Proposal for a Pivot-Based Vehicle Trajectory Clustering Method

Reyes, Gary; Lanzarini, Laura Cristina; Hasperué, Waldo; Bariviera, Aurelio F.

doi:10.1177/03611981211058429

Cited by 5 publications

(3 citation statements)

References 73 publications

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“…However, due to the large scale, time-aware, and lane change characteristics of vehicles, clustering vehicle trajectory is still a challenge. A great deal of trajectory clustering algorithms has been put forward and applied in the feld of transportation planning [21][22][23]. For example, Hong et al [24] proposed a TOPOSCAN trajectory clustering algorithm, which can be used in directed networks.…”

Section: Trajectory Clustering Studies Related To Transportationmentioning

confidence: 99%

Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach

Wang

Tian

Yang

et al. 2023

Journal of Advanced Transportation

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Many free-floating bicycle-sharing (FFBS) operators in cold region cities will put bicycles in warehouses and suspend services in winter due to factors such as safety and maintenance costs, resulting in the corresponding travel demand no longer be met. Considering the short-distance and green travel characteristics of FFBS, the flexible bus, as a sustainable demand-responsive transit service, is a suitable alternative transportation mode. To operate such a flexible bus system, service area and operation time planning is a key stage, however, the planning methods in relevant studies are not suitable for this research scenario. In view of the above, this paper proposed a data-driven method to determine the service area and operation time of flexible buses based on FFBS data. Firstly, an FFBS trip path reconstruction algorithm consists of fine-grained road network modelling and trajectory matching is proposed. Then, in the defined time slice, incorporating the idea of ride-sharing routes generation, according to the density-based clustering principle and considering topology between trajectories, a path clustering algorithm PATHSCAN is developed to generate the one-day path clusters. After that, a frequent pattern mining algorithm is applied to the multiday path clusters, and frequent pattern results with spatio-temporal correlation will be merged into the final service area. The generated planning results will cover ride-matching trips and high-frequency riding paths. Detailed application analysis and verification are carried out by using the real data from Tianjin, China. Through the evaluation and verification under the relatively limited experimental data set, the proposed data-driven method shows ideal planning results. Flexible bus service can supplement the green short-distance travel mode after the suspension of FFBS and can avoid FFBS travel demands switched to unsustainable transportation modes to a large extent. This study will contribute to urban sustainable transportation development and improving greenness.

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Section: Trajectory Clustering Studies Related To Transportationmentioning

confidence: 99%

Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach

Wang

Tian

Yang

et al. 2023

Journal of Advanced Transportation

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“…Additionally, trajectory clustering can also be used to extract meaning from the data. For example, by identifying movement patterns, researchers can gain insights into how people, animals, or vehicles use a particular area, such as which routes they prefer or which areas they avoid [8]. This information can then be used to make informed decisions, such as in transportation planning, where it can be used to optimize infrastructure and reduce traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Efficient Trajectory Clustering with Road Network Constraints Based on Spatiotemporal Buffering

Hussain

Hassan

Nasar

et al. 2023

IJGI

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The analysis of individuals’ movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users’ trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods.

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“…Como fue establecido por (Reyes, Lanzarini, Hasperué, et al, 2021), el análisis de trayectorias vehiculares con el objetivo de generar clústeres e identificar los patrones que emergen contribuyen considerablemente al análisis y caracterización del flujo de tránsito en diferentes partes del mundo. Por lo que el presente proyecto busca, mediante el análisis de trayectorias utilizando algoritmos de clustering basados en densidad, lograr la identificación de patrones, también conocidos como indicadores, de congestionamiento.…”

Section: Introductionunclassified

Identificación de patrones de congestionamiento vehicular utilizando algoritmos de agrupamiento de trayectorias basados en densidad

Reyes

Roldán²,

Macias³

et al. 2022

Inv. Tecn. Innov.

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Contexto: Este trabajo se centra en identificar patrones de congestionamiento. Para ello se utilizan 3 conjuntos de datos de las ciudades de Beijing, Guayaquil y Roma. Método: Se efectúa la implementación del algoritmo Dyclee, modificado para agrupar celdas de trayectorias en base a velocidades, con el cual se realiza experimentos en los que se calculó adecuadamente los patrones de volumen de servicio e índice de operatividad en base a sus resultados. La modalidad de la investigación predominante es la bibliográfica. Sin embargo, el estudio también incluye características de una investigación de campo, debido a que los algoritmos fueron ejecutados con datos de trayectorias GPS de tres conjuntos de datos diferentes. Resultados: Para validar esta investigación se ejecutaron dos experimentos. En el primer experimento se determinó que el algortimo Dyclee calculó correctamente los patrones de congestionamiento. En el segundo experimento se encontró que TRADBSCAN obtuvo los mejores resultados con respecto a métricas de validación tomando en cuenta los parámetros establecidos. Conclusiones: Se concluyó que el algoritmo de clustering basado en densidad Dyclee, es capaz de identificar patrones de congestionamiento vehicular.

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Proposal for a Pivot-Based Vehicle Trajectory Clustering Method

Cited by 5 publications

References 73 publications

Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach

Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach

Efficient Trajectory Clustering with Road Network Constraints Based on Spatiotemporal Buffering

Identificación de patrones de congestionamiento vehicular utilizando algoritmos de agrupamiento de trayectorias basados en densidad

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