{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:40:03Z","timestamp":1778150403991,"version":"3.51.4"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"4","license":[{"start":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:00:00Z","timestamp":1606348800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This work apply a deep learning artificial neural network model \u2013 the Multilayer Perceptron \u2013 as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and\/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).<\/jats:p>","DOI":"10.2478\/ttj-2020-0020","type":"journal-article","created":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T03:44:17Z","timestamp":1607139857000},"page":"255-264","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions"],"prefix":"10.2478","volume":"21","author":[{"given":"T\u00e2nia","family":"Fontes","sequence":"first","affiliation":[{"name":"INESC TEC - Institute for Systems and Computer Engineering, Technology and Science , Porto , Portugal , Rua Dr. Roberto Frias, 4200-465"}]},{"given":"Ricardo","family":"Correia","sequence":"additional","affiliation":[{"name":"INESC TEC and Faculty of Engineering , University of Porto , Porto , Portugal , Rua Dr. Roberto Frias, 4200-465"}]},{"given":"Joel","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"INESC TEC - Institute for Systems and Computer Engineering, Technology and Science , Porto , Portugal , Rua Dr. Roberto Frias, 4200-465"}]},{"given":"Jos\u00e9 Lu\u00eds","family":"Borges","sequence":"additional","affiliation":[{"name":"INESC TEC - Institute for Systems and Computer Engineering, Technology and Science , Porto , Portugal , Rua Dr. Roberto Frias, 4200-465"}]}],"member":"374","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"2026042921072444927_j_ttj-2020-0020_ref_001_w2aab3b7b4b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"1. 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Journal of Transport Geography, 66(35), 356\u2013368. DOI: 10.1016\/j.jtrangeo.2017.10.023.10.1016\/j.jtrangeo.2017.10.023","DOI":"10.1016\/j.jtrangeo.2017.10.023"},{"key":"2026042921072444927_j_ttj-2020-0020_ref_004_w2aab3b7b4b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"4. Zhou, M., Wang, D., Li, Q., Yue, Y., Tu, W., Cao, R. (2017) Impacts of weather on public transport ridership: Results from mining data from different sources. Transportation Research Part C: Emerging Technologies, 75, 17\u201329. DOI: 10.1016\/j.trc.2016.12.001.10.1016\/j.trc.2016.12.001","DOI":"10.1016\/j.trc.2016.12.001"},{"key":"2026042921072444927_j_ttj-2020-0020_ref_005_w2aab3b7b4b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"5. Singhal, A., Kamga, C., Yazici, A. (2014) Impact of weather on urban transit ridership. Transportation Research Part A: Policy and Practice, 69, 379\u2013391. 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