Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory

Zhuang, Chenyi; Yuan, Nicholas Jing; Song, Ruihua; Xie, Xing; Ma, Qiang

doi:10.24963/ijcai.2017/506

Cited by 29 publications

(15 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Knowledge Graph can be divided into two categories, including the general domain and vertical domain, which has many applications in different fields, such as Facebook [17], Baidu [18], Tecent [19], CN-DBpedia [20]. The main domains include: biology [21,22], financial [23,24], social meida [25,26], academic paper [27], tourism [28,29], life science [30],while there isn't any single application found in Heating industry. In this paper, a method of knowledge graph applied is proposed in the heating field and has great help to field operators.…”

Section: Aiim-2021mentioning

confidence: 99%

Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Xie

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

At present, heating companies mostly use a set-point temperature control curve. The operator adjusts the temperature control curve according to watching the outdoor weather conditions, which usually results in economic conditions. Given the above condition, we analyze the heating system’s mechanism first, then compile and analyze much historical data. According to the analysis results, we establish the heat exchange station-user room temperature joint model based on the machine learning method. Using the above model, the system is deeply explored, and the optimized secondary heating curve is designed and simulated. The results show that the user’s room temperature stability has an obvious control effect, which is of great significance to meet the room temperature compliance rate. At the same time, the knowledge graph of the heating field and some applications based on the knowledge graph are also built to provide more convenient and friendly operating advice for operators.

show abstract

Section: Aiim-2021mentioning

confidence: 99%

Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Xie

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In order to process timestamps with different resolutions in different datasets in a unified manner, we discretize timestamps into fixed-length time bins. Following the widely used preprocessing strategy of timestamps in existing approaches [40,47], we divide one day into 48 half-hour time bins, and we further distinguish time bins belonging to the working day and non-working day. Thus, there are totally 96 time bins.…”

Section: (M Rmentioning

confidence: 99%

Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

Wang¹,

Yu²,

Liu³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge" extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.

show abstract

“…Besides road networks as the main geographic source for the map matching task, other geographic sources can either be intrinsically inferred from the spatio-temporal patterns of the waypoints per se, or imported from external data sources for other general semantic enrichment tasks. For the first method, the patterns may be dense waypoint clusters that can be further inferred as home, workplace, or other places (Andrienko et al 2007, Zhuang et al 2017. Further constraints can be added to the waypoint clusters, such as sharing similar movement properties (Vrotsou et al 2015).…”

Section: Semantic Enrichment Of Trajectoriesmentioning

confidence: 99%

Adaptive simplification of GPS trajectories with geographic context – a quadtree-based approach

Huang

Weibel

2020

International Journal of Geographical Information Science

View full text Add to dashboard Cite

Big GPS trajectory datasets can have redundant spatio-temporal information for applications, which requires simplification as a key preprocessing for modeling. Many existing simplification methods focus on the geometric information from a trajectory per se. Conversely, methods considering geographic context often fail to provide spatially adaptive simplification, or require complex parameter settings to achieve this task. This study proposes a novel two-stage adaptive trajectory simplification method embedding spatial indexing, enrichment, and aggregation in an integrated process. The first stage employs a quadtree for the subdivision depending on the density of geographic context features (i.e. POIs), leading to a variable-resolution representation of the area. The second stage aggregates trajectory waypoints locating in the same quadtree leaf node into a representative point, making the aggregation adapting to the spatial layout of the geographic feature in the first stage. Evaluation with a real-world vehicle trajectory dataset shows that the proposed approach can automatically simplify trajectory segments at variable compression ratios with greater simplification in areas with sparse context features (e.g. rural) and less simplification in areas with dense context features (e.g. urban). More importantly, the method can still preserve inter-trajectory distances between original trajectories and simplified ones, while significantly reducing the computing time.

show abstract

Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory

Cited by 29 publications

References 20 publications

Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

Adaptive simplification of GPS trajectories with geographic context – a quadtree-based approach

Contact Info

Product

Resources

About