Visual Exploration of Cycling Semantics with GPS Trajectory Data
Abstract
:1. Introduction
- We propose a new visualization system called VizCycSemantics for the exploration of underlying urban cycling semantics from both the cyclist and road segment’s perspectives, based on large-scale cycling GPS trajectories and road network data. The system could help to improve cycling services in cities.
- We textually convert the cycling trajectories into street name and moving feature corpora, then use a topic model to automatically extract the cyclists’ behavior semantics (i.e., cycling topics of cyclists) and moving semantics on roads (i.e., moving topics on roads), respectively. We further employ a clustering algorithm to capture the groups of similar cyclists and road segments in the city.
- We implement multiple interfaces to facilitate the understanding of cycling semantics for pervasive computing, including a cycling map, cycling groups and topics, the size of cycling groups, the street cloud of cycling topics, the temporal evolution of cycling topics, moving topics and moving topic distribution. A group of case studies in Beijing demonstrates the effectiveness of our system and also obtains various insightful findings and cycling advice.
2. Related Work
2.1. Visualization for Raw and Processed Trajectory Data
2.2. Visualization for Hidden Knowledge of Trajectory Data
3. System Tasks and Framework of VizCycSemantics
- Task 1: Acquire cycling themes of cyclists in the city, i.e., behavior semantics. Based on the cycling trajectories and road network data, find out how many cycling themes of cyclists are in the city, which ones are more popular, how many groups of cyclists there are and how they distribute in time and space. This task could help cyclists to choose the desirable cycling routes and times according to their preferences.
- Task 2: Identify different moving topics of cycling on the road network, i.e., moving semantics on roads. By investigating the fine-grained moving features on road segments, find out the moving topics of road segments in the city and determine where the road network is good for smooth or challenging cycling, where people need to ride with care and so on. This task can enable cyclists to choose appropriate road segments for their physical needs and can also help city planners to properly deploy cycling facilities.
- Task 3: Facilitate the understanding of the cycling semantics for analysts. Through implementing an interactive visualization system, the cycling semantics derived from task 1 and task 2 could be presented more intuitively. The user interfaces should satisfy the criterion of a user-friendly interaction.
4. Backend Algorithms of VizCycSemantics
4.1. Preprocessing
4.2. Textualization
4.2.1. Trajectory–Streets Textualization
4.2.2. Moving Feature Textualization
4.3. Topic Extraction via LDA
4.3.1. Keyword–Topic Distribution
4.3.2. Topic–Document Distribution
4.4. Topic-Based Clustering
5. Visualization Implementation
5.1. Study Area and Data Source
5.2. Visual Design
5.2.1. Cycling Grouping and Street Cloud of Cycling Topics
5.2.2. Temporal Evolution of Cycling Topics
5.2.3. Moving Semantics Profiling
5.2.4. Cycling Map
6. Case Study
6.1. Case 1: Exploring the Spatiotemporal Patterns of Cycling Themes for Cyclists
6.1.1. Recreational Cycling
6.1.2. Connected Cycling
6.1.3. Daily Commuting Cycling
6.1.4. Exercising Cycling
6.1.5. Temporal Evolution of Cycling Topics
6.2. Case 2: Exploring the Moving Semantics of Road Segments
6.2.1. Topics Extracted by Velocity
6.2.2. Topics Extracted by Acceleration
6.2.3. Topics Extracted by Turning Angle
6.2.4. Regional Comparison
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Gao, X.; Liao, C.; Chen, C.; Li, R. Visual Exploration of Cycling Semantics with GPS Trajectory Data. Appl. Sci. 2023, 13, 2748. https://doi.org/10.3390/app13042748
Gao X, Liao C, Chen C, Li R. Visual Exploration of Cycling Semantics with GPS Trajectory Data. Applied Sciences. 2023; 13(4):2748. https://doi.org/10.3390/app13042748
Chicago/Turabian StyleGao, Xuansu, Chengwu Liao, Chao Chen, and Ruiyuan Li. 2023. "Visual Exploration of Cycling Semantics with GPS Trajectory Data" Applied Sciences 13, no. 4: 2748. https://doi.org/10.3390/app13042748
APA StyleGao, X., Liao, C., Chen, C., & Li, R. (2023). Visual Exploration of Cycling Semantics with GPS Trajectory Data. Applied Sciences, 13(4), 2748. https://doi.org/10.3390/app13042748