Analysis of driver behaviour through smartphone data: The case of mobile phone use while driving

Papadimitriou, Eleonora; Argyropoulou, Anastasia; Tselentis, Dimitrios I.; Yannis, George

doi:10.1016/j.ssci.2019.05.059

Cited by 75 publications

(28 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Unlike social media data which is collected from social network applications via mobile phones and other devices, mobile-phone-based data is collected either through public applications developed by third-parties (to provide navigation services or LBS) or through dedicated applications designed by researchers for specific purposes ( 47 ). A new method was proposed to investigate driving offense—mobile phone use while driving—on the basis of driver exposure and behavior metrics using data collected using smartphone sensors ( 32 ). Another study examined GPS-enabled mobile phone location data volunteered by 127 university students to assess urban street connectivity and confirmed that such data could provide an accessible and flexible evidence base to support the design of urban spaces ( 30 ).…”

Section: Urban Big Data In Travel Behavior Researchmentioning

confidence: 99%

Role of Urban Big Data in Travel Behavior Research

Wang

Hess

2020

Transportation Research Record

View full text Add to dashboard Cite

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

show abstract

Section: Urban Big Data In Travel Behavior Researchmentioning

confidence: 99%

Role of Urban Big Data in Travel Behavior Research

Wang

Hess

2020

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…Existing studies can be classified in several ways depending on the methodology applied or the approach followed to assess the effects of mobile phone usage while driving. The study by [24] distinguishes among the following three kinds of studies: driving simulator experiments, naturalistic driving experiments and crash analysis. Differences in findings regarding the impact of mobile phone conversation on driving found in driving simulators versus naturalistic driving studies are investigated in the review paper by [25].…”

Section: Mobile Phone Use During Car Driving: a Brief Literature Rementioning

confidence: 99%

Comparative analysis of the effects of mobile phone use on driving performance using ANOVA and ANCOVA

Fancello

Adamu

Serra

et al. 2020

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…Among the factors that relate to humans’ actions and reactions on the road, aggressiveness and distraction in driving behavior are of particular interest, as they become easier to monitor and study using the latest advances in technology [ 7 , 8 , 9 ]. More specifically, literature related to monitoring driving behavior using modern technology has centered to three attributes describing unsafe driving behavior, namely mobile phone usage, driving above the speed limit (speeding) and harsh driving [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

Stavrakaki

Tselentis

Barmpounakis

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

show abstract

Analysis of driver behaviour through smartphone data: The case of mobile phone use while driving

Cited by 75 publications

References 25 publications

Role of Urban Big Data in Travel Behavior Research

Role of Urban Big Data in Travel Behavior Research

Comparative analysis of the effects of mobile phone use on driving performance using ANOVA and ANCOVA

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

Contact Info

Product

Resources

About