Post-trip safety interventions: State-of-the-art, challenges, and practical implications

Michelaraki, Eva; Katrakazas, Christos; Yannis, George; Filtness, Ashleigh J.; Talbot, Rachel; Hancox, Graham; Pilkington-Cheney, Fran; Brijs, Kris; Ross, Veerle; Dirix, Hélène; Neven, An; Paul, Roeland; Brijs, Tom; Fortsakis, Petros; Frantzola, Eleni Konstantina; Taveira, Rodrigo Zaiden

doi:10.1016/j.jsr.2021.02.005

Cited by 15 publications

(7 citation statements)

References 56 publications

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“…For example, the DaCoTa Project, the Safetynet Project, (Pešić & Pešić, 2020), (Papadimitriou & Yannis, 2013) and the Sunflower Project (Papadimitriou & Yannis, 2013), (Chen et al, 2016) were all important projects that contributed significantly to road safety management. Other projects identified in the studies, such as the i-DREAMS Project (Michelaraki et al, 2021) and the DICA-VE Project (Coelho & Guarnaccia, 2020), were focused on specific road safety components such as road users or vehicles. These projects demonstrate the potential of innovative solutions to address road safety challenges faced by different countries.…”

Section: Framework and Flagship Projectsmentioning

confidence: 99%

“…2016); (Ansari et al,. 2023); (Corben et al, 2010); (Sun & Xiang, 2019); (Horswill et al, 2021); (Mooren et al,2014); (Albalate & Yarygina, 2013); (Cafiso & D'Agostino, 2016); (Sangrar et al, 2019); (Michelaraki et al, 2021); (Domenichini et al, 2018); (Fu & Mao, 2018); (Nogal & Honfi, 2019); (Islam et al, 2021); (Osei et al,. 2021); (Lu et al, 2019); (Connors et al, 2013); (Chajmowicz & Cuny, 2019); (Oviedo-Trespalacios et al, 2016); (Marciano et al, 2015); (Huertas-Leyva et al, 2019).…”

Section: Identification Of Main Road Safety Measures Usedmentioning

confidence: 99%

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Road Safety Performance Monitoring Practices: A Literature Review

EL KHALAI,

CHORFI,

BERRADO

2023

EPSTEM

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Road traffic crashes remain a major concern worldwide. They are considered by the World Health Organization as one of the leading causes of death worldwide. To address this road insecurity, many countries are developing national strategies and trying to put in place the necessary action plans for their implementation. At this stage, monitoring of performance is crucial to ensure the efficacy of these road safety systems. The primary objective of this study is to examine the state-of-the-art practices employed in researches for managing road safety systems, specifically performance monitoring, and present the results in an engaging and informative manner. Through a comprehensive review of existing literature, the study seeks to identify essential components to help policymakers develop and monitor the performance of their road safety systems. The findings of this study can serve as a foundation for decision-makers in their efforts to develop and manage effective road safety systems.

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Section: Framework and Flagship Projectsmentioning

confidence: 99%

Section: Identification Of Main Road Safety Measures Usedmentioning

confidence: 99%

Road Safety Performance Monitoring Practices: A Literature Review

EL KHALAI,

CHORFI,

BERRADO

2023

EPSTEM

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“…However, the ongoing progress in autonomous vehicles holds promise for enhancing road safety by reducing reliance on human drivers [ 6 ]. Additionally, intelligent driving behavior monitoring systems, equipped with real-time interventions, have shown remarkable effectiveness in enhancing road safety [ 7 ]. By combining the benefits of autonomous vehicles and intelligent monitoring systems, there is a strong potential for mitigating the impact of human error and creating a safer road environment for all road users.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies have focused on understanding the impact of various factors on unsafe driving and have sought to develop suitable models for identifying risky driving behavior and establishing intervention frameworks within vehicles. While there have been proposals for various interventions during and post-trip [ 8 , 9 ], the personalization of these interventions and a direct connection between real-time driving behavior and intervention activation remain areas for improvement.…”

Section: Introductionmentioning

confidence: 99%

Examination of the Effect of Task Complexity and Coping Capacity on Driving Risk: A Cross-Country and Transportation Mode Comparative Study

Roussou,

Garefalakis,

Michelaraki

et al. 2023

Sensors

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The i-DREAMS project established a ‘Safety Tolerance Zone (STZ)’ to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables “task complexity” and “coping capacity” that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver’s coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.

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“…Several published studies focus on understanding the effect of different characteristics on dangerous driving to develop the right models for recognizing risky driving behavior while setting the framework for in-vehicle interventions. Although a variety of in-vehicle and post-trip interventions have been proposed [ 6 , 7 ], there is a lack of intervention personalization and a direct link between real-time driving behavior and the triggering of interventions. In recent years, driving behavior analysis by utilizing machine learning techniques has been of high interest to the research community [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

Garefalakis

Katrakazas

Yannis

2022

Sensors

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Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project “i-DREAMS”, which aims at defining, developing, testing and validating a ‘Safety Tolerance Zone’ (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.

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Post-trip safety interventions: State-of-the-art, challenges, and practical implications

Cited by 15 publications

References 56 publications

Road Safety Performance Monitoring Practices: A Literature Review

Road Safety Performance Monitoring Practices: A Literature Review

Examination of the Effect of Task Complexity and Coping Capacity on Driving Risk: A Cross-Country and Transportation Mode Comparative Study

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

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