TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks
TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks
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DOI：
10.1109/icdm.2019.00193
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Yingxue Zhang;Yanhua Li;Xun Zhou;Xiangnan Kong;Jun Luo
中科院分区：
文献类型：
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作者：
Yingxue Zhang;Yanhua Li;Xun Zhou;Xiangnan Kong;Jun Luo
城市化的快速发展加快了城市规划的进程，例如新的住宅、商业区，这反过来又促进了当地的出行需求。我们提出了一种新的非部署交通估计问题，即在部署施工计划之前预测一个区域的交通状况变化。这一问题对于城市规划者评价和制定城市布局规划具有重要意义。然而，这项任务具有挑战性。传统的流量估计方法缺乏解决这一问题的能力，因为在部署之前无法收集有关影响的数据，并且旧数据无法捕获流量模式的变化。本文将非部署交通估计问题定义为一个交通生成问题，并提出了一种新的深度生成模型TrafficGAN，该模型能够根据出行需求变化和底层路网结构，捕捉交通状况跨空间区域演化的共享模式。特别是，TrafficGAN通过动态卷积层中的动态过滤器捕获道路网络结构。我们使用从深圳收集的大规模交通数据对我们的TrafficGan进行评估，中国。结果表明，与所有基线相比，TrafficGAN能够更准确地估计交通状况。
The rapid progress of urbanization has expedited the process of urban planning, e.g., new residential, commercial areas, which in turn boosts the local travel demand. We propose a novel "off-deployment traffic estimation problem", namely, to foresee the traffic condition changes of a region prior to the deployment of a construction plan. This problem is important to city planners to evaluate and develop urban deployment plans. However, this task is challenging. Traditional traffic estimation approaches lack the ability to solve this problem, since no data about the impact can be collected before the deployment and old data fails to capture the traffic pattern changes. In this paper, we define the off-deployment traffic estimation problem as a traffic generation problem, and develop a novel deep generative model TrafficGAN that captures the shared patterns across spatial regions of how traffic conditions evolve according to travel demand changes and underlying road network structures. In particular, TrafficGAN captures the road network structures through a dynamic filter in the dynamic convolutional layer. We evaluate our TrafficGAN using a large-scale traffic data collected from Shenzhen, China. Results show that TrafficGAN can more accurately estimate the traffic conditions compared with all baselines.