Travel time estimation of a path using sparse trajectories

By Yilun Wang, Yu Zheng, Yexiang Xue. Knowledge Discovery and Data Mining, 2014

523 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/44d811b066187865cce71d9acecdda8c3f9c8eee

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

In Transportation

In Signal Processing

Citations in the corpus

Learning k for kNN Classification (2017, background): “(3) The (cid:2) 1 -norm has been proved to lead the optimal sparse W ∗ [Zhu et al. 2016a; Wang et al. 2014; Zhang et al. 2011].”
Trajectory Data Mining (2015, methodology): “Figure 11 presents a sufﬁx-tree-based indexing indexing structure [Wang et al. 2014] that manages the four trajectories Tr 1 , Tr 2 , Tr 3 , and Tr 4 traversing a road network.”
Diagnosing New York city's noises with ubiquitous data (2014, methodology): “[21][23][28][29] used a context-aware tensor decomposition incorporating additional information, such as the activityactivity correlation and geographical features of a location, to conduct different kinds of recommendations.”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[127] model different drivers’ travel times on different road segments in different time slots with a three-dimensional tensor and then fill in the tensor’s missing values through a context-aware tensor decomposition (TD) approach.”
Methodologies for Cross-Domain Data Fusion: An Overview (2015, methodology): “The data was used in [65] and can be download from the following URL: http:// research.”
Tensors for Data Mining and Data Fusion (2016, methodology): “In Wang et al. [2014], the problem the authors solve is the one of estimating the travel time of a particular trajectory in a city road network.”
When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks (2018, methodology): “Although individual TTE methods (Yang, Guo, and Jensen 2013; Wang et al. 2016c; Pan, Demiryurek, and Shahabi 2012; Wang, Zheng, and Xue 2014) can estimate accurate travel time for each road segment, it cannot model complex traffic conditions within the entire path, including road intersections,…”
Dynamic route planning with real-time traffic predictions (2017, background): “Recent work [20] predicts the travel time of routes.”
Traffic prediction in a bike-sharing system (2015, background): “Wang et al. [21] proposed a model to predict time-dependent travel time of a path based on taxi trajectories, POIs, and road networks.”