Enabling smart curb management with spatiotemporal deep learning
Enabling smart curb management with spatiotemporal deep learning
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DOI：
10.1016/j.compenvurbsys.2022.101914
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Haiyan Hao;Yan Wang;Lili Du;S. Chen
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文献类型：
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作者：
Haiyan Hao;Yan Wang;Lili Du;S. Chen
路边空间是城市的重要资产。它们经常被旅行者用来转换交通工具，游客用来访问路边物业，市政当局用来放置路边基础设施。多式联运的推广和新型交通服务的出现使道路环境及其管理复杂化。因此，一些城市已经开始探索遏制管理的新策略，但缺乏对不同遏制法规和建筑环境特征如何共同影响用户群体的遏制使用模式的预期。我们向智能路缘环境迈出了一步，提出了一种基于图的深度学习方法，即MultiGCN-LSTM，以预测不同时间和空间的路缘使用。我们使用两个图卷积层和一个LSTM层来捕获路缘规则、建筑环境语义和不同路缘用途之间的空间、时间和语义依赖关系。针对美国一个中等规模的大学城和一个大都市分别开发了两个特定地点的模型，并通过消融研究验证了所提出模型的有效性，并在三个场景实验中得到了证明。随着新的交通服务和新兴的车辆技术的发展，面对更加多样化和集约化的路缘使用，该研究有助于智能路缘管理。
Curb spaces are important assets to cities. They are often used by travelers to switch transportation means, visitors to access curbside properties, and municipalities to place roadside infrastructure. The promotion of multi-modal transportation and the emergence of new mobility services have complicated both curb environments and their management. Consequently, some cities have started to explore new strategies for curb management, but lacked the anticipation on how different curb regulations and built-environment features may collectively influence curb-use patterns across user groups. We make a step toward smart curb environment by proposing a graph-based deep learning approach, i.e., MultiGCN-LSTM, to predict diverse curb uses across time and space. We used two graph convolution layers and an LSTM layer to capture the spatial, temporal, and semantic dependencies between curb regulations, built-environment semantics, and diverse curb uses. Two place-specific models were developed separately for a medium-sized college town and a metropolitan in the U.S. The effectiveness of the proposed models was validated with ablation studies and demonstrated in three scenario experiments. The research contributes to smart curb management in the face of more diversified and intensified curb uses with new mobility services and emerging vehicular technologies.