{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T05:53:54Z","timestamp":1723355634392},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41730105","42071452"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["2020zzts687"],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["2020JJ4696"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend to apply deep learning models; these methods are unexplainable and ignore the a priori characteristics of traffic flow. To address these issues, a temporal directed graph convolution network (T-DGCN) is proposed. A directed graph is first constructed to model the movement characteristics of vehicles, and based on this, a directed graph convolution operator is used to capture spatial dependence. For temporal dependence, we couple a keyframe sequence and transformer to learn the tendencies and periodicities of traffic flow. Using a real-world dataset, we confirm the superior performance of the T-DGCN through comparative experiments. Moreover, a detailed discussion is presented to provide the path of reasoning from the data to the model design to the conclusions.<\/jats:p>","DOI":"10.3390\/ijgi10090624","type":"journal-article","created":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T01:23:29Z","timestamp":1631928209000},"page":"624","source":"Crossref","is-referenced-by-count":7,"title":["A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Kaiqi","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410083, China"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410083, China"}]},{"given":"Yan","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1080\/13658816.2019.1697879","article-title":"A novel residual graph convolution deep learning model for short-term network-based traffic forecasting","volume":"34","author":"Zhang","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zambrano-Martinez, J.L., Calafate, C.T., Soler, D., Lemus-Z\u00fa\u00f1iga, L.G., Cano, J.C., Manzoni, P., and Gayraud, T. (2019). A centralized route-management solution for autonomous vehicles in urban areas. Electronics, 8.","DOI":"10.3390\/electronics8070722"},{"key":"ref_4","unstructured":"Chen, C., Li, K., Teo, S.G., Zou, X., Wang, K., Wang, J., and Zeng, Z. (February, January 27). Gated residual recurrent graph neural networks for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/3231541.3231544","article-title":"A brief overview of machine learning methods for short-term traffic forecasting and future directions","volume":"10","author":"Li","year":"2018","journal-title":"SIGSPATIAL Spec."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., and Zhang, J. (2019, January 4\u20138). Urban traffic prediction from spatio-temporal data using deep meta learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330884"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"05002","DOI":"10.1051\/matecconf\/20168105002","article-title":"A comparative study of urban road traffic simulators","volume":"81","author":"Saidallah","year":"2016","journal-title":"MATEC Web Conf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1111\/cgf.13803","article-title":"A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving","volume":"39","author":"Chao","year":"2020","journal-title":"Comput. Graph. Forum"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/TVCG.2010.27","article-title":"Virtualized traffic: Reconstructing traffic flows from discrete spatiotemporal data","volume":"17","author":"Sewall","year":"2011","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_10","unstructured":"Duan, P., Mao, G., Zhang, C., and Wang, S. (2016, January 1\u20134). STARIMA-based traffic prediction with time-varying lags. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.trc.2020.01.010","article-title":"A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data","volume":"112","author":"Bogaerts","year":"2020","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_12","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_13","unstructured":"Li, Y., Yu, R., Shahabi, C., and Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., and Zhu, Z. (2017). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref_15","unstructured":"Drew, D.R. (1968). Traffic Flow Theory and Control, McGraw-Hill."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"179","DOI":"10.3141\/1678-22","article-title":"Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting","volume":"1678","author":"Lee","year":"1999","journal-title":"Transp. Res. Rec."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","article-title":"Short-term prediction of traffic volume in urban arterials","volume":"121","author":"Hamed","year":"1995","journal-title":"J. Transp. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0968-090X(97)82903-8","article-title":"Combining kohonen maps with arima time series models to forecast traffic flow","volume":"4","author":"Dougherty","year":"1996","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.trc.2014.02.006","article-title":"Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification","volume":"43","author":"Guo","year":"2014","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_20","unstructured":"Vanajakshi, L., and Rilett, L.R. (2004, January 14\u201317). A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. Proceedings of the IEEE Intelligent Vehicles Symposium 2004, Parma, Italy."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Su, H., Zhang, L., and Yu, S. (2007, January 24\u201327). Short-term traffic flow prediction based on incremental support vector regression. Proceedings of the Third International Conference on Natural Computation (ICNC 2007), Haikou, China.","DOI":"10.1109\/ICNC.2007.661"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gopi, G., Dauwels, J., Asif, M.T., Ashwin, S., Mitrovic, N., Rasheed, U., and Jaillet, P. (2013, January 6\u20139). Bayesian support vector regression for traffic speed prediction with error bars. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728223"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0968-090X(01)00004-3","article-title":"Urban traffic flow prediction using a fuzzy-neural approach","volume":"10","author":"Yin","year":"2002","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.trc.2015.11.002","article-title":"A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting","volume":"62","author":"Cai","year":"2016","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/TITS.2006.869623","article-title":"A bayesian network approach to traffic flow forecasting","volume":"7","author":"Sun","year":"2006","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, X., Yu, H., Wang, Y., and Wang, Y. (2015). Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0119044"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tian, Y., and Li, P. (2015, January 19\u201321). Predicting short-term traffic flow by long short-term memory recurrent neural network. Proceedings of the IEEE International Conference on Smart City\/SocialCom\/SustainCom (SmartCity), Chengdu, China.","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref_30","unstructured":"Wu, Y., and Tan, H. (2016). Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv."},{"key":"ref_31","unstructured":"Yu, B., Li, M., Zhang, J., and Zhu, Z. (2019). 3D graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting. arXiv."},{"key":"ref_32","unstructured":"Chen, Z., Wen, J., and Geng, Y. (2016, January 8\u201311). Predicting future traffic using Hidden Markov Models. Proceedings of the 2016 IEEE 24th International Conference on Network Protocols, Singapore."},{"key":"ref_33","unstructured":"Ma, Y., Hao, J., Yang, Y., Li, H., Jin, J., and Chen, G. (2019). Large-scale transportation network congestion evolution prediction using deep learning theory. arXiv."},{"key":"ref_34","unstructured":"Chung, F.R., and Graham, F.C. (1997). Spectral Graph Theory, American Mathematical Society."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., and Qi, D. (2017, January 4\u20139). Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1111\/tgis.12644","article-title":"Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting","volume":"24","author":"Cai","year":"2020","journal-title":"Trans. GIS"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_38","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3141\/1748-12","article-title":"Freeway performance measurement system: Mining loop detector data","volume":"1748","author":"Chen","year":"2001","journal-title":"Transp. Res. Rec."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., and Huang, Y. (2010, January 2\u20135). T-drive: Driving directions based on taxi trajectories. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869807"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.trc.2017.03.017","article-title":"How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China","volume":"79","author":"Nie","year":"2017","journal-title":"Transp. Res. C Emerg. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., and Kr\u00fcger, A. (2012). Urban traffic modelling and prediction using large scale taxi GPS traces. Pervasive Computing, Springer.","DOI":"10.1007\/978-3-642-31205-2"},{"key":"ref_43","unstructured":"(2019, June 16). Shenzhen Municipal Government Data Open Platform, Available online: https:\/\/opendata.sz.gov.cn\/."},{"key":"ref_44","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"3","author":"Liu","year":"2004","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.eswa.2017.04.015","article-title":"Topology-regularized universal vector autoregression for traffic forecasting in large urban areas","volume":"82","author":"Schimbinschi","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_48","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/9\/624\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T22:51:51Z","timestamp":1721343111000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/9\/624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,17]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["ijgi10090624"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10090624","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,17]]}}}
