1. bookVolume 21 (2020): Issue 4 (December 2020)
Journal Details
License
Format
Journal
eISSN
1407-6179
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English
Open Access

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Published Online: 26 Nov 2020
Volume & Issue: Volume 21 (2020) - Issue 4 (December 2020)
Page range: 255 - 264
Journal Details
License
Format
Journal
eISSN
1407-6179
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English

1. Tao, S., Corcoran, J., Hickman, M., Stimson, R. (2016) The influence of weather on local geographical patterns of bus usage. Journal of Transport Geography, 54, 66–80. DOI: 10.1016/j.jtrangeo.2016.05.009.10.1016/j.jtrangeo.2016.05.009Search in Google Scholar

2. Stover, V., McCormack, E. (2015) The Impact of Weather on Bus Ridership in Pierce County, Washington. Journal of Public Transportation, 15(1), 95–110. DOI: 10.5038/2375-0901.15.1.6.10.5038/2375-0901.15.1.6Search in Google Scholar

3. Li, J., Li, X., Chen, D., Godding, L. (2018) Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing. Journal of Transport Geography, 66(35), 356–368. DOI: 10.1016/j.jtrangeo.2017.10.023.10.1016/j.jtrangeo.2017.10.023Search in Google Scholar

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, 17–29. DOI: 10.1016/j.trc.2016.12.001.10.1016/j.trc.2016.12.001Search in Google Scholar

5. Singhal, A., Kamga, C., Yazici, A. (2014) Impact of weather on urban transit ridership. Transportation Research Part A: Policy and Practice, 69, 379–391. DOI:10.1016/j.tra.2014.09.008Search in Google Scholar

6. Guo, Z., Wilson, N., Rahbee, A. (2008) Impact of Weather on Transit Ridership in Chicago, Illinois. Transportation Research Record: Journal of the Transportation Research Board, 2034, 3–10.10.3141/2034-01Search in Google Scholar

7. Guo, Z., Wilson, N. H., Rahbee, A. (2007) The Impact of Weather on Transit Ridership Chicago. TRB Annual Meeting, pages 3–10.10.3141/2034-01Search in Google Scholar

8. Kalkstein, A. J., Kuby, M., Gerrity, D., Clancy, J. J. (2009) An analysis of air mass effects on rail ridership in three US cities. Journal of Transport Geography, 17(3), 198–207. DOI: 10.1016/j.jtrangeo.2008.07.003.10.1016/j.jtrangeo.2008.07.003Search in Google Scholar

9. Cools, M., Moons, E., Creemers, L., Wets, G. (2010) Changes in Travel Behavior in Response to Weather Conditions. Transportation Research Record: Journal of the Transportation Research Board 2157, 22–28.10.3141/2157-03Search in Google Scholar

10. Sabir, M., van Ommeren, J., Koetse, M. J., Rietveld, P. (2010) Impact of weather on daily travel demand, p.25, ID: 2060163.Search in Google Scholar

11. Costa, V., Fontes, T., Borges, J., Dias, T. (2017) Impacts of Weather Conditions in Urban Public Transport: Understanding the Effects of Climatic Changes using Big Data. Transportation Research Board, Washington D.C.Search in Google Scholar

12. Arana, P., Cabezudo, S., Peñalba, M. (2014) Influence of weather conditions on transit ridership: A statistical study using data from Smartcards. Transportation Research Part A: Policy and Practice 59, 1–12. DOI: 10.1016/j.tra.2013.10.019.10.1016/j.tra.2013.10.019Search in Google Scholar

13. Kalkstein, A. J., Kuby, M., Gerrity, D., Clancy, J. J. (2009) An analysis of air mass effects on rail ridership in three US cities. Journal of Transport Geography 17(3), 198–207. DOI: 10.1016/j.jtrangeo.2008.07.003.10.1016/j.jtrangeo.2008.07.003Search in Google Scholar

14. Yagi, S., Mohammadian, A. (2008) Policy simulation for New BRT and area pricing alternatives using an opinion survey in Jakarta. Transportation Planning and Technology 31(5), 589–612. DOI:10.1080/03081060802087676.10.1080/03081060802087676Search in Google Scholar

15. Mahrsi, M. K. E. (2014) A Novel Method of Multi-Information Acquisition for Electromagnetic Flow Meters. Sensors, 16(1), 25. DOI:10.3390/s16010025.10.3390/s16010025473205826712762Search in Google Scholar

16. Hjorthol, R. (2013) Winter weather – an obstacle to older people’s activities? Journal of Transport Geography 28, 186–191. DOI: 10.1016/j.jtrangeo.2012.09.003.10.1016/j.jtrangeo.2012.09.003Search in Google Scholar

17. Zupan, J., Gasteiger, J. (1993) Neural Networks for Chemists: An Introduction. John Wiley Sons, Inc., USA.Search in Google Scholar

18. Cochocki, A., Unbehauen, R. (1993) Neural Networks for Optimization and Signal Processing. John Wiley Sons, Inc., USA, 1st edition.Search in Google Scholar

19. Rowland, Z., Vrbka, J. (2016) Using artificial neural networks for prediction of key indicators of a company in global world. In T. Klieštik, editor, Globalization and its socio-economic consequences, 16th international scientific conference proceedings, PTS I-V, pages 1896–1903, Žilina, Slovensko.Search in Google Scholar

20. Chollet, F. (2015) keras. https://github.com/fchollet/keras.Search in Google Scholar

21. Creemers, L., Wets, G., Cools, M. (2015) Meteorological variation in daily travel behaviour: evidence from revealed preference data from the Netherlands. Theoretical and Applied Climatology, 120(1-2), 183–194. DOI: 10.1007/s00704-014-1169-0.10.1007/s00704-014-1169-0Search in Google Scholar

22. de Montigny, L., Ling, R., Zacharias. J. (2011) The Effects of Weather on Walking Rates in Nine Cities. Environment and Behavior, 44(6), 821–840. DOI: 10.1177/0013916511409033.10.1177/0013916511409033Search in Google Scholar

23. Schmiedeskamp, P., Zhao, W. (2016) Estimating Daily Bicycle Counts in Seattle, Washington, from Seasonal and Weather Factors. Transportation Research Record: Journal of the Transportation Research Board, 2593(1), 94–102. DOI: 10.3141/2593-12.10.3141/2593-12Search in Google Scholar

24. Maas, A.L., Ng. A.Y. (2013) Rectifier non linearities improve neural network acoustic models. 28. Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, JMLR: W---amp---CP volume 28.Search in Google Scholar

25. Li, J., Li, X., Chen, D. and Godding L. (2018) Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing. Journal of Transport Geography, 66(35):356–368. DOI:10.1016/j.jtrangeo.2017.10.023.10.1016/j.jtrangeo.2017.10.023Search in Google Scholar

26. Correia, R., Fontes, T., Luís Borges, J., (2020). Forecasting of urban public transport demand based on weather conditions. Advances in Mobility as a Service Systems, Springer, Book Series Advances in Intelligent Systems and Computing, Edited by Nathanail E.G., Adamos, G., Karakikes, I., in press.Search in Google Scholar

27. Mukai N. and Yoden N. (2012) Taxi Demand Forecasting Based on Taxi Probe Data by Neural Network. In: Watanabe T., Watada J., Takahashi N., Howlett R., Jain L. (eds) Intelligent Interactive Multimedia: Systems and Services. Smart Innovation, Systems and Technologies, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29934-6_57.10.1007/978-3-642-29934-6_57Search in Google Scholar

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