NDLI: Vehicle type detection and passenger satisfaction analysis using smartphone sensors and digital surveys

Content Provider	IET Digital Library
Author	Perttula, Arto Nguyen, Nhan Collin, Jussi Jokinen, Jani Pekka
Abstract	Detailed knowledge of passenger context is essential for developing intelligent transportation systems. For example, automated ticket sales and personal routing require more information about used means of transportation than traditional time tables can offer. Here, the authors contribute to this topic by using measurements from smartphone sensors to predict (i) whether a person is inside a bus, (ii) if the person is travelling in a diesel or an electric bus, and (iii) how the person is rating the quality of the bus ride. All three tasks are worked out by using a selection of machine learning (ML) algorithms. In tandem with sensor data, collecting a digital passenger survey was conducted to add passengers' own evaluation of the quality of their bus ride. The tests showed that the context of a passenger can be predicted relatively well. However, the prediction of passenger satisfaction is a complex task that requires further research. This research aims to give a good premise for such efforts.
Starting Page	1499
Ending Page	1506
Page Count	8
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 10, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5349
Journal	IET Intelligent Transport Systems
Publisher Date	2019-06-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Ticket Sales Bus Ride Customer Satisfaction Detailed Knowledge Digital Passenger Survey Digital Surveys Electric Bus Intelligent Transport System Knowledge Engineering Technique Learning in AI Machine Learning Algorithm Mobile Computing Passenger Context Passenger Satisfaction Passengers Personal Routing Pervasive Computing Road Vehicle Sensor Data Smart Phones Smartphone Sonsor Traditional Time Tables Traffic Engineering Computing Transportation Travel Industry Ubiquitous Vehicle Type Detection
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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Vehicle type detection and passenger satisfaction analysis using smartphone sensors and digital surveys

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