Constructing popular routes from uncertain trajectories

By Ling-Yin Wei, Yu Zheng. Knowledge Discovery and Data Mining, 2012

362 citations recorded by the source corpus.

Subfields: Ocean Engineering, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2b392e62849e0ef4b39f89ffc1ef91219382860d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ocean Engineering

1. David Wilkie
2. Cyrus Shahabi
3. Bei Pan
4. Hai Yang
5. Mohamed F. Mokbel
6. Yu Zheng

In Transportation

1. Yu Zheng
2. Jie Bao
3. Ouri Wolfson
4. Hai Yang
5. David Wilkie
6. Mohamed F. Mokbel
7. Licia Capra
8. Cyrus Shahabi

In Signal Processing

1. Yu Zheng
2. Jie Bao
3. David Wilkie
4. Mohamed F. Mokbel
5. Ouri Wolfson
6. Licia Capra
7. Hai Yang
8. Cyrus Shahabi

Citations in the corpus

• Urban Computing: Concepts, Methodologies, and Applications (2014, background): “The geo-tagged social media may also re.ect human mobility patterns in urban spaces, therefore enabling some useful applications, such as travel recommendations [Wei et al. 2012; Bao et al. 2012, 2014; Yoon et al. 2010, 2011] and location choosing for a business [Karamshuk et al. 2012].”
• Trajectory Data Mining (2015, methodology): “The other is for a free space, where moving objects (like flying birds or people hiking a mountain) do not follow paths in road networks [105], as illustrated in Figure 13 B) and C).”
• Recommendations in location-based social networks: a survey (2015, background): “Representative research In [63, 102], the authors propose the Route Inference framework based on Collective Knowledge (RICK) to construct popular routes from uncertain trajectories.”
• Crowd sensing of traffic anomalies based on human mobility and social media (2013, methodology): “Therefore, we connect each consecutive pair of GPS points with a path calculated based on the method described in [11].”
• Understanding urban human activity and mobility patterns using large-scale location-based data from online social media (2013, background): “formation provided by the location-based data which has already enabled many novel applications such as recommendation system for physical locations (or activity) [1, 2] or recommending potential customers or friend [3, 4]; and determining popular travel routes in a city [5].”