Dynamic Identification of Stop Locations from GPS Trajectories Based on Their Temporal and Spatial Characteristics

Ferreira, Flora; Wojtak, Weronika; Fernandes, Carlos M.; Guimarães, Pedro; Monteiro, Sérgio; Bicho, Estela; Erlhagen, Wolfram

doi:10.1007/978-3-030-86380-7_28

Cited by 6 publications

(2 citation statements)

References 31 publications

(51 reference statements)

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“…Stopping points typically exhibit high-density characteristics, making density-based clustering algorithms the primary approach for their extraction [ 4 , 5 , 6 ]. However, trajectory points possess temporal characteristics, posing a significant challenge in incorporating temporal information into traditional Euclidean distance-based density clustering algorithms for stopping point extraction.…”

Section: Introductionmentioning

confidence: 99%

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints

Wang

Pang

2023

Sensors

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The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have proven ineffective in accurately identifying non-stopping points caused by trailing or round trips. To address this issue, this paper proposes a novel density peak clustering algorithm based on coherence distance, incorporating temporal and entropy constraints, referred to as the two-step DPCC-TE. The proposed algorithm introduces a coherence index to integrate spatial and temporal features, and imposes temporal and entropy constraints on the clusters to mitigate local density increase caused by slow-moving points and back-and-forth movements. Moreover, to address the issue of interactions between subclusters after one-step clustering, a two-step clustering algorithm is proposed based on the DPCC-TE algorithm. Experimental results demonstrate that the proposed two-step clustering algorithm outperforms the DBSCAN-TE and one-step DPCC-TE methods, and achieves an accuracy of 95.49% in identifying stopping points.

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Section: Introductionmentioning

confidence: 99%

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints

Wang

Pang

2023

Sensors

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“…Which passengers and objects enter and leave next?) and issue predictions that can be useful to plan the trips and assist the driver and/or the passengers [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Data Recording Mobile Application to Create Datasets of Vehicle Users’ Routines

Guimarães

Ferreira

Silva³

et al. 2022

2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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One of today's automotive research focus is the development of vehicles for the future, with their own intelligence, aware of their occupants, able to give support to its users, striving for natural and efficient interaction, and giving rise to the concept of the cognitive vehicle. Furthermore, truly adaptive intelligence can be achieved with assistance systems capable of adapting to different drivers. Vehicles with the potential to learn users' routines and preferences, and make decisions to prepare the next trip (e.g., manage comfort; check if the usual objects are being transported), is a concrete example that has started gaining attention. To accomplish such a challenge, data-driven approaches are required. Hence, datasets that include information on the habits of different vehicle occupants and their preferences are essential for building cognitive computational models. To the best of our knowledge, there is no tool capable of obtaining these data in a real-world situation. Thus, this work proposes a mobile application capable of collecting real data and creating datasets about: (1) where and when the driver and passengers get in and out of the vehicle;(2) objects brought/taken by the occupants; and (3) vehicle settings preferences. Collected data are internally structured in files that can be uploaded at any time. The developed mobile application can be described as an easy-to-use, flexible, and free of charge solution for collecting data on the travel routines of vehicle occupants, to support the development of personalized assistance systems.

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Towards Endowing Intelligent Cars with the Ability to Learn the Routines of Multiple Drivers: A Dynamic Neural Field Model

Wojtak

Ferreira

Guimarães

et al. 2021

Computational Science and Its Applications – ICCSA 2021

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Dynamic Identification of Stop Locations from GPS Trajectories Based on Their Temporal and Spatial Characteristics

Cited by 6 publications

References 31 publications

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints

A Data Recording Mobile Application to Create Datasets of Vehicle Users’ Routines

Towards Endowing Intelligent Cars with the Ability to Learn the Routines of Multiple Drivers: A Dynamic Neural Field Model

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