Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using Graph Convolutional Neural Network
Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using Graph Convolutional Neural Network
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DOI：
10.1007/s42421-023-00073-y
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发表时间：
2022-02
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通讯作者：
Rezaur Rahman;Samiul Hasan
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作者：
Rezaur Rahman;Samiul Hasan
考虑到网络中有许多起点到目的地（OD）出行需求和链路流量的实例，我们提出了一种新型的数据驱动方法来学习交通网络的交通流模式。代替假设某些用户行为（例如，用户平衡或系统最优），在这里我们探索直接从数据中学习这些流模式的想法。为了实现这一思想，我们将传统的交通分配问题（来自交通科学领域）描述为一个数据驱动的学习问题，并开发了一个基于神经网络的框架，称为图卷积神经网络（GCNN）来解决它。该框架以一种有效的方式表示交通网络和OD需求，并利用多个OD需求从节点到路段的扩散过程。我们验证了该模型的解决方案对运行静态用户平衡为基础的流量分配在苏福尔斯和东马萨诸塞州网络的分析解决方案。验证结果表明，实现的GCNN模型可以很好地学习流模式，在不同的拥塞条件下，两个网络的实际和估计链路流量之间的平均绝对差小于2%。当模型训练完成后，它可以立即确定大规模网络的流量。因此，这种方法可以克服在大规模网络上部署交通分配模型的挑战，并在数据驱动的网络建模中开辟新的研究方向。
We present a novel data-driven approach of learning traffic flow patterns of a transportation network given that many instances of origin to destination (OD) travel demand and link flows of the network are available. Instead of estimating traffic flow patterns assuming certain user behavior (e.g., user equilibrium or system optimal), here we explore the idea of learning those flow patterns directly from the data. To implement this idea, we have formulated the traditional traffic assignment problem (from the field of transportation science) as a data-driven learning problem and developed a neural network-based framework known as Graph Convolutional Neural Network (GCNN) to solve it. The proposed framework represents the transportation network and OD demand in an efficient way and utilizes the diffusion process of multiple OD demands from nodes to links. We validate the solutions of the model against analytical solutions generated from running static user equilibrium-based traffic assignments over Sioux Falls and East Massachusetts networks. The validation results show that the implemented GCNN model can learn the flow patterns very well with less than 2% mean absolute difference between the actual and estimated link flows for both networks under varying congested conditions. When the training of the model is complete, it can instantly determine the traffic flows of a large-scale network. Hence, this approach can overcome the challenges of deploying traffic assignment models over large-scale networks and open new directions of research in data-driven network modeling.