Mining Trajectory Data and Identifying Patterns for Taxi Movement Trips

Ibrahim, Rami; Shafiq, Omair

doi:10.1109/icdim.2018.8847135

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Campello et al [20] proposed a hierarchical clustering method that provides a clustering hierarchy from which a simplified tree of significant clusters can be constructed, and a novel cluster stability measurement to formalize the problem of maximizing the overall stability of selected clusters, and provides interpretable dendrogram plots. Zhang et al [21], Ghamarian and Marquis [22], Lentzakis et al [23] and Ibrahim, et al [24] used HDBSCAN in their research and their conclusions prove that HDBSCAN has good results for clustering with different densities. Wilson et al [25] applied HDBSCAN on the trajectory clustering of flight data in the United States, within which a distance geometry is integrated into the method to cluster the flight trajectory with their shape characteristics.…”

Section: Introductionmentioning

confidence: 92%

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Wang

Chen

et al. 2021

JMSE

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The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

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Section: Introductionmentioning

confidence: 92%

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Wang

Chen

et al. 2021

JMSE

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“…Zhu et al [7] introduced a taxi OD and street datasets in Beijing to explore the spatio-temporal dynamic patterns of urban mobility on Beijing's urban streets. In [8], the authors used an improved DBSCAN algorithm to group similar points in each timeframe during the taxi trip. They proposed an approach to explore the movement patterns and the behavior of people.…”

Section: Study On Traffic Datamentioning

confidence: 99%

A Novel Trajectory Based Prediction Method for Urban Subway Design

Cai

Wang

et al. 2022

IJGI

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In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajectory data of taxis. In this paper, a method based on LeaderRank and Gaussian Mixed Model (GMM) is proposed to conduct subway station locations selection. In this method, the author builds a subway-passenger traffic zone weighted network and a station location prediction model. First, we evaluate the nodes in the network, then use the GPS track data of taxis to predict the location of new stations in future subway construction, and analyze and discuss the land use characteristics in the prediction area. Taking the design of the Beijing subway line as an example, the suitability of this method is illustrated.

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“…In addition, trajectory data preprocessing can improve data quality, but data processing should not only focus on improving data quality, but also pay attention to improving data expression ability through trajectory data analysis and mining [15,16]. For trajectory data, it is important to express the movement behavior of the trajectory.…”

Section: Introductionmentioning

confidence: 99%

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

Cao¹,

Jiang²,

Wang³

et al. 2020

Journal of Information Hiding and Privacy Protection

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Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods, the movement characteristics of trajectory data cannot be well expressed, which in turn affects the accuracy of the prediction results. First, a new trajectory data expression method by associating the movement behavior information is given. The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region. The movement behavior features based on pre-association may not always be the best for the prediction model. Therefore, through association analysis and importance analysis, the final association feature is selected from the pre-association features. The trajectory data is input into the LSTM networks after associated features and genetic algorithm (GA) is used to optimize the combination of the length of time window and the number of hidden layer nodes. The experimental results show that compared with the original trajectory data, the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.

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Mining Trajectory Data and Identifying Patterns for Taxi Movement Trips

Cited by 5 publications

References 27 publications

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

A Novel Trajectory Based Prediction Method for Urban Subway Design

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

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