The Role of GNSS-RTN in Transportation Applications

Raza, Sajid; Al-Kaisy, Ahmed; Teixeira, Rafael; Meyer, Benjamin

doi:10.3390/encyclopedia2030083

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Global Positioning System (GPS) devices are ubiquitous tools used to record spatial and temporal information of human movement during trips and have significant application in transportation sector (Raza et al 2022;Zhao et al 2017). However, GPS tracking data can only record positional recording characteristics such as latitude, longitude, altitude, speed, and time without any information regarding which transportation mode is used during a trip.…”

Section: Transportationmentioning

confidence: 99%

A Deep Semi-Supervised Machine Learning Algorithm for Detecting Transportation Modes Based on Gps Tracking Data

Sadeghian¹,

Golshan²,

Zhao³

et al. 2022

SSRN Journal

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Section: Transportationmentioning

confidence: 99%

A Deep Semi-Supervised Machine Learning Algorithm for Detecting Transportation Modes Based on Gps Tracking Data

Sadeghian¹,

Golshan²,

Zhao³

et al. 2022

SSRN Journal

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“…Significant technological advances have been made in global navigation satellite systems (GNSS) which include the GPS (U.S. global positioning system) and its counterparts: GLONASS (Russia), Galileo (Europe), QZSS (Japan), and BeiDou (China) [1]. The applications of geospatial data embrace every sphere of modern-day science and industry where geographical positioning matters [2]. The list includes navigation, agriculture, logistics and transportation, ecology, forestry, mining, and many others.…”

Section: Introductionmentioning

confidence: 99%

Statewide GNSS-RTN Systems: Current Practices

Raza¹,

Al-Kaisy²

2023

JGIS

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The applications of geospatial technologies and positioning data embrace every sphere of modern-day science and industry. With technological advancement, the demands for highly accurate positioning services in real-time led to the development of the Global Navigation Satellite System-Real-Time Network (GNSS-RTN). While there is numerous published information on the technical aspects of the GNSS-RTN technology, information on the best practices or guidelines in building, operating, and managing the GNSS-RTN networks is lacking in practice. To better understand the current practice in establishing and operating the GNSS-RTN systems, an online questionnaire survey was sent to the GNSS-RTN system owners/operators across the U.S. Additionally, a thorough review of available literature on business models and interviews with representatives of two major manufacturers/vendors of GNSS-RTN products and services were conducted. Study results revealed a great deal of inconsistency in current practices among states in the way the GNSS-RTN systems are built, operated, and managed. Aspects of the diversity in state practices involved the business models for the GNSS-RTN systems besides the technical attributes of the network and system products. The information gathered in this study is important in helping state agencies make informed decisions as they build, expand or manage their own GNSS-RTN systems.

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A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data

Sadeghian,

Golshan,

Zhao

et al. 2024

Transportation

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Transportation research has benefited from GPS tracking devices since a higher volume of data can be acquired. Trip information such as travel speed, time, and most visited locations can be easily extracted from raw GPS tracking data. However, transportation modes cannot be extracted directly and require more complex analytical processes. Common approaches for detecting travel modes heavily depend on manual labelling of trajectories with accurate trip information, which is inefficient in many aspects. This paper proposes a method of semi-supervised machine learning by using minimal labelled data. The method can accept GPS trajectory with adjustable length and extract latent information with long short-term memory (LSTM) Autoencoder. The method adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. The proposed method is assessed by applying it to the case study where an accuracy of 93.94% can be achieved, which significantly outperforms similar studies.

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The Role of GNSS-RTN in Transportation Applications

Cited by 5 publications

References 30 publications

A Deep Semi-Supervised Machine Learning Algorithm for Detecting Transportation Modes Based on Gps Tracking Data

A Deep Semi-Supervised Machine Learning Algorithm for Detecting Transportation Modes Based on Gps Tracking Data

Statewide GNSS-RTN Systems: Current Practices

A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data

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