Use of codes data to improve estimates of at-fault risk for elderly drivers

Sagar, Shraddha; Stamatiadis, Nikiforos; Wright, Samantha; Green, Eric

doi:10.1016/j.aap.2020.105637

Cited by 16 publications

(15 citation statements)

References 12 publications

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“…Computer science and advanced estimation techniques enable us to predict and optimize several of our procedures (Aggarwal et al 2020 ; Bhardwaj et al 2020 ; Sagar et al 2020 ). However, such advances in modeling procedures may sometimes have the opposite than the desired outcome in the social sciences.…”

Section: The Motivation For This Studymentioning

confidence: 99%

Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

Vasileiou

2021

Comput Econ

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Forecasting accurate Value-at-Risk (VaR) estimations is a crucial task in applied financial risk management. Even though there have been significant advances in the field of financial econometrics, many crises have been documented throughout the world in the last decades. An explanation for this discrepancy is that many contemporary models are too complex and cannot be easily understood and implemented in the financial industry (Fama in Financ Anal J 51:75–80, 1995; Ross in AIMR conference proceedings, vol. 1993, no. 6, pp. 11–15, Association for Investment Management and Research, 1993). In order to bridge this theory–practice gap, we present a computational method based on the leverage effect. This method allows us to focus on financial theory and remove complexity. Examining the US stock market (2000–2020), we provide empirical evidence that our newly suggested approach, which uses only the most appropriate observation period, significantly increases the accuracy of the Conventional Delta Normal VaR model and generates VaR estimations which are as accurate as those of advanced econometric models, such as GARCH(1,1).

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Section: The Motivation For This Studymentioning

confidence: 99%

Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

Vasileiou

2021

Comput Econ

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“…Therefore, these variables should perhaps not be taken into account in the liability assignment process. However, disability is mainly related with the age of the drivers [39] and elderly drivers are more likely than younger ones to be at-fault in a crash [15]. Thus it could be useful to include this variable in order to obtain more information about driver behavior patterns.…”

Section: Most Influential Variables On Driver Liabilitymentioning

confidence: 99%

“…However, to assess the risk levels of the different driver groups, it is necessary to count on some measure of the exposure levels of the driver groups or vehicles which are being analyzed, given that vehicle collision rates are defined as the ratio between the number of collisions in the group and their exposure [1,4,14]. The problem here lies in the exposure term, which is not available in most databases and its determination is not an easy task, particularly in the more specific (restricted) groups of drivers or under several risk combinations [2,15,16], so this constitutes one of the most important issues for vehicle collision analyses. This occurs especially in countries like Spain, where the surveys for determining the exposure of the different driver groups are not carried out routinely [1,9,12,14].…”

Section: Introductionmentioning

confidence: 99%

“…The quasi-induced exposure method is selected here to estimate the relative exposure of the drivers studied and also the crash-causing propensity by means the relative accident involvement ratio (RAIR), which is calculated by taking the ratio of the number of at-fault drivers in a specific subgroup to that of the not-at-fault drivers in the same subgroup [15,17,18]. The quasi-induced exposure method uses the vehicle collision data on the basis of several hypotheses [1,4,[8][9][10]12,13,17,19,20].…”

Section: Introductionmentioning

confidence: 99%

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Driver Liability Assessment in Vehicle Collisions in Spain

Sanjurjo-de-No

Arenas-Ramírez

Mira

et al. 2021

IJERPH

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An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.

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“…Despite their importance, the exposure data are far from perfect due to the difficulties in acquisition and availability, particularly in the more specific (restricted) groups of drivers or with several risk combinations [10,11]. This is especially an issue in countries such as Spain, where the collection of the necessary information (for example, mobility surveys) for the direct determination of exposure is not carried out on a regular basis.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain

Narváez-Villa

Arenas-Ramírez

Mira

et al. 2021

IJERPH

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Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.

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Use of codes data to improve estimates of at-fault risk for elderly drivers

Cited by 16 publications

References 12 publications

Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

Driver Liability Assessment in Vehicle Collisions in Spain

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain

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