:\/\/marinecadastre.gov\/ais."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","unstructured":"2022. CRAWDAD roma\/taxi. 10.15783\/C7QC7M","DOI":"10.15783\/C7QC7M"},{"key":"e_1_2_1_5_1","unstructured":"2024. T-Assess: full version. https:\/\/github.com\/ZJU-DAILY\/T-Assess\/blob\/main\/technical_report.pdf."},{"key":"e_1_2_1_6_1","first-page":"1","article-title":"A model for enriching trajectories with semantic geographical information","volume":"22","author":"Alvares Luis Ot\u00e1vio","year":"2007","unstructured":"Luis Ot\u00e1vio Alvares, Vania Bogorny, Bart Kuijpers, Jos\u00e9 Ant\u00f4nio Fernandes de Mac\u011bdo, Bart Moelans, and Alejandro A. Vaisman. 2007. A model for enriching trajectories with semantic geographical information. In SIGSPATIAL. 22:1--22:8.","journal-title":"SIGSPATIAL."},{"key":"e_1_2_1_7_1","first-page":"33","article-title":"Segmenting trajectories: A framework and algorithms using spatiotemporal criteria","volume":"3","author":"Buchin Maike","year":"2011","unstructured":"Maike Buchin, Anne Driemel, Marc J. van Kreveld, and Vera Sacrist\u00e1n. 2011. Segmenting trajectories: A framework and algorithms using spatiotemporal criteria. J. Spatial Inf. Sci. 3, 1 (2011), 33--63.","journal-title":"J. Spatial Inf. Sci."},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Emily Caveness Paul Suganthan G. C. Zhuo Peng Neoklis Polyzotis Sudip Roy and Martin Zinkevich. 2020. TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines. In SIGMOD. 2793--2796.","DOI":"10.1145\/3318464.3384707"},{"key":"e_1_2_1_9_1","volume-title":"Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond. CoRR abs\/2403.14151","author":"Chen Wei","year":"2024","unstructured":"Wei Chen, Yuxuan Liang, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei Li, Yanwei Yu, Qingsong Wen, Chao Chen, Kai Zheng, Yunjun Gao, Xiaofang Zhou, and Yu Zheng. 2024. Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond. CoRR abs\/2403.14151 (2024)."},{"key":"e_1_2_1_10_1","unstructured":"Gao Cong Wenfei Fan Floris Geerts Xibei Jia and Shuai Ma. 2007. Improving Data Quality: Consistency and Accuracy. In VLDB. 315--326."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Philippe Cudr\u00e9-Mauroux Eugene Wu and Samuel Madden. 2010. TrajStore: An adaptive storage system for very large trajectory data sets. In ICDE. 109--120.","DOI":"10.1109\/ICDE.2010.5447829"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/TKDE.2019.2896985","article-title":"Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data","volume":"32","author":"Dabiri Sina","year":"2020","unstructured":"Sina Dabiri, Chang-Tien Lu, Kevin P. Heaslip, and Chandan K. Reddy. 2020. Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data. IEEE Trans. Knowl. Data Eng. 32, 5 (2020), 1010--1023.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","first-page":"787","DOI":"10.14778\/3192965.3192970","article-title":"Ul-TraMan: A Unified Platform for Big Trajectory Data Management and Analytics","volume":"11","author":"Ding Xin","year":"2018","unstructured":"Xin Ding, Lu Chen, Yunjun Gao, Christian S. Jensen, and Hujun Bao. 2018. Ul-TraMan: A Unified Platform for Big Trajectory Data Management and Analytics. Proc. VLDB Endow. 11, 7 (2018), 787--799.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_14_1","first-page":"1","article-title":"Network-less trajectory imputation","volume":"8","author":"Elshrif Mohamed M.","year":"2022","unstructured":"Mohamed M. Elshrif, Keivin Isufaj, and Mohamed F. Mokbel. 2022. Network-less trajectory imputation. In SIGSPATIAL. 8:1--8:10.","journal-title":"SIGSPATIAL."},{"key":"e_1_2_1_15_1","unstructured":"Mohammad Etemad. 2020. TrajSeg. https:\/\/github.com\/metemaad\/TrajSeg."},{"key":"e_1_2_1_16_1","article-title":"GPSClean: A Framework for Cleaning and Repairing GPS Data","volume":"13","author":"Fang Chenglong","year":"2022","unstructured":"Chenglong Fang, Feng Wang, Bin Yao, and Jianqiu Xu. 2022. GPSClean: A Framework for Cleaning and Repairing GPS Data. ACM Trans. Intell. Syst. Technol. 13, 3 (2022), 40:1--40:22.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Ziquan Fang Yuntao Du Lu Chen Yujia Hu Yunjun Gao and Gang Chen. 2021. E2 DTC: An End to End Deep Trajectory Clustering Framework via Self-Training. In ICDE. 696--707.","DOI":"10.1109\/ICDE51399.2021.00066"},{"key":"e_1_2_1_18_1","volume-title":"Jensen","author":"Fang Ziquan","year":"2022","unstructured":"Ziquan Fang, Yuntao Du, Xinjun Zhu, Danlei Hu, Lu Chen, Yunjun Gao, and Christian S. Jensen. 2022. Spatio-Temporal Trajectory Similarity Learning in Road Networks. In KDD. 347--356."},{"key":"e_1_2_1_19_1","first-page":"2","volume-title":"Proc. ACM Manag. Data 1","author":"Fang Ziquan","year":"2023","unstructured":"Ziquan Fang, Shenghao Gong, Lu Chen, Jiachen Xu, Yunjun Gao, and Christian S. Jensen. 2023. Ghost: A General Framework for High-Performance Online Similarity Queries over Distributed Trajectory Streams. Proc. ACM Manag. Data 1, 2 (2023), 173:1--173:25."},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.14778\/3457390.3457394","article-title":"MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data","volume":"14","author":"Fang Ziquan","year":"2021","unstructured":"Ziquan Fang, Lu Pan, Lu Chen, Yuntao Du, and Yunjun Gao. 2021. MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data. Proc. VLDB Endow. 14, 8 (2021), 1289--1297.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Daniele Foroni Matteo Lissandrini and Yannis Velegrakis. 2021. Estimating the extent of the effects of Data Quality through Observations. In ICDE. 1913--1918.","DOI":"10.1109\/ICDE51399.2021.00176"},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Daniele Foroni Matteo Lissandrini and Yannis Velegrakis. 2021. The F4U System for Understanding the Effects of Data Quality. In ICDE. 2717--2720.","DOI":"10.1109\/ICDE51399.2021.00312"},{"key":"e_1_2_1_23_1","volume-title":"Ali Oran, and Patrick Jaillet.","author":"Goh Chong Yang","year":"2012","unstructured":"Chong Yang Goh, Justin Dauwels, Nikola Mitrovic, Muhammad Tayyab Asif, Ali Oran, and Patrick Jaillet. 2012. Online map-matching based on Hidden Markov model for real-time traffic sensing applications. In ITSC. 776--781."},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1109\/TKDE.2023.3323535","article-title":"Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative Study","volume":"36","author":"Hu Danlei","year":"2024","unstructured":"Danlei Hu, Lu Chen, Hanxi Fang, Ziquan Fang, Tianyi Li, and Yunjun Gao. 2024. Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative Study. IEEE Trans. Knowl. Data Eng. 36, 5 (2024), 2191--2212.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Jae-Gil Lee Jiawei Han and Xiaolei Li. 2008. Trajectory Outlier Detection: A Partition-and-Detect Framework. In ICDE. 140--149.","DOI":"10.1109\/ICDE.2008.4497422"},{"key":"e_1_2_1_26_1","volume-title":"Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects. ACM Comput. Surv. 55, 3","author":"Li Huan","year":"2023","unstructured":"Huan Li, Hua Lu, Christian S. Jensen, Bo Tang, and Muhammad Aamir Cheema. 2023. Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects. ACM Comput. Surv. 55, 3 (2023), 57:1--57:41."},{"key":"e_1_2_1_27_1","volume-title":"Muhammad Aamir Cheema, and Christian S. Jensen","author":"Li Huan","year":"2022","unstructured":"Huan Li, Bo Tang, Hua Lu, Muhammad Aamir Cheema, and Christian S. Jensen. 2022. Spatial Data Quality in the IoT Era: Management and Exploitation. In SIGMOD. 2474--2482."},{"key":"e_1_2_1_28_1","volume-title":"Hua Lu, and Christian S. Jensen.","author":"Li Xiao","year":"2023","unstructured":"Xiao Li, Huan Li, Harry Kai-Ho Chan, Hua Lu, and Christian S. Jensen. 2023. Data Imputation for Sparse Radio Maps in Indoor Positioning. In ICDE. 2235--2248."},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","first-page":"2696","DOI":"10.1109\/TKDE.2017.2744619","article-title":"Feature Grouping-Based Outlier Detection Upon Streaming Trajectories","volume":"29","author":"Mao Jiali","year":"2017","unstructured":"Jiali Mao, Tao Wang, Cheqing Jin, and Aoying Zhou. 2017. Feature Grouping-Based Outlier Detection Upon Streaming Trajectories. IEEE Trans. Knowl. Data Eng. 29, 12 (2017), 2696--2709.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_2_1_30_1","doi-asserted-by":"crossref","first-page":"5655","DOI":"10.1109\/TITS.2021.3055614","article-title":"GeoTrackNet - A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection","volume":"23","author":"Nguyen Duong","year":"2022","unstructured":"Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, Ren\u00e9 Garello, and Ronan Fablet. 2022. GeoTrackNet - A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection. IEEE Trans. Intell. Transp. Syst. 23, 6 (2022), 5655--5667.","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","first-page":"3982","DOI":"10.14778\/3611540.3611601","article-title":"TsQuality: Measuring Time Series Data Quality in Apache IoTDB","volume":"16","author":"Qiu Yuanhui","year":"2023","unstructured":"Yuanhui Qiu, Chenguang Fang, Shaoxu Song, Xiangdong Huang, Chen Wang, and Jianmin Wang. 2023. TsQuality: Measuring Time Series Data Quality in Apache IoTDB. Proc. VLDB Endow. 16, 12 (2023), 3982--3985.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Xuan Rao Lisi Chen Yong Liu Shuo Shang Bin Yao and Peng Han. 2022. Graph-Flashback Network for Next Location Recommendation. In KDD. 1463--1471.","DOI":"10.1145\/3534678.3539383"},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.comnet.2015.12.023","article-title":"Urban planning and building smart cities based on the Internet of Things using Big Data analytics","volume":"101","author":"Rathore M. Mazhar","year":"2016","unstructured":"M. Mazhar Rathore, Awais Ahmad, Anand Paul, and Seungmin Rho. 2016. Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Comput. Networks 101 (2016), 63--80.","journal-title":"Comput. Networks"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"C\u00e9dric Renggli Luka Rimanic Luka Kolar Wentao Wu and Ce Zhang. 2023. Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise. In ICDE. 218--231.","DOI":"10.1109\/ICDE55515.2023.00024"},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Sebastian Schelter Stefan Grafberger Philipp Schmidt Tammo Rukat Mario Kie\u00dfling Andrey Taptunov Felix Bie\u00dfmann and Dustin Lange. 2019. Differential Data Quality Verification on Partitioned Data. In ICDE. 1940--1945.","DOI":"10.1109\/ICDE.2019.00210"},{"key":"e_1_2_1_36_1","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.14778\/3229863.3229867","article-title":"Automating Large-Scale Data Quality Verification","volume":"11","author":"Schelter Sebastian","year":"2018","unstructured":"Sebastian Schelter, Dustin Lange, Philipp Schmidt, Meltem Celikel, Felix Bie\u00dfmann, and Andreas Grafberger. 2018. Automating Large-Scale Data Quality Verification. Proc. VLDB Endow. 11, 12 (2018), 1781--1794.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_37_1","first-page":"4","volume-title":"Proc. ACM Manag. Data 1","author":"Shah Raunak","year":"2023","unstructured":"Raunak Shah, Koyel Mukherjee, Atharv Tyagi, Sai Keerthana Karnam, Dhruv Joshi, Shivam Pravin Bhosale, and Subrata Mitra. 2023. R2D2: Reducing Redundancy and Duplication in Data Lakes. Proc. ACM Manag. Data 1, 4 (2023), 268:1--268:25."},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.14778\/3503585.3503602","article-title":"DQDF","volume":"15","author":"Sinthong Phanwadee","year":"2021","unstructured":"Phanwadee Sinthong, Dhaval Patel, Nianjun Zhou, Shrey Shrivastava, Arun Iyengar, and Anuradha Bhamidipaty. 2021. DQDF: Data-Quality-Aware Dataframes. Proc. VLDB Endow. 15, 4 (2021), 949--957.","journal-title":"Data-Quality-Aware Dataframes. Proc. VLDB Endow."},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Shaoxu Song and Aoqian Zhang. 2020. IoT Data Quality. In CIKM. 3517--3518.","DOI":"10.1145\/3340531.3412173"},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Han Su Kai Zheng Haozhou Wang Jiamin Huang and Xiaofang Zhou. 2013. Calibrating trajectory data for similarity-based analysis. In SIGMOD. 833--844.","DOI":"10.1145\/2463676.2465303"},{"key":"e_1_2_1_41_1","first-page":"1","volume-title":"Proc. ACM Manag. Data 1","author":"Su Yunxiang","year":"2023","unstructured":"Yunxiang Su, Yikun Gong, and Shaoxu Song. 2023. Time Series Data Validity. Proc. ACM Manag. Data 1, 1 (2023), 85:1--85:26."},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","first-page":"4864","DOI":"10.1109\/TITS.2020.2983614","article-title":"SAPIENT: Enabling RealTime Monitoring and Control in the Future Communication Infrastructure of Air Traffic Management","volume":"22","author":"Virdis Antonio","year":"2021","unstructured":"Antonio Virdis, Giovanni Stea, and Gianluca Dini. 2021. SAPIENT: Enabling RealTime Monitoring and Control in the Future Communication Infrastructure of Air Traffic Management. IEEE Trans. Intell. Transp. Syst. 22, 8 (2021), 4864--4875.","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1145\/355744.355749","article-title":"An Efficient Method for Generating Discrete Random Variables with General Distributions","volume":"3","author":"Walker Alastair J.","year":"1977","unstructured":"Alastair J. Walker. 1977. An Efficient Method for Generating Discrete Random Variables with General Distributions. ACM Trans. Math. Softw. 3, 3 (1977), 253--256.","journal-title":"ACM Trans. Math. Softw."},{"key":"e_1_2_1_44_1","volume-title":"A Survey on Trajectory Data Management, Analytics, and Learning. ACM Comput. Surv. 54, 2","author":"Wang Sheng","year":"2022","unstructured":"Sheng Wang, Zhifeng Bao, J. Shane Culpepper, and Gao Cong. 2022. A Survey on Trajectory Data Management, Analytics, and Learning. ACM Comput. Surv. 54, 2 (2022), 39:1--39:36."},{"key":"e_1_2_1_45_1","volume-title":"Torch: A Search Engine for Trajectory Data. In SIGIR. ACM, 535--544.","author":"Wang Sheng","year":"2018","unstructured":"Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Zizhe Xie, Qizhi Liu, and Xiaolin Qin. 2018. Torch: A Search Engine for Trajectory Data. In SIGIR. ACM, 535--544."},{"key":"e_1_2_1_46_1","volume-title":"EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation. In SIGIR. 383--392.","author":"Wang Xinfeng","year":"2023","unstructured":"Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, and Dongjin Yu. 2023. EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation. In SIGIR. 383--392."},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Yuan Xu Jiajie Xu Jing Zhao Kai Zheng An Liu Lei Zhao and Xiaofang Zhou. 2022. MetaPTP: An Adaptive Meta-optimized Model for Personalized Spatial Trajectory Prediction. In SIGKDD. 2151--2159.","DOI":"10.1145\/3534678.3539360"},{"key":"e_1_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Yanwei Yu Lei Cao Elke A. Rundensteiner and Qin Wang. 2014. Detecting moving object outliers in massive-scale trajectory streams. In KDD. 422--431.","DOI":"10.1145\/2623330.2623735"},{"key":"e_1_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Haitao Yuan Guoliang Li Zhifeng Bao and Ling Feng. 2020. Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter. In SIGMOD. 2135--2149.","DOI":"10.1145\/3318464.3389771"},{"key":"e_1_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Jing Yuan Yu Zheng Xing Xie and Guangzhong Sun. 2011. Driving with knowledge from the physical world. In KDD. 316--324.","DOI":"10.1145\/2020408.2020462"},{"key":"e_1_2_1_51_1","article-title":"Trajectory Data Mining","volume":"6","author":"Zheng Yu","year":"2015","unstructured":"Yu Zheng. 2015. Trajectory Data Mining: An Overview. ACM Trans. Intell. Syst. Technol. 6, 3 (2015), 29:1--29:41.","journal-title":"An Overview. ACM Trans. Intell. Syst. Technol."},{"key":"e_1_2_1_52_1","first-page":"32","article-title":"GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory","volume":"33","author":"Zheng Yu","year":"2010","unstructured":"Yu Zheng, Xing Xie, and Wei-Ying Ma. 2010. GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory. IEEE Data Eng. Bull. 33, 2 (2010), 32--39.","journal-title":"IEEE Data Eng. Bull."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3712221.3712233","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T18:48:23Z","timestamp":1744051703000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3712221.3712233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10.14778\/3712221.3712233"],"URL":"https:\/\/doi.org\/10.14778\/3712221.3712233","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"2025-04-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}