An Interactive-Voting Based Map Matching Algorithm

By Jing Yuan, Yu Zheng, Chengyang Zhang, Xing Xie, Guangzhong Sun. 2010 Eleventh International Conference on Mobile Data Management, 2010

346 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/03329fbf411b3de57c349a341b52b974fc9021f7

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Citations in the corpus

T-Finder: A Recommender System for Finding Passengers and Vacant Taxis (2013, methodology): “Using our recommender system, a taxi will find passengers more quickly and people will take a taxi more easily thereby reducing the supply/ demand disequilibrium problem to some extent.”
Where to find my next passenger (2011, methodology): “lon, located road segment (provided by map matching algorithms [13]) p.”
U-Air: when urban air quality inference meets big data (2013, methodology): “Preprocessing data flow: In this flow (denoted by dotted black arrows), we receive spatial trajectories generated by vehicles (taxicabs in the experiments) and map each trajectory onto a road network using a map-matching algorithm [12].”
T-share: A large-scale dynamic taxi ridesharing service (2013, methodology): “We map each occupied trip to the road network of Beijing using the map-matching algorithm proposed in [24].”
Travel time estimation of a path using sparse trajectories (2014, methodology): “In the above part, we project each trajectory received in a current time slot onto a road network, using a map-matching algorithm [21].”
Driving with knowledge from the physical world (2011, methodology): “This increases the uncertainty of the routes traversed by a taxi [20][27].”
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data (2011, background): “Some interesting approaches mixing points and paths that use a voting scheme have also recently been proposed [19].”
Crowd sensing of traffic anomalies based on human mobility and social media (2013, methodology): “Next, we employ IVMM algorithm [13], to map each GPS point onto a road segment.”
Trajectory Data Mining (2015, methodology): “After the map-matching process, T-Drive regards the top-k road segments frequently traversed by taxicabs as landmark nodes (i.e., the red points shown in Figure 19(a)).”
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].”
T-drive: driving directions based on taxi trajectories (2010, methodology): “2) Map matching : We employ our IVMM algorithm [14], which has a better performance than existing map-matching algorithms when dealing with the low-sampling-rate trajectories, to map each GPS point of a trip to the corresponding road segment where the point was recorded.”
Urban Computing: Concepts, Methodologies, and Applications (2014, background): “Existing research majorly focuses on subgraph pattern mining [Zheng et al. 2011b] and time-dependent routing [Yuan et al. 2010a] on a spatiotemporal graph.”
T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence (2013, methodology): “This increases the uncertainty of the routes traversed by a taxi [3], [4].”
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].”