Detecting Pattern Changes in Individual Travel Behavior from Vehicle GPS/GNSS Data

Lou, Jingfeng; Cheng, Aiguo

doi:10.3390/s20082295

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…After time series clustering, we randomly selected 100 mileag each TMTP types to show the characteristics of different TMTP ty The orange line is the center of the cluster for different TMTP type indicates that driving time is mostly concentrated between 7:00-9:0 a small concentration at 12:00-13:00. User types can be inferred time patterns [3,39]. As such, the 𝐶 𝑇𝑀𝐷𝑃 driving pattern may ref ior of household members going to work and picking up students.…”

Section: Distribution Fitting Methodsmentioning

confidence: 99%

A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification

Zhang

Wang

et al. 2021

Sustainability

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Determining the distribution fitting of traditional private vehicle user driving behavior is an effective way to understand the differences between different users and provides valuable information on user travel demands. The classification of users is significant to product improvement, precision marketing, and driving recommendations. This study proposed a method which includes four aspects: (1) data collection; (2) data preprocessing; (3) data analysis—a two-stage hybrid user classification, and (4) distribution fitting method. A two-stage hybrid user classification method is used to cluster traditional vehicle users. First, the first-stage classification of the classification method extracts the daily typical time–mileage-series travel patterns (TMTP) to obtain user driving time characteristics. This first-stage classification also extracts the mean and standard deviation of the daily vehicle mileage traveled (DVMT) to express user driving demands. Next, users are divided by K-means based on the driving time characteristics and driving demands from the first stage. Finally, a three-parameter log-normal distribution is used to fit the DVMT of different user types. Comparison with traditional clustering based on the mean and standard deviation and the proportion of each vehicle’s time series in the TMTP types, this study reveals that the new methods provide significant advantages in analyzing driving behavior and high reference value for enterprises making electric vehicle driving range recommendations, car market segmentation, and policy making decisions.

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Section: Distribution Fitting Methodsmentioning

confidence: 99%

A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification

Zhang

Wang

et al. 2021

Sustainability

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“…In addition, researchers have delved into the realm of dynamic clustering algorithms to manage the continuous influx of trajectory data and dynamically adjust to shifts in traffic patterns over time. Through the implementation of these dynamic approaches, a timely identification of an emerging congestion is possible [21,22].…”

Section: Related Workmentioning

confidence: 99%

Method for the Identification and Classification of Zones with Vehicular Congestion

Reyes,

Tolozano-Benites,

Lanzarini

et al. 2024

IJGI

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Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells.

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“…Por otra parte, en [12]se presenta una nueva técnica de agrupamiento de trayectorias que utiliza campos vectoriales para representar los centros de los grupos y proponer una definición de similitud entre trayectorias. Hoy en día continúan los esfuerzos de investigación en esta área como se evidencia en [13], [14].…”

Section: Trabajos Previosunclassified

GPS Trajectory segmentation and clustering method

Reyes¹,

Córdova²,

León³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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The analysis of GPS trajectories, in the context of current life, constitutes one of the main sources of support for decision making. The detection of patterns in GPS trajectories that allow determining actions to follow and decisions to be taken more quickly is one of the main lines of research in this field. In this paper we propose a method of segmentation and clustering of GPS trajectories composed of a segmentation algorithm that aims to segment the trajectories based on a partitioning criterion. Then the generated segments are used by a trajectory clustering algorithm based on traditional k-means; the method improves the clustering centroid selection process by using an initial grid and proposes a similarity function between trajectory segments for the clustering process. The results of the experiments performed show a significant improvement compared to the methods published in the literature.

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Detecting Pattern Changes in Individual Travel Behavior from Vehicle GPS/GNSS Data

Cited by 9 publications

References 29 publications

A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification

A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification

Method for the Identification and Classification of Zones with Vehicular Congestion

GPS Trajectory segmentation and clustering method

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