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Understanding Spatiotemporal Mobility Patterns related to Transport Hubs from Floating Car Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ding, Linfang Jahnke, Mathias Wang, Shirui Karja, Katre |
| 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 their mobility patterns, for instance traffic flows in/out of the transport hubs, helps better understanding passengers travel behaviors and improving the transportation planning. In this paper, we aim to visually present the spatiotemporal mobility patterns related to transport hubs using floating car data. Following a visual analysis workflow, which consists of computational algorithms, data mining approaches, and visualization techniques, we preprocess the raw data related to the transport hubs, derive relevant pick-up and drop-off events, cluster and aggregate those events, and visually analyze their spatiotemporal distributions using mainly proportional symbol mapping and pie-chart mapping techniques. We use one-week Floating Car Data (FCD) in Shanghai as our test dataset and select Hongqiao international airport as the test transport hub. The preliminary experiment results show that there are obvious temporal mobility patterns as well as significant hotspot places related to Hongqiao airport. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://lbs2016.lbsconference.org/wp-content/uploads/2016/11/3_9.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
