Big Trajectory Data Mining: A Survey of Methods, Applications, and Services
Abstract
:1. Introduction
2. Research Questions
3. Methodology
4. Trajectory Data
4.1. Explicit Trajectory Data
4.2. Implicit Trajectory Data
4.2.1. Sensor-Based Trajectory Data
4.2.2. Signal-Based Trajectory Data
4.2.3. Web-Based Trajectory Data
4.3. Supplementary Data
5. Trajectory Data Mining Methods
5.1. First-Tier Trajectory Data Mining Methods
5.1.1. Clusterings
5.1.2. Classification
5.2. Second-Tier Trajectory Data Mining Methods
5.2.1. Pattern Mining
5.2.2. Outlier Identification
5.2.3. Prediction
5.3. Relationships between Trajectory Data Mining Methods
6. Application Issues with Trajectory Data Mining
6.1. Social Dynamics Issues
6.1.1. Discovery of Social Relationships
6.1.2. Detection of Social Events
6.1.3. Characterization of Connections between Places
6.2. Traffic Dynamics Issues
6.2.1. Profiling of Moving Objects
6.2.2. Trajectory-Based Prediction
6.3. Operational Dynamics Issues
6.3.1. Interest Recommendation
6.3.2. Trip Recommendation
7. Trajectory Data-Based Services
7.1. Transportation and Urban Planning
7.2. Environment and Energy
7.3. Social and Commercial Services and Public Administration
8. Practical Implications
8.1. Practical Tools in Trajectory Data Mining
8.2. Privacy Protection in Trajectory Data Mining
8.3. Future Prospects for Trajectory Data Mining
8.4. Trajectory Data Mining in Industry 4.0
9. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Categories | First-Tier Mining Methods | ||
---|---|---|---|
Categories | Methods | Clustering | Classification |
Second-tier Mining Methods | Pattern Mining | Grouping spatially close trajectories [86,87]; Grouping temporally related trajectories for periodic pattern mining [69,71,73]; Extracting places of significance for frequent pattern mining [79,80]; Detecting similar mobility interests for collective pattern mining [83,86,87,88,89]; Aggregating close locations for sequence analysis [114]; | No classification-related tasks have been identified for pattern mining. |
Outlier Identification | Grouping trajectories or sub-trajectories with homogeneity [91,94]; | Sorting out trajectories based on pre-identified features [95,96]; | |
Prediction | Grouping multiple users with similar mobility intentions [102,115]; Grouping similar trips of one specific object [116]; Mining trajectory patterns for location prediction [100,101,106,107]; | Matching one object’s current movement with its movement patterns for location prediction [107,116]; Matching one object’s ongoing trajectory with its previous trajectories for route prediction [110]; |
Application Categories | Application Issues | Description of Issues | Major Tasks Involved | Mining Methods Involved |
---|---|---|---|---|
Social Dynamics | Discovery of Social Relationships | Discovery of social ties between individuals and communities | Grouping individuals’ stay locations | Clustering [117] |
Extracting chronologically ordered sequences of stay locations | Frequent pattern mining [117] | |||
Discovery of interaction between animals | Detecting groups of moving animals, describing groups’ features | Collective pattern mining [88] | ||
Detection of Social Events | Detection of event occurrence | Grouping based on spatiotemporal properties | Clustering [118] | |
Profiling of discovered events | Extracting features and categorizing events | Classification [119] | ||
Characterization of Connection between Places | Detection of hotspots | Grouping according to spatiotemporal properties | Clustering [120,121] | |
Description of land uses and regional functions | Discovering regions with similar functions | Clustering [120,121] | ||
Extracting features and categorizing regions | Classification [120,121] | |||
Description of connection between places | Extracting origin/destination links | Clustering [122,123] | ||
Discriminating abnormal links | Outlier identification [124] | |||
Traffic Dynamics | Profiling of Moving Objects | Inferring mobility activities and modes | Extracting features and categorizing activities | Classification [61,125,126] |
Profiling movement patterns | Extracting sequences of visited places | Frequent pattern mining [127,128]; Clustering [129] | ||
Trajectory-based Prediction | Predicting an object’s future location/route | Establishing probabilistic model for prediction | Statistical methods (e.g., Markov Chain) [104,105] | |
Comparing current trajectory with extracted historical trajectories | Frequent pattern mining [106,107] | |||
Predicting traffic jams | Inferring traffic density and comparing it with road capacity | Frequent pattern mining [130]; Statistical methods [131] | ||
Operational Dynamics | Interest Recommendation | Friend–place recommendation | Extracting shared movement patterns and ranking similarities | Frequent pattern mining [132,133,134] |
Trip Recommendation | Suggesting order of visiting locations | Predicting routes based on user preferences | Frequent pattern mining [135,136] |
Services | Service Contents | Application Issues Involved | ||
---|---|---|---|---|
Social Dynamics | Traffic Dynamics | Operational Dynamics | ||
Transportation | Improving driving experience | Trip recommendation [153,154,155] | ||
Augmenting public transit services | Characterization of connections between places [96,156] | Trajectory-based prediction [157,158,159] | ||
Enhancing transportation planning and management | Characterization of connections between places [14,160] | Trajectory-based prediction [161] | ||
Urban Planning | Understanding urban land use and urban evolution | Characterization of connections between places [120,162] | ||
Facilitating urban infrastructure planning | Characterization of connections between places [163,164] | |||
Evaluating transportation system | Characterization of connections between places [165,166] | |||
Environment | Assessing air pollution | Characterization of connections between places [167] | ||
Assessing noise pollution | Characterization of connections between places [168,169] | |||
Energy | Inferring energy consumption | Characterization of connections between places [170] | ||
Eco-car infrastructure planning | Characterization of connections between places [171] | Profiling of moving objects [171] | ||
Social Services | Supporting friend-searching | Discovery of social relationships [133,142] | Profiling of moving objects [133,142] | Interest recommendation [134,142] |
Suggesting routes and places | Profiling of moving objects [172,173,174] | Trip recommendation [172,173,174] | ||
Understanding communities | Discovery of social relationships [175] | Profiling of moving objects [175,176] | ||
Commercial Services | Optimizing commercial localization | Characterization of connections between places [177] | Profiling of moving objects [177] | Trip recommendation [177] |
Guiding advertising allocation | Characterization of connections between places [178] | Profiling of moving objects [178] | Trip recommendation [178] | |
Optimizing department layout | Characterization of connections between places [179] | Profiling of moving objects [179] | ||
Public Administration | Detecting abnormal behavior | Profiling of moving objects [180] | ||
Monitoring public gathering | Detection of social events [181] | Profiling of moving objects [181] | ||
Predicting natural disasters | Trajectory-based prediction [182] |
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Wang, D.; Miwa, T.; Morikawa, T. Big Trajectory Data Mining: A Survey of Methods, Applications, and Services. Sensors 2020, 20, 4571. https://doi.org/10.3390/s20164571
Wang D, Miwa T, Morikawa T. Big Trajectory Data Mining: A Survey of Methods, Applications, and Services. Sensors. 2020; 20(16):4571. https://doi.org/10.3390/s20164571
Chicago/Turabian StyleWang, Di, Tomio Miwa, and Takayuki Morikawa. 2020. "Big Trajectory Data Mining: A Survey of Methods, Applications, and Services" Sensors 20, no. 16: 4571. https://doi.org/10.3390/s20164571
APA StyleWang, D., Miwa, T., & Morikawa, T. (2020). Big Trajectory Data Mining: A Survey of Methods, Applications, and Services. Sensors, 20(16), 4571. https://doi.org/10.3390/s20164571