Authors | Approaches | Dataset | Helpful Aspect | Drawback |
Tobler, 1987 | Flow mapping through arrows. | Migration OD data | First introduced the use of arrows. | Overlapped arrows confuse visualization. |
Andreinko & Andreinko, 2008 | OD matrix | GPS data | Effective aggregation of movement data. | Region to region spatial information is missing. |
Yue et al., 2009 | A time-dependent flow interaction matrix | Taxi Trajectory data | Understanding pattern regarding the level of attractiveness. | Pick Up and Drop off point is taken as O & D. So, which segment is being used is not clear. |
Wood et al., 2010 | Mapping OD vector using a regular grid. | Migration Flow | Representation of region-to-region spatial information | Fails expressing OD flow change. |
Wang et al., 2015 | Using Eigen lines as discrete OD links | Mobile Phone Record | High penetration rate, wide service area. | Eigen lines are not capable of representing all streets with traffic but only those having obvious traffic. |
Wang et al., 2019 | Expressing OD characteristic through chord diagram. | Taxi GPS Data | High automation, Grasp real-time traffic situation. | Pick Up and Drop off point is taken as O & D. So, which segment is being used is not clear. |
Liao et al., 2019 | Human check-in & trajectory data fusion. | Taxi Trajectory Data | Allows understanding time-evolving trip purpose pattern. | Little Visualization capability, only covers weekly data. |
Rizwan et al., 2020 | Spatial distribution of human check-in data | LBSN data | Gives insights on human activities over space-time. | Not comprehensive. Only include recorded check-ins which may differ from actual urban flow. |
Liu et al., 2022 | Flow clustering method. | Taxi Trajectory Data | High-level activity dynamics and travel behavior are expressed on geographic context. | Pick Up and Drop off point is taken as O & D. So, which segment is being used is not clear. |