{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:54:23Z","timestamp":1753890863942,"version":"3.41.2"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>As environmental awareness increased due to the surge in greenhouse gases, green travel modes such as bicycles and walking have gradually became popular choices. However, the current traffic environment has many hidden problems that endanger the personal safety of traffic participants and hinder the development of green travel. Traditional methods, such as identifying risky locations after traffic accidents, suffer from the disadvantages of delayed response and lack of foresight. Against this background, we proposed a mobile edge crowdsensing framework to dynamically assess urban traffic green travel safety risks. Specifically, a large number of mobile devices were used to sense the road environment, from which a semantic detection framework detected the traffic high-risk behaviors of traffic participants. Then multi-source and heterogeneous urban crowdsensing data were used to model the travel safety risk to achieve a comprehensive and real-time assessment of urban green travel safety. We evaluated our method by leveraging real-world datasets collected from Xiamen Island. Results showed that our framework could accurately detect traffic high-risk behaviors with average F1-scores of 86.5% and assessed the travel safety risk with <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of 0.85 outperforming various baseline methods.<\/jats:p>","DOI":"10.3389\/fdata.2025.1440816","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T09:22:21Z","timestamp":1742548941000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CrowdRadar: a mobile crowdsensing framework for urban traffic green travel safety risk assessment"],"prefix":"10.3389","volume":"8","author":[{"given":"Yigao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Qingxian","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Wenxuan","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Chenhui","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Dingqi","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Longbiao","family":"Chen","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.3390\/app9112335","article-title":"Pedestrian and cyclist detection and intent estimation for autonomous vehicles: a survey","volume":"9","author":"Ahmed","year":"2019","journal-title":"Appl. Sci"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934","article-title":"Yolov4: optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020","journal-title":"arXiv"},{"key":"B3","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1109\/TITS.2011.2119372","article-title":"A review of computer vision techniques for the analysis of urban traffic","volume":"12","author":"Buch","year":"2011","journal-title":"IEEE Trans. Intellig. Transp. Syst"},{"key":"B4","first-page":"3560","article-title":"\u201cLow cost, high performance automatic motorcycle helmet violation detection,\u201d","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Chairat","year":"2020"},{"key":"B5","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"\u201cXgboost: A scalable tree boosting system,\u201d","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"B6","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1080\/08839514.2021.1935588","article-title":"A new spatio-temporal neural network approach for traffic accident forecasting","volume":"35","author":"de Medrano","year":"2021","journal-title":"Appl.Artif. Intellig"},{"key":"B7","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1145\/1644038.1644095","article-title":"\u201cCommon sense: participatory urban sensing using a network of handheld air quality monitors,\u201d","volume-title":"Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems","author":"Dutta","year":"2009"},{"key":"B8","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1145\/1518701.1518861","article-title":"\u201cUbigreen: investigating a mobile tool for tracking and supporting green transportation habits,\u201d","volume-title":"Proceedings of the SIGCHI Conference on Human Factors in Computing Systems","author":"Froehlich","year":"2009"},{"key":"B9","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MCOM.2011.6069707","article-title":"Mobile crowdsensing: current state and future challenges","volume":"49","author":"Ganti","year":"2011","journal-title":"IEEE Commun. Magaz"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1808.10437","article-title":"ICAN: Instance-centric attention network for human-object interaction detection","author":"Gao","year":"2018","journal-title":"arXiv"},{"key":"B11","doi-asserted-by":"publisher","first-page":"6290","DOI":"10.1109\/TII.2022.3146281","article-title":"Protecting location privacy of users based on trajectory obfuscation in mobile crowdsensing","volume":"18","author":"Gao","year":"2022","journal-title":"IEEE Trans. Indust. Inform"},{"key":"B12","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TITS.2010.2098029","article-title":"Automatic traffic signs and panels inspection system using computer vision","volume":"12","author":"Gonz\u00e1lez","year":"2011","journal-title":"IEEE Trans. Intellig. Transport. Syst"},{"key":"B13","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.1109\/COMST.2017.2726686","article-title":"The emergence of visual crowdsensing: challenges and opportunities","volume":"19","author":"Guo","year":"2017","journal-title":"IEEE Commun. Surv. Tutor"},{"key":"B14","doi-asserted-by":"publisher","first-page":"107493","DOI":"10.1016\/j.aap.2024.107493","article-title":"A spatiotemporal deep learning approach for pedestrian crash risk prediction based on poi trip characteristics and pedestrian exposure intensity","volume":"198","author":"Guo","year":"2024","journal-title":"Accid. Analy. Prevent"},{"key":"B15","doi-asserted-by":"publisher","first-page":"107400","DOI":"10.1016\/j.aap.2023.107400","article-title":"Towards safer streets: a framework for unveiling pedestrians' perceived road safety using street view imagery","volume":"195","author":"Hamim","year":"2024","journal-title":"Accid. Analy. Prevent"},{"key":"B16","doi-asserted-by":"publisher","first-page":"3182","DOI":"10.1109\/TITS.2015.2437998","article-title":"Recognition of car makes and models from a single traffic-camera image","volume":"16","author":"He","year":"2015","journal-title":"IEEE Trans. Intellig. Transp. Syst"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.3390\/su9061043","article-title":"Green travel: exploring the characteristics and behavior transformation of urban residents in china","volume":"9","author":"Jia","year":"2017","journal-title":"Sustainability"},{"key":"B18","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1109\/TMC.2021.3112394","article-title":"P 2 AE: preserving privacy, accuracy, and efficiency in location-dependent mobile crowdsensing","volume":"22","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Mobile Comp"},{"key":"B19","doi-asserted-by":"publisher","first-page":"3913","DOI":"10.1109\/JSYST.2020.3012743","article-title":"itv: Inferring traffic violation-prone locations with vehicle trajectories and road environment data","volume":"15","author":"Jiang","year":"2020","journal-title":"IEEE Syst. J"},{"key":"B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2517351.2517372","article-title":"\u201cPiggyback crowdsensing (PCS) energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities,\u201d","volume-title":"Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems","author":"Lane","year":"2013"},{"key":"B21","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"B22","first-page":"1","article-title":"Road traffic data: collection methods and applications","author":"Leduc","year":"2008","journal-title":"Working Papers on Energy, Transport and Climate Change"},{"key":"B23","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.bushor.2015.03.008","article-title":"The internet of things (IOT): Applications, investments, and challenges for enterprises","volume":"58","author":"Lee","year":"2015","journal-title":"Bus. Horiz"},{"key":"B24","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.trc.2015.03.015","article-title":"A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction","volume":"55","author":"Lin","year":"2015","journal-title":"Transp. Res. Part C: Emerg. Technol"},{"key":"B25","first-page":"740","article-title":"\u201cMicrosoft coco: common objects in context,\u201d","volume-title":"European Conference on Computer Vision","author":"Lin","year":"2014"},{"key":"B26","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1007\/978-3-319-46448-0_51","article-title":"\u201cVisual relationship detection with language priors,\u201d","volume-title":"Computer Vision-ECCV 2016: 14th European Conference","author":"Lu","year":"2016"},{"key":"B27","doi-asserted-by":"publisher","first-page":"106285","DOI":"10.1016\/j.aap.2021.106285","article-title":"Predicting unsafe driving risk among commercial truck drivers using machine learning: lessons learned from the surveillance of 20 million driving miles","volume":"159","author":"Mehdizadeh","year":"2021","journal-title":"Accid. Analy. Prevent"},{"key":"B28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12544-021-00497-z","article-title":"Near accidents and collisions between pedestrians and cyclists","volume":"13","author":"Mesim\u00e4ki","year":"2021","journal-title":"Eur. Transp. Res. Rev"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1707.05005","article-title":"graph2vec: learning distributed representations of graphs","author":"Narayanan","year":"2017","journal-title":"arXiv"},{"journal-title":"Traffic Safety Facts 2013","year":"2013","key":"B30"},{"key":"B31","first-page":"75","article-title":"\u201cA method for distinction of bicycle traffic violations by detection of cyclists' behavior using multi-sensors,\u201d","volume-title":"Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services","author":"Ooi","year":"2016"},{"key":"B32","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1007\/s11227-016-1624-z","article-title":"Highway traffic accident prediction using vds big data analysis","volume":"72","author":"Park","year":"2016","journal-title":"J. Supercomput"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12544-019-0371-7","article-title":"Truck-bicycle safety: an overview of methods of study, risk factors and research needs","volume":"11","author":"Pokorny","year":"2019","journal-title":"Eur. Transp. Res. Rev"},{"key":"B34","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1080\/15389588.2019.1696963","article-title":"Riding behavior and electric bike traffic crashes: a chinese case-control study","volume":"21","author":"Qian","year":"2020","journal-title":"Traffic Inj. Prev"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1710.09543","article-title":"A deep learning approach to the prediction of short-term traffic accident risk","author":"Ren","year":"2017","journal-title":"arXiv"},{"key":"B36","first-page":"91","article-title":"\u201cFaster R-CNN: towards real-time object detection with region proposal networks,\u201d","volume-title":"Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1","author":"Ren","year":"2015"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1109\/ACVMOT.2005.107","article-title":"\u201cSemi-supervised self-training of object detection models,\u201d","author":"Rosenberg","year":"2005","journal-title":"2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV\/MOTION'05) - Volume 1"},{"volume-title":"Intelligent Surveillance System For Riders Without Helmet And Triple Riding Detection On Two Wheelers","year":"2020","author":"Saumya","key":"B38"},{"key":"B39","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TITS.2016.2568920","article-title":"Looking at intersections: a survey of intersection monitoring, behavior and safety analysis of recent studies","volume":"18","author":"Shirazi","year":"2016","journal-title":"IEEE Trans. Intellig. Transp. Syst"},{"key":"B40","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1109\/TITS.2011.2159857","article-title":"Efficient data propagation in traffic-monitoring vehicular networks","volume":"12","author":"Skordylis","year":"2011","journal-title":"IEEE Trans. Intellig. Transp. Syst"},{"key":"B41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/NEUREL.2018.8586996","article-title":"\u201cDetection of traffic violations of road users based on convolutional neural networks,\u201d","volume-title":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","author":"\u0160pa\u0148hel","year":"2018"},{"key":"B42","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1109\/TENCON.2018.8650252","article-title":"\u201cDetection of cyclists' violation of stop sign rules using smartphone sensors,\u201d","volume-title":"TENCON 2018-2018 IEEE Region 10 Conference","author":"Tanaka","year":"2018"},{"key":"B43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3156993","article-title":"Vehicle inertial tracking via mobile crowdsensing: Experience and enhancement","volume":"71","author":"Tong","year":"2022","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1109\/ADICS58448.2024.10533619","article-title":"\u201cYOLOv8: a novel object detection algorithm with enhanced performance and robustness,\u201d","author":"Varghese","year":"2024","journal-title":"2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)"},{"key":"B45","doi-asserted-by":"publisher","first-page":"6784","DOI":"10.1109\/TMC.2022.3195706","article-title":"Spatiotemporal urban inference and prediction in sparse mobile crowdsensing: a graph neural network approach","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Trans. Mobile Comp"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1109\/TMC.2021.3093552","article-title":"Towards privacy-driven truthful incentives for mobile crowdsensing under untrusted platform","volume":"22","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"B47","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MCOM.001.1800674","article-title":"When mobile crowdsensing meets privacy","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Commun. Magaz"},{"key":"B48","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1145\/2384716.2384777","article-title":"\u201cA programmatic introduction to Neo4j,\u201d","volume-title":"Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Software for Humanity","author":"Webber","year":"2012"},{"key":"B49","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/ICITE.2017.8056908","article-title":"\u201cA model of traffic accident prediction based on convolutional neural network,\u201d","volume-title":"2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","author":"Wenqi","year":"2017"},{"key":"B50","first-page":"83","article-title":"\u201cAnalysis of the influencing factors of road environment in road traffic accidents,\u201d","volume-title":"2020 4th Annual International Conference on Data Science and Business Analytics (ICDSBA)","author":"Wu","year":"2020"},{"key":"B51","doi-asserted-by":"publisher","first-page":"25424","DOI":"10.1109\/JIOT.2022.3196808","article-title":"P 2 sim: Privacy-preserving and source-reliable incentive mechanism for mobile crowdsensing","volume":"9","author":"Yan","year":"2022","journal-title":"IEEE Intern. Things J"},{"key":"B52","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3390\/su9020237","article-title":"The influence of household heterogeneity factors on the green travel behavior of urban residents in the east china region","volume":"9","author":"Yang","year":"2017","journal-title":"Sustainability"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505","article-title":"Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting","author":"Yu","year":"2017","journal-title":"arXiv"},{"key":"B54","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1145\/3219819.3219922","article-title":"\u201cHetero-convlstm: a deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data,\u201d","author":"Yuan","year":"2018","journal-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery"},{"key":"B55","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3141\/2443-14","article-title":"Automated analysis of pedestrians' nonconforming behavior and data collection at an urban crossing","volume":"2443","author":"Zaki","year":"2014","journal-title":"Transp. Res. Rec"},{"key":"B56","doi-asserted-by":"publisher","first-page":"3569","DOI":"10.1109\/TIFS.2022.3207905","article-title":"Enabling efficient and strong privacy-preserving truth discovery in mobile crowdsensing","volume":"17","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Inform. Forens. Secur"},{"key":"B57","doi-asserted-by":"publisher","first-page":"4607","DOI":"10.1109\/TMC.2022.3157603","article-title":"Crowdfl: Privacy-preserving mobile crowdsensing system via federated learning","volume":"22","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Mobile Comp"},{"key":"B58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3498321","article-title":"Compressive sensing based distributed data storage for mobile crowdsensing","volume":"18","author":"Zhou","year":"2022","journal-title":"ACM Trans. Sensor Netw"},{"key":"B59","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: a survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"}],"container-title":["Frontiers in Big Data"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2025.1440816\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T09:23:30Z","timestamp":1742549010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2025.1440816\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":59,"alternative-id":["10.3389\/fdata.2025.1440816"],"URL":"https:\/\/doi.org\/10.3389\/fdata.2025.1440816","relation":{},"ISSN":["2624-909X"],"issn-type":[{"type":"electronic","value":"2624-909X"}],"subject":[],"published":{"date-parts":[[2025,3,21]]},"article-number":"1440816"}}