{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:41:31Z","timestamp":1761709291076,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T00:00:00Z","timestamp":1478217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient\u2013boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient\u2013boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient\u2013boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.<\/jats:p>","DOI":"10.3390\/ijgi5110201","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T11:18:38Z","timestamp":1478258318000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations"],"prefix":"10.3390","volume":"5","author":[{"given":"Faming","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Xinyan","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Tao","family":"Hu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Lingjia","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1080\/15472450802644447","article-title":"Feasibility of using taxi dispatch system as probes for collecting traffic information","volume":"13","author":"Liu","year":"2009","journal-title":"J. Intell. Transp. Syst. Technol. Plan. Oper."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zheng, Y., and Xue, Y. (2014, January 24\u201327). Travel time estimation of a path using sparse trajectories. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623656"},{"key":"ref_3","unstructured":"Li, J. (2012). Estimation and Prediction of Link Travel Time for Urban Trunk and Secondary Street. [Ph.D. Thesis, Jilin University]. (in Chinese)."},{"key":"ref_4","first-page":"909","article-title":"Real-time map matching algorithm based on low-sampling-rate probe vehicle data","volume":"39","author":"Yao","year":"2012","journal-title":"Beijing J. Beijing Univ. Tech."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, Y., Yuan, J., and Xie, X. (2011, January 17\u201321). Urban computing with taxicabs. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China.","DOI":"10.1145\/2030112.2030126"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, F., and Hsieh, H.P. (2013, January 10\u201313). U-Air: When urban air quality inference meets big data. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia.","DOI":"10.1145\/2487575.2488188"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.trc.2010.10.002","article-title":"Real-time road traffic prediction with spatio-temporal correlations","volume":"19","author":"Min","year":"2011","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1016\/j.trc.2010.10.005","article-title":"A Bayesian dynamic linear model approach for real-time short-term freeway travel time prediction","volume":"19","author":"Fei","year":"2011","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.trc.2013.05.008","article-title":"Efficient missing data imputing for traffic flow by considering temporal and spatial dependence","volume":"34","author":"Li","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3141\/2160-07","article-title":"Freeway travel time ground truth data collection using bluetooth sensors","volume":"2160","author":"Haghani","year":"2010","journal-title":"J. Transp. Res. Board"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3141\/1644-14","article-title":"Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models","volume":"1644","author":"Williams","year":"1998","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1061\/(ASCE)0733-947X(1997)123:4(261)","article-title":"Traffic flow forecasting: Comparison of modeling approaches","volume":"123","author":"Smith","year":"1997","journal-title":"J. Transp. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.trc.2012.08.004","article-title":"Short-term traffic speed forecasting hybrid model based on Chaos-wavelet analysis-support vector machine theory","volume":"27","author":"Wang","year":"2012","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.trc.2011.06.009","article-title":"Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks","volume":"21","author":"Wei","year":"2012","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/TITS.2013.2256132","article-title":"Freeway travel-time estimation based on temporal\u2013spatial queueing model","volume":"14","author":"Li","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.trc.2013.11.011","article-title":"A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model","volume":"43","author":"Zhang","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","unstructured":"Montgomery, D.C., Jennings, C.L., and Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting, John Wiley & Sons."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1016\/j.neucom.2010.12.032","article-title":"Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm","volume":"74","author":"Hong","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.trc.2009.04.007","article-title":"Bayesian committee of neural networks to predict travel times with confidence intervals","volume":"17","year":"2009","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.trc.2013.05.012","article-title":"Dynamic data-driven local traffic state estimation and prediction","volume":"34","author":"Antoniou","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","article-title":"Short-term traffic forecasting: Where we are and where we\u2019re going","volume":"43","author":"Vlahogianni","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2012). Ensemble Methods: Foundations and Algorithms, CRC Press.","DOI":"10.1201\/b12207"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hamner, B. (2010, January 14\u201317). Predicting travel times with context-dependent random forests by modeling local and aggregate traffic flow. Proceedings of the ICDMW 2010, Sydney, Australia.","DOI":"10.1109\/ICDMW.2010.128"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Y. (2011, January 25\u201329). Prediction of weather impacted airport capacity using ensemble learning. Proceedings of the Digital Avionics Systems Conference (DASC) 2011, Sacramento, CA, USA.","DOI":"10.1109\/DASC.2011.6096002"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3141\/2386-04","article-title":"Application of stochastic gradient boosting technique to enhance reliability of real-time risk assessment","volume":"2386","author":"Ahmed","year":"2013","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.aap.2012.08.015","article-title":"Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees","volume":"61","author":"Chung","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transp. Res. Part C Emerg. Technol.","DOI":"10.1016\/j.trc.2015.02.019"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","article-title":"Ensemble based systems in decision making","volume":"6","author":"Polikar","year":"2006","journal-title":"IEEE Circ. Syst. Mag."},{"key":"ref_29","unstructured":"Leistner, C., Saffari, A., Santner, J., and Bischof, H. (March, January 27). Semi-supervised random forests. Proceedings of the IEEE 12th International Conference on Computer Vision, Porto, Portugal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1037\/a0016973","article-title":"An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests","volume":"14","author":"Strobl","year":"2009","journal-title":"Psychol. Method."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.ejor.2005.12.009","article-title":"A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem","volume":"177","author":"Ruiz","year":"2007","journal-title":"Eur. J. Operat. Res."},{"key":"ref_34","unstructured":"Breiman, L. (1997). Statistics Department, University of California at Berkeley. Technical Report 486."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Annal. Stat., 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_37","unstructured":"Mason, L., Baxter, J., Bartlett, P.L., and Frean, M. (December, January 29). Boosting algorithms as gradient descent in function space. Proceedings of the NIPS 1999, Denver, CO, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Front. Neurorobot.","DOI":"10.3389\/fnbot.2013.00021"},{"key":"ref_39","first-page":"328","article-title":"On the distribution of the correlation coefficient in small samples. Appendix II to the papers of\" Student\" and RA Fisher","volume":"11","author":"Soper","year":"1917","journal-title":"Biometrika"},{"key":"ref_40","unstructured":"Box, G., and Jenkins, G. (1970). Time Series Analysis: Forecasting and Control, Holden-Day."},{"key":"ref_41","first-page":"119","article-title":"A review of travel time estimation and forecasting for advanced traveller information systems","volume":"11","author":"Mori","year":"2015","journal-title":"Transp. A Transp. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/TITS.2012.2186295","article-title":"Matching raw GPS measurements on a navigable map without computing a global position","volume":"13","author":"Fouque","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/13658816.2013.816427","article-title":"Map-matching algorithm for large-scale low-frequency floating car data","volume":"28","author":"Chen","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, C., Xie, X., and Sun, G.Z. (2010, January 23\u201326). An interactive-voting based map matching algorithm. Proceedings of the Eleventh International Conference on Mobile Data Management, Kansas City, MI, USA.","DOI":"10.1109\/MDM.2010.14"},{"key":"ref_45","first-page":"933","article-title":"Automated matching urban road networks using probabilistic relaxation","volume":"41","author":"Zhang","year":"2012","journal-title":"Acta Geod. Catogr. Sin."},{"key":"ref_46","first-page":"805","article-title":"Flowing car data map-matching based on constrained shortest path algorithm","volume":"7","author":"Li","year":"2013","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_47","first-page":"965","article-title":"Individual vehicle travel-time estimation based on GPS data and analysis of vehicle running characteristics","volume":"40","author":"Yu","year":"2010","journal-title":"J. Jilin Univ."},{"key":"ref_48","first-page":"426","article-title":"Estimation of average link travel time using fuzzy C-mean","volume":"27","author":"Dong","year":"2011","journal-title":"Bull. Sci. Technol."},{"key":"ref_49","first-page":"182","article-title":"Comparison of link travel-time estimation methods based on GPS equipped floating car","volume":"39","author":"Jiang","year":"2009","journal-title":"J. Jilin Univ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10109-012-0166-z","article-title":"Understanding intra-urban trip patterns from taxi trajectory data","volume":"14","author":"Liu","year":"2012","journal-title":"J. Geogr. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jtrangeo.2015.01.016","article-title":"Revealing travel patterns and city structure with taxi trip data","volume":"43","author":"Liu","year":"2013","journal-title":"J. Transp. Geogr."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhu, X., Guo, W., Ye, X., Hu, T., and Huang, L. (2016). Analyzing urban human mobility patterns through a thematic model at a finer scale. ISPRS Int. J. Geo-Inf.","DOI":"10.3390\/ijgi5060078"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/10095020.2015.1126071","article-title":"What about people in pedestrian navigation?","volume":"18","author":"Fang","year":"2015","journal-title":"Geo-spat. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learn."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tsay, R.S. (2005). Analysis of Financial Time Series, John Wiley & Sons.","DOI":"10.1002\/0471746193"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/11\/201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:34:46Z","timestamp":1760211286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/11\/201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,4]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["ijgi5110201"],"URL":"https:\/\/doi.org\/10.3390\/ijgi5110201","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2016,11,4]]}}}