{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T12:54:40Z","timestamp":1773060880796,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871329"],"award-info":[{"award-number":["41871329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science and Technology Planning Project of Guangdong Province","award":["2018B020207005"],"award-info":[{"award-number":["2018B020207005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.<\/jats:p>","DOI":"10.3390\/rs13244994","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T23:30:00Z","timestamp":1639006200000},"page":"4994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator"],"prefix":"10.3390","volume":"13","author":[{"given":"Qing","family":"Li","sequence":"first","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Zhanzhan","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Jiasong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"Urban Intelligent Traffic Safety Operation and Maintenance Research Institute, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Jiaxin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Tianzhu","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1002\/atr.1397","article-title":"Developing evasive action-based indicators for identifying pedestrian conflicts in less organized traffic environ ents","volume":"50","author":"Tageldin","year":"2016","journal-title":"J. Adv. Transp."},{"key":"ref_2","first-page":"169","article-title":"Design of Vehicle Accident Alarm System for Sudden Traffic Accidents","volume":"7","author":"Wang","year":"2021","journal-title":"World Sci. Res. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1061\/(ASCE)0733-947X(2007)133:3(149)","article-title":"Freeway Accident Likelihood Prediction Using a Panel Data Analysis Approach","volume":"133","author":"Qi","year":"2007","journal-title":"J. Transp. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106309","DOI":"10.1016\/j.aap.2021.106309","article-title":"Multi-type Bayesian hierarchical modeling of traffic conflict extremes for crash estimation","volume":"160","author":"Fu","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ssci.2018.08.029","article-title":"Exploratory study involving observation of traffic behaviour and conflicts in Nigeria using the Traffic Conflict Technique","volume":"110","author":"Uzondu","year":"2018","journal-title":"Saf. Sci."},{"key":"ref_6","first-page":"228","article-title":"Traffic Conflict Technique Development for Traffic Safety Evaluation under Mixed Traffic Conditions of Developing Countries","volume":"5","author":"Vuong","year":"2017","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_7","unstructured":"Olszewski, P., Osi\u0144ska, B., Szaga\u0142a, P., W\u0142odarek, P., Niesen, S., Kidholm, O., Madsen, T., Van Haperen, W., Johnsson, C., and Laureshyn, A. (2016). Review of Current Study Methods for VRU Safety. Part 1\u2013Main Report, University of Technology."},{"key":"ref_8","unstructured":"Hayward, J.C. (1972, January 17\u201321). Near miss determination through use of a scale of danger. Proceedings of the 51 Annual Meeting of the Highway Research Board, Washington, DC, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cooper, P.J. (1984). Experience with Traffic Conflicts in Canada with Emphasis on Post Encroachment Time Techniques. International Calibration Study of Traffic Conflict Techniques, Springer.","DOI":"10.1007\/978-3-642-82109-7_8"},{"key":"ref_10","first-page":"306","article-title":"A Conflict simulation model","volume":"17","author":"Cooper","year":"1976","journal-title":"Traffic Eng. Control"},{"key":"ref_11","first-page":"1263","article-title":"Mapping Pedestrian-Vehicle Behavior at Urban Undesignated Mid-Block Crossings under Mixed Traffic Environment\u2014A Trajectory-Based Approach","volume":"48","author":"Golakiya","year":"2020","journal-title":"Transp. Res. Proc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11270-017-3593-0","article-title":"Fabrication of Tannin-Based Dithiocarbamate Biosorbent and Its Application for Ni(II) Ion Removal","volume":"228","author":"Zhao","year":"2017","journal-title":"Water Air Soil Pollut."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1757-899X\/1175\/1\/012005","article-title":"Dynamic time distribution system monitoring on traffic light using image processing and convolutional neural network method","volume":"1175","author":"Rifai","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1504\/IJSNET.2021.113842","article-title":"Application of convolutional neural networks and image processing algorithms based on traffic video in vehicle taillight detection","volume":"35","author":"Cao","year":"2021","journal-title":"Int. J. Sens. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bautista, C.M., Dy, C.A., Manalac, M.I., Orbe, R.A., and Cordel, M. (2016, January 9\u201311). Convolutional Neural Network for Vehicle Detection in Low Resolution Traffic Videos. Proceedings of the 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia.","DOI":"10.1109\/TENCONSpring.2016.7519418"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201312). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","first-page":"1","article-title":"Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN","volume":"2017","author":"Xu","year":"2017","journal-title":"J. Adv. Transp."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 19\u201325). Sparse R-CNN: End-to-End Object Detection with Learnable Proposals. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"122774","DOI":"10.1109\/ACCESS.2021.3109606","article-title":"A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn","volume":"9","author":"Cao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","unstructured":"Grabner, H., and Bischof, H. (2006, January 17\u201322). On-Line Boosting and Vision. Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object Detection with Discriminatively Trained Part-Based Models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TPAMI.2011.239","article-title":"Tracking-Learning-Detection","volume":"34","author":"Kalal","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Lim, J., and Yang, M. (2013, January 23\u201328). Online Object Tracking: A Benchmark. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yun, S., Choi, J., Yoo, Y., and Yun, K. (2017, January 22\u201325). Action-Decision Networks for Visual object with Deep Reinforcement Learning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.148"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fan, H., and Ling, H. (2017, January 22\u201325). Sanet: Structure-Aware Network for Visual object. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.275"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","article-title":"Deep visual object: Review and experimental comparison","volume":"76","author":"Li","year":"2018","journal-title":"Pattern Recognition"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4855","DOI":"10.1007\/s12652-018-01171-4","article-title":"The visual object tracking algorithm research based on adaptive combination kernel","volume":"10","author":"Chen","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/0952813X.2019.1647565","article-title":"An improved multi-objective antlion optimization algorithm for the optimal design of the robotic gripper","volume":"32","author":"Mahanta","year":"2020","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_34","first-page":"119","article-title":"Improved Faster RCNN Approach for Vehicles and Pedestrian Detection","volume":"6","author":"Ping","year":"2020","journal-title":"Int. Core J. Eng."},{"key":"ref_35","first-page":"100106","article-title":"Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach","volume":"24","author":"Zheng","year":"2019","journal-title":"Anal. Methods Accid. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Puri, A., Valavanis, K.P., and Kontitsis, M. (2007, January 27\u201329). Statistical profile generation for traffic monitoring using real-time UAV based video data. Proceedings of the 2007 Mediterranean Conference on Control & Automation, Athens, Greece.","DOI":"10.1109\/MED.2007.4433658"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.4028\/www.scientific.net\/AMM.587-589.2224","article-title":"Research on Traffic Safety on Freeway Merging Sections Based on TTC and PET","volume":"587","author":"Meng","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9089817","DOI":"10.1155\/2020\/9089817","article-title":"In Search of the Consequence Severity of Traffic Conflict","volume":"2020","author":"Jiang","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.trc.2015.04.007","article-title":"Large-scale automated proactive road safety analysis using video data","volume":"58","author":"Saunier","year":"2015","journal-title":"Transp. Res. Part C"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105264","DOI":"10.1016\/j.aap.2019.105264","article-title":"Estimation of traffic conflicts using precise lateral position and width of vehicles for safety assessment","volume":"132","author":"Charly","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Goecke, R., Asthana, A., Pettersson, N., and Pettersson, L. (2007, January 13\u201315). Visual vehicle egomotion estimation using the fourier-mellin transform. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290156"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","first-page":"1097","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","first-page":"61","article-title":"A Method of Identifying Serious Conflicts of Motor and Non-motor Vehicles during Passing Maneuvers","volume":"4","author":"Yaodong","year":"2015","journal-title":"J. Transp. Inform. Safety"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4994\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:43:37Z","timestamp":1760168617000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,8]]},"references-count":44,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13244994"],"URL":"https:\/\/doi.org\/10.3390\/rs13244994","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,8]]}}}