Open AccessModelling multiple quantiles together with the mean based on SA‐ConvLSTM for taxi pick‐up prediction
Author(s) -
Chen Qixiang,
Lv Bin,
Hao Binbin,
Luo Weizhuang,
Lang Binke,
Li Xu
Publication year - 2022
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12238
Subject(s) - quantile , mean squared error , taxis , computer science , truncated mean , artificial neural network , artificial intelligence , quantile regression , mean squared prediction error , statistics , data mining , machine learning , mathematics , engineering , estimator , transport engineering
A more complete taxi pick‐ups prediction going beyond the conditional expectation would be more beneficial to allocate taxis effectively. Prior works have predicted the conditional expectation of taxi demand and improved the prediction accuracy. But the ability to convey calibrated uncertainty estimates in prediction was less studied. Here, a multi‐output multi‐quantile deep neural network architecture is presented for predicting the mean and the multiple quantiles simultaneously, which jointly models multiple quantiles together with the conditional expectation based on self‐attention ConvLSTM. Using the NYC taxi dataset, we have analyzed the performance of the developed architecture. For the mean prediction, the proposed method has an error of 8.584 in terms of RMSE criteria and Linear QR, Mcdropout‐Gal, ConvLSTM and SA‐ConvLSTM methods, which have error values of 8.800, 8.671, 8.599, and 8.597, respectively. According to the MAE criterion, this method has a value of 5.934. The other methods have obtained values of 6.614, 5.990, 5.986, and 5.959, respectively. For multiple quantiles prediction, the proposed method produces narrower intervals while obtaining the target coverage percentage. Overall, the proposed model not only outperforms the other models, but also provides a rich description about the predictive density.