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YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang. Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning[J]. Journal of Transport Information and Safety.
Citation: YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang. Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning[J]. Journal of Transport Information and Safety.

Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning

  • Received Date: 2021-07-31
    Available Online: 2021-12-14
  • To accurately discover the companion relationship among passengers in the interior space of a cruise, UWB positioning is employed in the cruise to carry out on-board personnel location experiment. An improved Haussdorff-DBSCAN based scheme combined with indoor positional semantics is proposed to realize the trajectory clustering of the passenger trajectories, considering the characteristics of the UWB location data. Afterwards, the LSTM neural network is applied to predict the changing similarity of the suspected companions. Traditional Hausdorff algorithm does not consider the consistency of trajectory timing while calculating the trajectory similarity, and the introduction of positional semantic sequence can solve this problem well. In the first phase, the passenger trajectory data set is input to the improved Hausdorff-DBSCAN algorithm, and the clustering radius is determined according to the overall similarity threshold of trajectories. The outputs are the emerging clusters of passenger trajectories in the same companion group. In the second phase, the LSTM neural network takes the point similarity sequence with a fixed time window as the input to predict the point similarity value at the next time. The sequential change of passengers companion relationship is analyzed by the similarity threshold and prediction results. The validity of the presented algorithm is demonstrated by the trajectory data obtained from the passengers simulation on one deck of the cruise under study, which is modeled in Anylogic. The results indicate that the precision of the proposed algorithm reaches 0.92, the recall value reaches 0.95 and the F1 value is 0.934, which are at least 5.7 percent, 8.0 percent and 6.7 percent higher than the comparing algorithm. The LSTM neural network also shows a promising effect in predicting the changing trend of the similarity, for the loss is at a stable level of 3 to 4 percent.


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