As a decisive part in the success of Mobility-as-a-Service (MaaS),
spatio-temporal predictive modeling for crowd movements is a challenging task
particularly considering scenarios where societal events drive mobility
behavior deviated from the normality. While tremendous progress has been made
to model high-level spatio-temporal regularities with deep learning, most, if
not all of the existing methods are neither aware of the dynamic interactions
among multiple transport modes nor adaptive to unprecedented volatility brought
by potential societal events. In this paper, we are therefore motivated to
improve the canonical spatio-temporal network (ST-Net) from two perspectives:
(1) design a heterogeneous mobility information network (HMIN) to explicitly
represent intermodality in multimodal mobility; (2) propose a memory-augmented
dynamic filter generator (MDFG) to generate sequence-specific parameters in an
on-the-fly fashion for various scenarios. The enhanced event-aware
spatio-temporal network, namely EAST-Net, is evaluated on several real-world
datasets with a wide variety and coverage of societal events. Both quantitative
and qualitative experimental results verify the superiority of our approach
compared with the state-of-the-art baselines. Code and data are published on
this https URL

该论文提出了一种增强的事件感知时空网络 (EAST-Net)，其中包括设计一个异构移动信息网络 (HMIN) 和提出一个增强型记忆动态过滤器生成器 (MDFG)，以更好地建模 Mobility-as-a-Service 中的人群移动，特别是在社会事件驱动移动行为偏离正常情况的情况下。

事件感知多模式出行即时预测

Event-Aware Multimodal Mobility Nowcasting

The future of mobility-as-a-Service (Maas)should embrace an integrated system
of ride-hailing, street-hailing and ride-sharing with optimised intelligent
vehicle routing in response to a real-time, stochastic demand pattern. We aim
to optimise routing policies for a large fleet of vehicles for street-hailing
services, given a stochastic demand pattern in small to medium-sized road
networks. A model-based dispatch algorithm, a high performance model-free
reinforcement learning based algorithm and a novel hybrid algorithm combining
the benefits of both the top-down approach and the model-free reinforcement
learning have been proposed to route the \emph{vacant} vehicles. We design our
reinforcement learning based routing algorithm using proximal policy
optimisation and combined intrinsic and extrinsic rewards to strike a balance
between exploration and exploitation. Using a large-scale agent-based
microscopic simulation platform to evaluate our proposed algorithms, our
model-free reinforcement learning and hybrid algorithm show excellent
performance on both artificial road network and community-based Singapore road
network with empirical demands, and our hybrid algorithm can significantly
accelerate the model-free learner in the process of learning.

本论文提出了一种基于模型的派遣算法、基于高性能模型无关的强化学习算法和将自顶向下方法和模型无关强化学习的优点结合的新型混合算法，以路线优化策略的形式为车队提供路线规划，研究了面对实时、随机需求的情况下，在小到中型的道路网络中，对街道叫车服务的路线进行优化；使用基于代理的大规模微观仿真平台，评估了提出的算法在人工道路网络和社区基础新加坡道路网络中的表现，结果显示出基于模型的派遣算法、基于高性能模型无关的强化学习算法和混合算法在性能上均表现出色，并且混合算法可以显著加速模型无关学习者的学习过程。