The use of graph-based asset information is growing in the Architecture, Engineering, Construction, and Operation (AECO) domain. However, there has been no comprehensive investigation into its internal structure, which hinders the efficiency of information querying and knowledge discovery. This study aims to reveal the internal structure of graph-based IFC (Industry Foundation Classes) information, referred to as IFC-Graph, and to assess the impact of graph structure on the efficiency of information querying and knowledge discovery. First, through a close examination of the IFC standard, five levels of detail were identified and proposed for IFC-Graph. Second, methods for generating graphs at each level were developed. Lastly, the impact of graph structure on information querying and knowledge discovery was assessed. The results show that: (1) relations constitute the main framework of graph-based IFC information and are key to efficiently extracting relational information from IFC-Graph; and (2) the proposed five-level framework makes graph-based asset information more practical to use, by increasing flexibility in graph generation and improving the efficiency of graph queries and knowledge discovery.

Frequent traffic congestion has a detrimental effect on our travel experience and the overall quality of urban life. Accurate prediction of traffic congestion plays a pivotal role in alleviating the congestion problem. However, existing traffic prediction approaches primarily focus on extracting its local changing patterns, overlooking the importance of incorporating global dynamic patterns. This presents three challenges: 1) Complicated spatial and temporal information exists in local (microscopic) traffic patterns; 2) The propagation and dissipation patterns of global (macroscopic) traffic congestion exhibit complex dynamics across time and space; 3) Modeling the interactions between macro and micro changing patterns of congestion remains unknown. In this paper, we present a novel framework for traffic congestion prediction that integrates microscopic and macroscopic cross-scale spatiotemporal modeling. Our approach utilizes contagion dynamics to characterize congestion propagation and recovery at the network-wide scale. Additionally, we employ a spatio-temporal graph neural network to capture local traffic patterns. A key contribution is the introduction of a differentiable micro-macro transformation mechanism, enabling the aggregation of microscopic states into macroscopic ones in a differentiable manner during model training. Further, we utilize the knowledge derived from macro contagion dynamics to constrain the micro traffic patterns by employing the physics-informed neural network. Experiments on three real-world datasets of traffic congestion demonstrate that our prediction model consistently outperforms the state-of-the-art baselines.

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively usek-means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap.

Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available athttps://github.com/jinguangyin/Auto-DSTSGN.

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim to discover the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: 1) traffic congestion is affected by several factors with complex spatio-temporal relations; 2) the amount of congestion data with known causes is small due to the limitation of human label; 3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: 1) congestion feature extraction, which extracts the important features influencing congestion; and 2) congestion cause discovery, which utilize a deep semi-supervised learning based method to discover the causes of traffic congestion with limited labeled causes. Specifically, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, the k-means algorithm is performed to produce the clusters. Extensive experiments show that the performance of our proposed method is superior to the baselines. Additionally, our system is deployed and used in the practical production environment at Amap.

Realistic simulation of a massive amount of human mobility data is of great use in epidemic spreading modeling and related health policy-making. Existing solutions for mobility simulation can be classified into two categories: model-based methods and model-free methods, which are both limited in generating high-quality mobility data due to the complicated transitions and complex regularities in human mobility. To solve this problem, we propose a model-free generative adversarial framework, which effectively integrates the domain knowledge of human mobility regularity utilized in the model-based methods. In the proposed framework, we design a novel self-attention based sequential modeling network as the generator to capture the complicated temporal transitions in human mobility. To augment the learning power of the generator with the advantages of model-based methods, we design an attention-based region network to introduce the prior knowledge of urban structure to generate a meaningful trajectory. As for the discriminator, we design a mobility regularity-aware loss to distinguish the generated trajectory. Finally, we utilize the mobility regularities of spatial continuity and temporal periodicity to pre-train the generator and discriminator to further accelerate the learning procedure. Extensive experiments on two real-life mobility datasets demonstrate that our framework outperforms seven state-of-the-art baselines significantly in terms of improving the quality of simulated mobility data by 35%. Furthermore, in the simulated spreading of COVID-19, synthetic data from our framework reduces MAPE from 5% ~ 10% (baseline performance) to 2%.