Ride-hailing platforms generally provide various service options to
customers, such as solo ride services, shared ride services, etc. It is
generally expected that demands for different service modes are correlated, and
the prediction of demand for one service mode can benefit from historical
observations of demands for other service modes. Moreover, an accurate joint
prediction of demands for multiple service modes can help the platforms better
allocate and dispatch vehicle resources. Although there is a large stream of
literature on ride-hailing demand predictions for one specific service mode,
little efforts have been paid towards joint predictions of ride-hailing demands
for multiple service modes. To address this issue, we propose a deep multi-task
multi-graph learning approach, which combines two components: (1) multiple
multi-graph convolutional (MGC) networks for predicting demands for different
service modes, and (2) multi-task learning modules that enable knowledge
sharing across multiple MGC networks. More specifically, two multi-task
learning structures are established. The first one is the regularized
cross-task learning, which builds cross-task connections among the inputs and
outputs of multiple MGC networks. The second one is the multi-linear
relationship learning, which imposes a prior tensor normal distribution on the
weights of various MGC networks. Although there are no concrete bridges between
different MGC networks, the weights of these networks are constrained by each
other and subject to a common prior distribution. Evaluated with the
for-hire-vehicle datasets in Manhattan, we show that our propose approach
outperforms the benchmark algorithms in prediction accuracy for different
ride-hailing modes.

本研究提出了一种深度多任务多图卷积学习方法，通过多个多图卷积神经网络对不同服务模式的需求进行预测，并建立多任务学习模块来实现跨网络知识共享，从而更好地实现车辆资源的分配和调度。在曼哈顿的出租车数据集上进行评估，结果表明我们的方法在不同出行方式的需求预测准确性方面优于基准算法。