Route Identification of Freight Vehicle's Tour Using GPS Probe Data and its Application to Evaluation of on and off Ramp Usage of Expressways

Yokota, Takashi; Tamagawa, Dai

doi:10.1016/j.sbspro.2012.03.106

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…We assumed that the trajectory ends where the vehicle remains in the same position for longer than 8 hours. Using the results of a preliminary study (35), we produced a table of sequential stop-move events with the following attributes: i. vehicle ID; ii. trajectory ID: a unique sequential identifier per vehicle and per trajectory; iii.…”

Section: Data Preparationmentioning

confidence: 99%

“…In terms of truck GPS data, the literature in freight transportation recognises that a wide variety of types exists in order to capture and understand several aspects of truck movement. Examples include stop and trip-leg identification [4,27,28], tour characteristics [7,17], travel time reliability and terminal performance [12,[29][30][31], identification of key supply chain players [32], toll impact on truck-speed profiles [33], and route choice behaviour [15,[34][35][36][37][38]. Notably, stop identification constitutes a crucial piece of information that is common to several of the aforementioned studies and is positioned prior to identifying trips, configuring routes, and performing other ancillary analyses [39].…”

Section: Introductionmentioning

confidence: 99%

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Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model

Taghavi,

Irannezhad,

Prato

2023

IEEE Access

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The increasing wealth of truck global positioning system (GPS) data has broadened the opportunities for understanding freight logistics activities and enhancing research capabilities to real-world case studies. A very important piece of information from the planning and regulatory perspective concerns the occurrence and location of rest stops. In this study, we propose a data-driven unsupervised machine learning method to impute truck stop events by using a Continuous Hidden Markov Model (CHMM). Specifically, we estimate the joint probability distribution of a mixture of multivariate Gaussian densities, whose parameters depend on the latent states of a Markov chain. Each density represents a cluster of stops that are identified not only from their spatial proximity but also from their temporal proximity as the clustering of the rest stops depends on latent states that are conditional on expected times retrieved from the observed data. In this study, we applied the proposed method to a database containing more than 71 million GPS records of Australian trucks, and we particularly aimed to identify rest stops based on a list of features related to the locations and the load of the trucks. The results showed that the CHMM could account for the location proximity for different activities of truck drivers, and they were validated against complementary data on truck loads and land use by using a stratified sampling technique. Validation results indicated that 94.1% of the rest stops were correctly identified, and highlighted the advantage of the proposed approach without any requirement of labelled data, driver logbook or complimentary survey.

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Section: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model

Taghavi,

Irannezhad,

Prato

2023

IEEE Access

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“…Large applications of freight activity analysis require methodologies that rely on minimal manual input and fully exploit GPS data mining. To this effect, Yokota and Tamagawa ( 14 ) used GPS data to evaluate road network usage by freight vehicles. They proposed an algorithm with dynamic programming for map-matching and route identification, particularly of expressway ramps.…”

Section: Literature Reviewmentioning

confidence: 99%

Mechanistic Identification of Freight Activity Stops from Global Positioning System Data

Holguín‐Veras

Encarnación

Pérez-Guzmán

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The identification of freight pick-ups and deliveries, referred to as “freight activity” in this paper, is crucial to characterizing freight operations and assessing the performance of freight transportation systems. However, identifying freight activity stops from global positioning system (GPS) data is challenging, particularly in urban freight where congested traffic is common. This paper presents a mechanistic—because it is based on the physics of driving patterns—procedure to identify freight activity stops from raw GPS data. The procedure was implemented to identify stops in three distinct case studies that present a wide range of traffic conditions: Barranquilla, Colombia; Dhaka, Bangladesh; and New York City, United States. The results show that the procedure achieves an average accuracy of above 98.6% when identifying freight activity stops. The results of the proposed procedure were compared with results from support vector machines, random forest, and k nearest neighbors. The mechanistic procedure outperformed these methods in correctly classifying freight activity using second-by-second GPS data.

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“…Unfortunately, map-matching algorithms developed in Japan have not often been published in English and referenced. The proposed algorithm is a 'topological, probabilistic, and advanced map-matching algorithm' and is as an extension of the algorithm proposed by Yokota and Tamagawa [6,7]. Another problem concerning map-matching concerns sparse probe data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Before the proposed map-matching algorithm is described in detail, the basis of the algorithm, which was proposed by Yokota and Tamagawa [6,7], is described first. In the DRM database, Japan's road network is split into two layers: a basic-road network and allroad networks.…”

Section: Overview Of the Previous Workmentioning

confidence: 99%

Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data

Yokota

Okude

Sakamoto

et al. 2019

IET Intelligent Transport Systems

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Route Identification of Freight Vehicle's Tour Using GPS Probe Data and its Application to Evaluation of on and off Ramp Usage of Expressways

Cited by 6 publications

References 10 publications

Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model

Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model

Mechanistic Identification of Freight Activity Stops from Global Positioning System Data

Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data

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