Map-matching for low-sampling-rate GPS trajectories

By Yin Lou, Chengyang Zhang, Yu Zheng, Xing Xie, Wei Wang. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2009

886 citations recorded by the source corpus.

Subfields: Building and Construction, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0e80a2bdc44f4b89747ad93709b52a611dbd5bbe

How researchers see this paper

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

In Building and Construction

1. Yu Zheng
2. Xing Xie
3. Jing Yuan
4. Haitao Yuan
5. Guoliang Li
6. Ouri Wolfson
7. Guangzhong Sun
8. Zhifeng Bao

In Ocean Engineering

1. Yu Zheng
2. Jing Yuan
3. Xing Xie
4. Guangzhong Sun
5. Haitao Yuan
6. Guoliang Li
7. Zhifeng Bao
8. Hai Yang

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jing Yuan
4. Ouri Wolfson
5. Guangzhong Sun
6. Guoliang Li
7. Haitao Yuan
8. Licia Capra

Citations in the corpus

• An Interactive-Voting Based Map Matching Algorithm (2010, methodology): “[1]”
• End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners (2018, background): “The localization based on vehicle’s moving trajectories is tackled under the name map-matching, and this, in itself, is a long-standing research problem [80,81,82].”
• Trajectory Data Mining (2015, methodology): “…to the additional categorized into four groups: geometric [Greenfeld 2002], topological [Chen et al. 2003; Yin and Wolfson 2004], probabilistic [Ochieng et al. 2004; Pink and Hummel 2008; Quddus et al. 2006], and other advanced techniques [Lou et al. 2009; Newson and Krumm 2009; Yuan et al. 2010b].”
• T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence (2013, background): “This increases the uncertainty of the routes traversed by a taxi [3], [4].”
• Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “Map-matching techniques deal­ing with high-sampling-rate trajectories have already been commercialized in per­sonal navigation devices, while those for low-sampling-rate trajectories [Lou et al. 2009] are still considered challenging.”
• Driving with knowledge from the physical world (2011, background): “This increases the uncertainty of the routes traversed by a taxi [20][27].”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “Fourthly, methods leverage some powerful models to solve the map-matching problem, such as partial filter [47, 49], HMM (hidden Markov model) [44–46, 48, 50], CRF (conditional random field) [51] and WGT (weighted graph technique) [52].”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “Using a road network, map-matching [119, 129] projects a raw trajectory onto a real path, and supports cleaning and compression.”