Using trip diaries to mitigate route risk and risky driving behavior among older drivers

Payyanadan, Rashmi P.; Maus, Adam; Sanchez, Fabrizzio A.; Lee, John D.; Miossi, Lillian; Abera, Amsale; Melvin, Jacob; Wang, Xu‐Fan

doi:10.1016/j.aap.2016.09.023

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…Moreover, a multiobjective genetic search algorithm was designed to solve the model. In [15], risky driver behaviours were examined. The authors of [15] determined through interviews and psychological tests that only self-discipline cannot be relied upon to prevent drivers from selecting high-risk paths.…”

Section: Routing Design and Decision-maker's Risk Aversionmentioning

confidence: 99%

“…In [15], risky driver behaviours were examined. The authors of [15] determined through interviews and psychological tests that only self-discipline cannot be relied upon to prevent drivers from selecting high-risk paths. Safe urban transportation is treated as critical issue to ensure the development of sustainable cities and communities [16][17][18], which has been put forward as one of the sustainable development goals by UN.…”

Section: Routing Design and Decision-maker's Risk Aversionmentioning

confidence: 99%

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Optimal Routing for Safe Construction and Demolition Waste Transportation: A CVaR Criterion and Big Data Analytics Approach

2019

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Rapid urbanisation worldwide, especially in developing countries and areas, has led to the generation of large amounts of construction and demolition waste (C&DW). The resultant transportation demands pose severe threats to safe transportation and secure city operation. By considering the low-probability-high-consequence nature of C&DW traffic accidents and the effectiveness of route optimisation in transportation risk control, a risk-averse project was implemented. Furthermore, an optimal routing model based on the conditional value at risk (CVaR) criterion is proposed. The model considered various risk-averse attitudes of decision-makers. For practicality and for strongly supporting policy-making, big data technology, including the construction of multistructure databases and in-depth analysis, was applied to achieve the proposed CVaR routing model. Therefore, the present study extended the CVaR method to optimal routing design in the field of safe urban C&DW transportation and integrated the optimal model with big data technology.

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Section: Routing Design and Decision-maker's Risk Aversionmentioning

confidence: 99%

Section: Routing Design and Decision-maker's Risk Aversionmentioning

confidence: 99%

Optimal Routing for Safe Construction and Demolition Waste Transportation: A CVaR Criterion and Big Data Analytics Approach

2019

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“…Current DSS are not equipped with retrospective feedback features to provide post-drive feedback that can remind older drivers of the route driven and their driving behavior, challenges experienced along a driven route such as getting lost or missing a turn, and opportunities to improve driving safety outcomes by selecting alternate routes with fewer challenges. Several studies have shown that providing older drivers with retrospective feedback can provide older drivers with safety-critical feedback about their trips, and reduce their risky driving behaviors and route risk [48,49].…”

Section: Can Driver Support Systems Help Address Older Driver Chalmentioning

confidence: 99%

Challenges for Older Drivers in Urban, Suburban, and Rural Settings

2018

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Along with age-related factors, geographical settings—urban, suburban, and rural areas—also contribute to the differences in fatal crashes among older drivers. These differences in crash outcomes might be attributed to the various driving challenges faced by older drivers residing in different locations. To understand these challenges from the perspective of the older driver, a focus group study was conducted with drivers 65 and older from urban, suburban, and rural settings. Guided-group interviews were used to assess driving challenges, mobility options, opportunities for driver support systems (DSS), and alternate transportation needs. Content analysis of the interview responses resulted in four categories representing common challenges faced by older drivers across the settings: behavior of other drivers on the road, placement of road signs, reduced visibility of road signs due to age-related decline, and difficulties using in-vehicle technologies. Six categories involved location-specific challenges such as heavy traffic situations for urban and suburban drivers, and multi-destination trips for rural drivers. Countermeasures implemented by older drivers to address these challenges primarily involved route selection and avoidance. Technological advances of DSS systems provide a unique opportunity to support the information needs for route selection and avoidance preferences of drivers. Using the content analysis results, a framework was built to determine additional and modified DSS features to meet the specific challenges of older drivers in urban, suburban, and rural settings. These findings suggest that there is heterogeneity in the driving challenges and preferences of older drivers based on their location. Consequently, DSS technologies and vehicle automation need to be tailored to not only meet the driving safety and mobility needs of older drivers as a population, but also to their driving environment.

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“…Results showed that the low-risk alternative reduced risk for 77.7% of the older drivers' trips on average by 61.4%. Similarly, Payyanadan et al (2017b) [29] used trip diaries to mitigate route risk and risky driving behavior among older drivers.…”

Section: Introductionmentioning

confidence: 99%

Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis

Xiao

et al. 2021

IEEE Access

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This study aimed to examine the driving actions of at-fault older drivers, and investigate the interrelations between the unobservable factors. To reach the goal, a Bayesian bivariate ordered probit model was proposed, which addressed the driving actions of different drivers simultaneously, and accommodated the interrelations between the unobservables by covariance. The data with 27 arterials from 2014 to 2017 were collected from ArcGIS open data site maintained by Nevada Department of Transportation (NDOT). Compared to individual Bayesian random parameter ordered probit model, the proposed model outperformed according to goodness-of-fit. Results revealed that injury severity and total vehicles were potentially significant factors for actions of at-fault older drivers, while total vehicle and vehicle condition were significant for actions of not-at-fault drivers. The findings can provide potential insights for practitioners to apply the new technology and remind the driving actions of older drivers.

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Using trip diaries to mitigate route risk and risky driving behavior among older drivers

Cited by 15 publications

References 23 publications

Optimal Routing for Safe Construction and Demolition Waste Transportation: A CVaR Criterion and Big Data Analytics Approach

Optimal Routing for Safe Construction and Demolition Waste Transportation: A CVaR Criterion and Big Data Analytics Approach

Challenges for Older Drivers in Urban, Suburban, and Rural Settings

Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis

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