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VLDB Endow."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:p>In this paper, we revisit the problem of route travel time estimation on a road network and aim to boost its accuracy by capturing and utilizing spatio-temporal features from four significant aspects: heterogeneity, proximity, periodicity and dynamicity.<\/jats:p>\n          <jats:p>Spatial-wise, we consider two forms of heterogeneity at link level in a road network: the turning ways between different links are heterogeneous which can make the travel time of the same link various; different links contain heterogeneous attributes and thereby lead to different travel time. In addition, we take into account the proximity: neighboring links have similar traffic patterns and lead to similar travel speeds. To this end, we build a link-connection graph to capture such heterogeneity and proximity.<\/jats:p>\n          <jats:p>Temporal-wise, the weekly\/daily periodicity of temporal background information (e.g., rush hours) and dynamic traffic conditions have significant impact on the travel time, which result in static and dynamic spatio-temporal features respectively. To capture such impacts, we regard the travel time\/speed as a combination of static and dynamic parts, and extract many spatio-temporal relevant features for the prediction task.<\/jats:p>\n          <jats:p>Talking about the methodology, it remains an open problem to build a generic learning model to boost the estimation accuracy. Hence, we design a novel encoder-decoder framework - The encoder uses the sequence attention model to encode dynamic features from the temporal-wise perspective. The decoder first uses the heterogeneous graph attention model to decode the static part of travel speed based on static spatio-temporal features, and then leverages the sequence attention model to decode the estimated travel time from spatial-wise perspective. Extensive experiments on real datasets verify the superiority of our method as well as the importance of the four aspects outlined above.<\/jats:p>","DOI":"10.14778\/3570690.3570691","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T17:29:55Z","timestamp":1674494995000},"page":"393-405","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Route Travel Time Estimation on a Road Network Revisited"],"prefix":"10.14778","volume":"16","author":[{"given":"Haitao","family":"Yuan","sequence":"first","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Bao","sequence":"additional","affiliation":[{"name":"RMIT University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2021. 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