Context-Awareness in the Car: Prediction, Evaluation and Usage of Route Trajectories

Helmholz, Patrick; Ziesmann, Edgar; Robra-Bissantz, Susanne

doi:10.1007/978-3-642-38827-9_30

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Several papers investigate how contextual information improves the performance of predictive models . The most common examples are time‐context such as the day of the week, or part of the day (morning/evening) . Weather information is also sometimes incorporated .…”

Section: Characteristics Of Mobility Datamentioning

confidence: 99%

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A survey of evaluation methods for personal route and destination prediction from mobility traces

Stegmann

Žliobaitė

Tolvanen

et al. 2017

WIREs Data Min & Knowl

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Personal mobility data can nowadays be easily collected by personal mobile phones and used for analytical modeling. To assist in such an analysis, a variety of computational approaches have been developed. The goal is to extract mobility patterns in order to provide traveling assistance, information, recommendations or on‐demand services. While various computational techniques are being developed, research literature on destination and route prediction lacks consistency in evaluation methods for such approaches. This study presents a review and categorization of evaluation criteria and terminology used in assessing the performance of such methods. The review is complemented by experimental analysis of selected evaluation criteria, to highlight the nuances existing between the evaluation measures. The experimental study uses previously unpublished mobility data of 15 users collected over a period of 6 months in Helsinki metropolitan area in Finland. The article is primarily intended for researchers developing approaches for personalized mobility analysis, as well as a guideline for practitioners to select criteria when assessing and selecting between computational approaches. Our main recommendation is to consider user‐specific accuracy measures in addition to averaged aggregates, as well as to take into consideration that for many users accuracy does not saturate fast and the performance keeps evolving over time. Therefore, we recommend using time‐weighted measures. WIREs Data Mining Knowl Discov 2018, 8:e1237. doi: 10.1002/widm.1237 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Society and Culture Application Areas > Industry Specific Applications

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Section: Characteristics Of Mobility Datamentioning

confidence: 99%

“…16,24,25 The most common examples are time-context such as the day of the week, or part of the day (morning/evening). 26 Weather information is also sometimes incorporated. 27 These are all contextual information as a function of time.…”

Section: Concepts and Notationmentioning

confidence: 99%

A survey of evaluation methods for personal route and destination prediction from mobility traces

Stegmann

Žliobaitė

Tolvanen

et al. 2017

WIREs Data Min & Knowl

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“…Some music recommendation systems available in the market like Last.fm [Last 2014], and the existing research work in mobile music recommendation like CAMRS [Wang et al 2012], recommend music to users based only on their listening behavior, history, and/or locations [Baltrunas et al 2012]; or only focus on distributed music resource (i.e., decoder) sharing [Ayaki et al 2009]. Also, recent works like AmbiTune [Helmholz et al 2013[Helmholz et al , 2014 only adapt the music to drivers based on the prediction of their route trajectories and driving speeds, but they do not include interactive methods that enable individual drivers to recommend suitable music in their new driving situations collaboratively. On the other hand, research works [Cassidy et al 2009;Hunter et al 2011] and our online survey [Driving 2014] have demonstrated that drivers' situations including their mood and fatigue status impact the choice of preferable music significantly while driving.…”

Section: Introductionmentioning

confidence: 99%

SAfeDJ

Deng

Zhao

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

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Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and moodfatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

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ISO/SAE 21434-Based Risk Assessment of Security Incidents in Automated Road Vehicles

Püllen

Liske

Katzenbeisser

2021

Lecture Notes in Computer Science

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Context-Awareness in the Car: Prediction, Evaluation and Usage of Route Trajectories

Cited by 7 publications

References 12 publications

A survey of evaluation methods for personal route and destination prediction from mobility traces

A survey of evaluation methods for personal route and destination prediction from mobility traces

SAfeDJ

ISO/SAE 21434-Based Risk Assessment of Security Incidents in Automated Road Vehicles

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