Public transport routing plays a crucial role in transit network design,
ensuring a satisfactory level of service for passengers. However, current
routing solutions rely on traditional operational research heuristics, which
can be time-consuming to implement and lack the ability to provide quick
solutions. Here, we propose a novel deep learning-based methodology for a
decision support system that enables public transport (PT) planners to identify
short-term route improvements rapidly. By seamlessly adjusting specific
sections of routes between two stops during specific times of the day, our
method effectively reduces times and enhances PT services. Leveraging diverse
data sources such as GTFS and smart card data, we extract features and model
the transportation network as a directed graph. Using self-supervision, we
train a deep learning model for predicting lateness values for road segments.
These lateness values are then utilized as edge weights in the transportation
graph, enabling efficient path searching. Through evaluating the method on Tel
Aviv, we are able to reduce times on more than 9\% of the routes. The improved
routes included both intraurban and suburban routes showcasing a fact
highlighting the model's versatility. The findings emphasize the potential of
our data-driven decision support system to enhance public transport and city
logistics, promoting greater efficiency and reliability in PT services.

利用深度学习方法构建决策支持系统，通过调整公共交通特定时间段的路线以提高运输效率和可靠性。

基于深度学习加权图的短途公交路线规划决策支持系统

Short Run Transit Route Planning Decision Support System Using a Deep  Learning-Based Weighted Graph

Logistics optimization nowadays is becoming one of the hottest areas in the
AI community. In the past year, significant advancements in the domain were
achieved by representing the problem in a form of graph. Another promising area
of research was to apply reinforcement learning algorithms to the above task.
In our work, we made advantage of using both approaches and apply reinforcement
learning on a graph. To do that, we have analyzed the most recent results in
both fields and selected SOTA algorithms both from graph neural networks and
reinforcement learning. Then, we combined selected models on the problem of
AMOD systems optimization for the transportation network of New York city. Our
team compared three algorithms - GAT, Pro-CNN and PTDNet - to bring to the fore
the important nodes on a graph representation. Finally, we achieved SOTA
results on AMOD systems optimization problem employing PTDNet with GNN and
training them in reinforcement fashion.
Keywords: Graph Neural Network (GNN), Logistics optimization, Reinforcement
Learning

我们结合了图神经网络和强化学习，在纽约市的广义交通网络中，运用强化学习方法，分别比较了 GAT，Pro-CNN 和 PTDNet 三种算法。最终我们利用 PTDNet 和 GNN 进行强化训练，在 AMOD 系统优化问题上取得了 SOTA 结果。

基于图的鲁棒强化学习在物流优化中的应用

Robust Reinforcement Learning on Graphs for Logistics optimization

Predicting large-scale transportation network traffic has become an important
and challenging topic in recent decades. Inspired by the domain knowledge of
motion prediction, in which the future motion of an object can be predicted
based on previous scenes, we propose a network grid representation method that
can retain the fine-scale structure of a transportation network. Network-wide
traffic speeds are converted into a series of static images and input into a
novel deep architecture, namely, spatiotemporal recurrent convolutional
networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the
advantages of deep convolutional neural networks (DCNNs) and long short-term
memory (LSTM) neural networks. The spatial dependencies of network-wide traffic
can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An
experiment on a Beijing transportation network with 278 links demonstrates that
SRCNs outperform other deep learning-based algorithms in both short-term and
long-term traffic prediction.

提出了一种基于运动预测领域知识的网络网格表示方法，将网络整体交通速度转换为静态图像，并输入一种新的深度体系结构 —— 时空循环卷积网络 (SRCNs)，可以用于交通预测，实验证明其在短期和长期交通预测方面优于其他基于深度学习的算法。