{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:27:39Z","timestamp":1772141259148,"version":"3.50.1"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each road segment has an associated travel-cost time series, which is derived from GPS data.<\/jats:p>\n          <jats:p>We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies with a substantial GPS data set offer insight into the design properties of the proposed framework and algorithms, demonstrating the effectiveness and efficiency of travel cost inferencing.<\/jats:p>","DOI":"10.14778\/2536360.2536375","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"769-780","source":"Crossref","is-referenced-by-count":110,"title":["Travel cost inference from sparse, spatio temporally correlated time series using Markov models"],"prefix":"10.14778","volume":"6","author":[{"given":"Bin","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Aarhus University, Denmark"}]},{"given":"Chenjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aarhus University, Denmark"}]},{"given":"Christian S.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aarhus University, Denmark"}]}],"member":"320","published-online":{"date-parts":[[2013,7]]},"reference":[{"issue":"2","key":"e_1_2_1_1_1","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1061\/(ASCE)0733-947X(2002)128:2(182)","article-title":"Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels","volume":"128","author":"Ahn K.","year":"2002","journal-title":"Journal of Transportation Engineering"},{"key":"e_1_2_1_2_1","volume-title":"Pattern recognition and machine learning","author":"Bishop C.","year":"2006"},{"key":"e_1_2_1_3_1","unstructured":"M. Brand. Coupled hidden Markov models for modeling interacting processes. Technical report MIT Media Lab.  M. Brand. Coupled hidden Markov models for modeling interacting processes. Technical report MIT Media Lab."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1145\/1353343.1353371","article-title":"Finding time-dependent shortest paths over large graphs","author":"Ding B.","year":"2008","journal-title":"EDBT"},{"key":"e_1_2_1_5_1","first-page":"269","volume-title":"GIS","author":"Guo C.","year":"2012"},{"key":"e_1_2_1_6_1","first-page":"10","volume-title":"ICDE","author":"Kanoulas E.","year":"2006"},{"key":"e_1_2_1_7_1","volume-title":"California","author":"Kwon J.","year":"2000"},{"key":"e_1_2_1_8_1","first-page":"792","volume-title":"ICDE","author":"Malviya N.","year":"2011"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511809071","volume-title":"Introduction to information retrieval","author":"Manning C.","year":"2008"},{"issue":"3","key":"e_1_2_1_10_1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s12544-009-0013-6","article-title":"An off-line map-matching algorithm for incomplete map databases","volume":"1","author":"Pereira F.","year":"2009","journal-title":"European Transport Research Review"},{"issue":"2","key":"e_1_2_1_11_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A tutorial on hidden Markov models and selected applications in speech recognition","volume":"77","author":"Rabiner L.","year":"1989","journal-title":"Proceedings of the IEEE"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1049\/ip-smt:20000851","article-title":"Learning interaction dynamics with coupled hidden Markov models","volume":"147","author":"Rezek I.","year":"2000","journal-title":"IEE Proceedings-Science, Measurement and Technology"},{"key":"e_1_2_1_13_1","first-page":"385","volume-title":"SIGMOD Conference","author":"Wang P.","year":"2011"},{"key":"e_1_2_1_14_1","first-page":"316","article-title":"Driving with knowledge from the physical world","author":"Yuan J.","year":"2011","journal-title":"KDD"},{"key":"e_1_2_1_15_1","volume-title":"TKDE","author":"Yang B.","year":"2013"},{"issue":"2","key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1061\/(ASCE)0733-947X(2006)132:2(114)","article-title":"Short-term freeway traffic flow prediction: Bayesian combined neural network approach","volume":"132","author":"Zheng W.","year":"2006","journal-title":"Journal of Transportation Engineering"},{"key":"e_1_2_1_17_1","volume-title":"Texas","author":"Zhong S.","year":"2001"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2536360.2536375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:53:25Z","timestamp":1672221205000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2536360.2536375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7]]},"references-count":17,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2013,7]]}},"alternative-id":["10.14778\/2536360.2536375"],"URL":"https:\/\/doi.org\/10.14778\/2536360.2536375","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2013,7]]}}}