628 research outputs found
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand
vehicle sharing services. However, predicting passenger demand over multiple
time horizons is generally challenging due to the nonlinear and dynamic
spatial-temporal dependencies. In this work, we propose to model multi-step
citywide passenger demand prediction based on a graph and use a hierarchical
graph convolutional structure to capture both spatial and temporal correlations
simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a short-term encoder to derive the
next-step prediction for generating multi-step prediction; 3) an
attention-based output module to model the dynamic temporal and channel-wise
information. Experiments on three real-world datasets show that our model
consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page
Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System
Designing spatio-temporal forecasting models separately in a task-wise and
city-wise manner poses a burden for the expanding transportation network
companies. Therefore, a multi-task learning architecture is proposed in this
study by developing gated ensemble of spatio-temporal mixture of experts
network (GESME-Net) with convolutional recurrent neural network (CRNN),
convolutional neural network (CNN), and recurrent neural network (RNN) for
simultaneously forecasting spatio-temporal tasks in a city as well as across
different cities. Furthermore, a task adaptation layer is integrated with the
architecture for learning joint representation in multi-task learning and
revealing the contribution of the input features utilized in prediction. The
proposed architecture is tested with data from Didi Chuxing for: (i)
simultaneously forecasting demand and supply-demand gap in Beijing, and (ii)
simultaneously forecasting demand across Chengdu and Xian. In both scenarios,
models from our proposed architecture outperformed the single-task and
multi-task deep learning benchmarks and ensemble-based machine learning
algorithms.Comment: arXiv admin note: text overlap with arXiv:2012.0886
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
- …