论文概览 |《IJGIS》2024.10 Vol.38 issue10

本次给大家整理的是《International Journal of Geographical Information Science》杂志2024年第38卷第10期的论文的题目和摘要，一共包括9篇SCI论文！

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论文1

Next track point prediction using a flexible strategy of subgraph learning on road networks

在道路网络上使用灵活的子图学习策略进行下一个轨迹点预测

【摘要】

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

【摘要翻译】

准确预测车辆行驶的下一个轨迹点对于各种智能交通系统（ITS）应用至关重要，例如出行行为研究、交通控制和交通拥堵监测。近年来，轨迹预测的研究遵循一种范式，即首先表示原始轨迹，然后基于该表示进行预测。目前，轨迹表示方法通常通过地图匹配将轨迹点投影到道路网络，并基于匹配道路的表示来表示轨迹。然而，在智能交通系统中，准确地将轨迹与道路匹配是一项挑战，因为匹配精度受到轨迹质量的重大影响。同时，由于很难判断轨迹匹配结果是否准确或存在混淆，因此如何有效利用这种不确定的地理上下文信息也是一个挑战，这在地理信息科学中被定义为不确定地理上下文问题（UGCoP）。因此，我们提出了一种灵活的子图学习策略，称为SLM，用于预测车辆的下一个轨迹点。具体来说，首先提出一个子图生成模块，以提取历史轨迹点周围道路的拓扑上下文信息。其次，设计了一个子图学习模块，从生成的子图中学习丰富的时空特征。最后，将提取的时空特征输入到预测模块，以预测道路网络上车辆的下一个轨迹点。我们的模型能够有效利用道路网络上轨迹的不确定地理上下文信息，同时避免地图匹配带来的误差。基于两个不同城市的轨迹数据集的广泛实验验证了我们方法的有效性。

【doi】

https://doi.org/10.1080/13658816.2024.2358527

【作者信息】

Yifan Zhang，中国地质大学（武汉）地理与信息工程学院，武汉，中国

Wenhao Yu，中国地质大学（武汉）地理与信息工程学院，武汉，中国；国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国

Di Zhu，美国明尼苏达大学双城校区地理、环境与社会系，明尼苏达州，美国

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论文2

Next location prediction using heterogeneous graph-based fusion network with physical and social awareness

使用具有物理和社会意识的异构图融合网络进行下一个位置预测

【摘要】

Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets.