hls__Interpreting Trajectories from Multiple Views_2022_Chen

most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments
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multi-view trajectory representation
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a segment encoder is developed to capture the spatio-temporal dependencies at a fine granularity
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n adaptive self-attention module is designed to boost performance
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o characterize the natural trajectory structure consisting of alternatively arranged links and intersections
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realize a tradeoff between the multi-view spatio-temporal features
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traditional ETA algorithms mainly employ the divide-and-conquer strategy by representing a trajectory as a segment sequence and then summing up the local predictions
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segment-view representation is artificially produced to capture the fined-grained local traffic conditions, which is however not comprehensive in characterizing the natural structure of the road network
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preserve static road characteristics, such as pavement type, road width and road functional level
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valued information such as the waiting time, the number of traffic lights, and the historical traffic volume
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the link- and intersection-views characterize the trajectory attributes from a coarse perspective; a link can be further decomposed into several segments, and hence the segment-view representation models the spatial dependencies at a fine granularity
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On the one hand
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However, without explicitly modeling the link-view characteristics, existing studies can hardly model the coherent consistency across segments within the same links.
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On the other hand
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Hierarchical Self-Attention Network for Estimating the Time of Arrival
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HierETA exploits the hierarchical relationship among the three views to portray the underlying road structure
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proposed hierarchical self-attention network organizes the segment-, link-, and intersection-views efficiently according to their natural relationships.
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adaptive self-attention network to jointly leverage the global and local patterns for spatio-temporal dependency modeling within the multi-view representation framework.
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hierarchy-aware attention decoder
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GMAN [ 50] employs a graph multi-attention structure to extract the spatial and temporal relationships
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graph representation learning generally suffers from the negative impact from irrelevant spatial neighboring regions, resulting in error propagation especially when the involved area grows larger
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graph modeling is limited to process only narrow neighboring regions and falls short on developing large-scale urban-wise systems
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DeepTTE
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DeepGTT
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learns the representation of spatio-temporal information using a multi-relational network;
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extracts the travel speed and representation of road network from historical trajectories based on tensor decomposition and graph embedding.
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we design an adaptive self-attention network to explicitly capture the spatio-temporal dependencies of the trajectory using multi-view sequences
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Attribute Feature Extractor
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Hierarchical Self-Attention Network for Multi-View Trajectory Representation
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Hierarchy-Aware Attention Decoder
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capture the spatiotemporal dependencies of segments in the same link
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a local semantic pattern
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a gating mechanism
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Joint Link-Intersection Encoder.
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the joint link-intersection encoder also includes a self-attention layer, a residual connection and a layer normalization
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as links and intersections appear alternatively
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coarse-scale representation
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t fails to model the consistency shared within the same link
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employ two BiLSTMs to respectively encode the links and intersections
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local pattern
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segment-view context feature that captures the local traffic conditions
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joint link-intersection context feature that preserves the common road attributes
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separately processing each segment without considering the link-view correlation is problematic as it lacks the feedback from the link-view consistency.
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attention guidance that adopts the link-view consistency to further adjust the segment-view attention
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we can adaptively select the most relevant features from different representation granularities.
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travel time estimation is closely related to the critical components
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EXPERIMENTS
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probability density functions (PDFs) and cumulative distribution functions (CDFs)
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We repeat each experiment for five times except the statistics-based approach Route-ETA and report the mean and the standard deviation of different runs.
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mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and satisfaction rate (SR), similar to existing approaches [ 23 ]. Specifically, SR refers to the fraction of trips with error rates less than 10% and a higher SR indicates better performance and customer satisfaction
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ConstGAT considers the graph structures of the road network to exploit the joint relations of spatio-temporal information.
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That is, interpreting the trajectory from multiple views effectively portrays the hierarchical structure of road network and eases the error propagation for estimating the travel time.
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window sizes
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he correlation between adjacent segments slightly decreases while the modeling uncertainty increases.
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The local attention in encoder is removed to verify the effectiveness for modeling the semantic traffic condition.
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verify the necessity of extracting the structural traffic pattern.
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removing the joint link-intersection encoder
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HierETA performs better than both variants that eliminating local and global attentions, which is contributed to the introduction of the global structural and local semantic patterns.
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