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- Title
Hotspot Forecast of Taxi Demand Based on WCGAN.
- Authors
WANG Bowei; DENG Jun; LYU Bin
- Abstract
Empty taxis affect the allocation of traffic resources and the income of taxi drivers. Accurate taxi demand hot spot prediction can effectively guide taxi drivers to optimize the route for passengers. Aiming at this problem, a taxi based on conditional generative adversarial network is proposed. Demand hot spot prediction method, which is based on the wasserstein conditional generative adversarial network(WCGAN), uses the convolutional long short-term memory neural network (ConvLSTM) in the generator to capture the long-term dependencies of the time series, and uses the temporal discriminator and spatial discriminator respectively. The discriminator extracts the spatio-temporal characteristics of passenger historical demand distribution. Using Lanzhou taxi trajectory data, the method proposed in this paper is compared with three algorithms: long short-term memory neural network (LSTM) algorithm, spatio-temporal residual network (ST-ResNet) and BP neural network (BPNN). The average absolute error is reduced by 17.3%, 8.4% and 10.3% respectively.
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 12, p293
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2203-0274