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Meng Spatiotemporal Visual Analysis of Traffic Flow Patterns Related to Transport Hubs from Floating Car Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shi-Rui |
| Copyright Year | 2016 |
| Abstract | Transport hubs such as airports and railway stations are places where plenty of passengers are exchanged between vehicles or between transport modes. Analyzing the patterns of traffic flows in/out of the transport hubs may help traffic engineers to better understand passenger behaviors and to improve the transportation planning. However, dealing with the movement data, is very challenging due to their large data volume, implicit spatiotemporal relationship, and uncertain semantics. The goal of this thesis is for visual analysis of the traffic flow patterns related to transport hubs using floating car data. We propose a visual analysis workflow incorporating computational algorithms, data mining approaches, and visualization techniques for exploration of the spatiotemporal patterns of taxi flows. More specifically, we preprocess a large amount of movement data, reconstruct trajectories and extract the starting and ending points especially related to the transport hubs. Secondly, we identify appropriate spatial and semantic clustering methods to derive and categorize transport hubs related significant places. Finally, we design appropriate spatial and temporal visualization techniques, e.g. dot maps, proportional symbol maps, pie chart maps, to visually analyze those significant places. We use one-week Floating Car Data (FCD) in Shanghai as our test dataset and select Hongqiao international airport as the test transport hub. By applying our framework to the test dataset, the experiment results reveal significant spatiotemporal traffic flow patterns related to Hongqiao airport, which demonstrates the feasibility of the proposed workflow. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://cartographymaster.eu/wp-content/theses/2016_Wang_Thesis.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
