Detecting mischoice of public transportation route based on smartphone and GIS

Jimbo, Takuya; Fujinami, Kaori

doi:10.1145/2800835.2800904

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

2018

Publication Types

Select...

Article3

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, machine learning has been vastly applied to different applications including transport and civil engineering [24,25]. Machine learning as part of artificial intelligence is about enabling computers to learn without being explicitly programmed [26].…”

Section: Related Workmentioning

confidence: 99%

Hybrid machine learning and optimisation method to solve a tri‐level road network protection problem

Kaviani

Thompson

Rajabifard

et al. 2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Hybrid machine learning and optimisation method to solve a tri‐level road network protection problem

Kaviani

Thompson

Rajabifard

et al. 2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…as software. Various sensors are embedded into the device, which allows the a system to extract a user's and/or a device's context such as engaging activity [19], [23], [26] and a person/device location [16], [24], identity of pedestrian [28], environmental conditions around a user [8], [10], [15], [27], and so on, which contributes to provide appropriate information/services to a user based on the context. According to a phone carrying survey, 17% of people determine the position of storing a mobile phone based on contextual restrictions, e.g.…”

Section: Introductionmentioning

confidence: 99%

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

Fujinami¹,

Saeki²,

Li³

et al. 2017

ijacsa

Self Cite

View full text Add to dashboard Cite

Abstract-Due to the dependency of our daily lives on smartphones, the states of the device have impact on the quality of services offered through a smartphone. In this article, we focus on the carrying states of the device while the user is walking, in which 17 states, e.g., in the front-left trouser pocket, calling phone in the right hand, in a backpack are subjects to recognition based on supervised learning with accelerometer-derived features. A large-scale data collection from 70 persons with three walking speeds allows reliable evaluation regarding suitable features and classifiers model, the feature selection method, robustness of localization against unknown person, and effect of walking speed in training a classifier. Person-independent evaluation shows that average F-measures of 17 class classification and merged 9 class classification were 0.823 and 0.913, respectively.

show abstract

SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

Yan

2018

Neural Process Lett

View full text Add to dashboard Cite

Detecting mischoice of public transportation route based on smartphone and GIS

Cited by 3 publications

References 1 publication

Hybrid machine learning and optimisation method to solve a tri‐level road network protection problem

Hybrid machine learning and optimisation method to solve a tri‐level road network protection problem

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

Contact Info

Product

Resources

About