Driving safety efficiency benchmarking using smartphone data

Tselentis, Dimitrios I.; Vlahogianni, Eleni I.

doi:10.1016/j.trc.2019.11.006

Cited by 37 publications

(21 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This finding, although expected, because drivers differ in driving aggressiveness, hints that the identification of a driver’s observation time—before forming his driving profile—should be preceded by an analysis of the aggressiveness profile. Results indicate that the most aggressive drivers (i.e., the ones with a larger number of harsh events per km) tend to converge at a faster rate than the less aggressive drivers, confirming the results of the literature [ 9 , 15 ]. More specifically, it is noticed that, on average, more aggressive drivers tend to converge (for all metrics and their volatility) at around 80 trips, while less aggressive drivers converge at around 100 trips.…”

Section: Resultssupporting

confidence: 86%

“…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%

See 1 more Smart Citation

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

Section: Resultssupporting

confidence: 86%

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

show abstract

“…total duration, total distance driven per trip, driving duration, risky hours driving per trip, harsh accelerations per 100 km, harsh braking per 100 km, speeding duration/driving duration, average speeding, average total speed, average driving speed and mobile phone usage duration/driving duration) are captured through a specially developed smartphone application provided by OSeven Telematics and transmitted to a back-end platform. Taking into account that recently published studies ( Papadimitriou et al, 2019 ; Stavrakaki et al, 2020 ; Tselentis et al, 2019 ) have used the aforementioned indicators in order to estimate driving behavior, these can form a representative set of indicators to reflect the effect of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety

Katrakazas

Michelaraki

Sekadakis

et al. 2020

Transportation Research Interdisciplinary Perspectives

Self Cite

167

122

View full text Add to dashboard Cite

The spread of the new coronavirus COVID-19, has led to unparalleled global measures such as lockdown and suspension of all retail, recreation and religious activities during the first months of 2020. Nevertheless, no scientific evidence has been reported so far with regards to the impact on road safety and driving behavior. This paper investigates the effect of COVID-19 on driving behavior and safety indicators captured through a specially developed smartphone application and transmitted to a back-end platform. These indicators are reflected with the spread of COVID-19 and the respective governmental countermeasures in two countries, namely Greece and Kingdom of Saudi Arabia (KSA), which had the most completed routes for users of the smartphone applications. It was shown that reduced traffic volumes due to lockdown, led to a slight increase in speeds by 6–11%, but more importantly to more frequent harsh acceleration and harsh braking events (up to 12% increase) as well mobile phone use (up to 42% increase) during March and April 2020, which were the months where COVID-19 spread was at its peak. On the bright side, accidents in Greece were reduced by 41% during the first month of COVID-19-induced measures and driving in the early morning hours (00:00–05:00) which are considered dangerous dropped by up to 81%. Policymakers should concentrate on establishing new speed limits and ensure larger spaces for cycling and pedestrians in order to enlarge distances between users in order to safeguard both an enhanced level of road safety and the prevention of COVID-19 spread.

show abstract

“…The type of road also affect the risk of crashing as urban, regional, and rural roads present different conditions [11]. The time also increase the risk for young people as their are more likely to crash at night and over the weekend [12], as we can see those risk factors plays an important role, thus measuring the safety efficiency of the drivers is very important [13].…”

Section: Introductionmentioning

confidence: 99%

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Celaya-Padilla

Romero-González

Galván-Tejada

et al. 2021

Sensors

View full text Add to dashboard Cite

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

show abstract

Driving safety efficiency benchmarking using smartphone data

Cited by 37 publications

References 42 publications

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Contact Info

Product

Resources

About