  Urban resource scheduling is an important part of the development of a smart
city, and transportation resources are the main components of urban resources.
Currently, a series of problems with transportation resources such as
unbalanced distribution and road congestion disrupt the scheduling discipline.
Therefore, it is significant to predict travel demand for urban resource
dispatching. Previously, the traditional time series models were used to
forecast travel demand, such as AR, ARIMA and so on. However, the prediction
efficiency of these methods is poor and the training time is too long. In order
to improve the performance, deep learning is used to assist prediction. But
most of the deep learning methods only utilize temporal dependence or spatial
dependence of data in the forecasting process. To address these limitations, a
novel deep learning traffic demand forecasting framework which based on Deep
Spatio-Temporal ConvLSTM is proposed in this paper. In order to evaluate the
performance of the framework, an end-to-end deep learning system is designed
and a real dataset is used. Furthermore, the proposed method can capture
temporal dependence and spatial dependence simultaneously. The closeness,
period and trend components of spatio-temporal data are used in three predicted
branches. These branches have the same network structures, but do not share
weights. Then a linear fusion method is used to get the final result. Finally,
the experimental results on DIDI order dataset of Chengdu demonstrate that our
method outperforms traditional models with accuracy and speed.
