Assessing Route Choice to Mitigate Older Driver Risk

Payyanadan, Rashmi P.; Sanchez, Fabrizzio A.; Lee, John D.

doi:10.1109/tits.2016.2582513

Cited by 11 publications

(10 citation statements)

References 24 publications

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“…Navigation systems can be adapted to enable pre-trip planning features suited for older drivers by providing choice of low traffic routes, routes with fewer turns, optimal time of departure to avoid hazardous driving situations such as traffic congestion and poor weather conditions, routes that are familiar, avoid routes with tolls, highways, construction zones, and routes with no GVM restrictions. Payyanadan, Sanchez, and Lee [47] showed that there is opportunity to improve the driving safety outcomes of older drivers in urban and rural settings by providing them with alternate routes that reduce their exposure to left turns, U-turns, construction zones, and lane closures. 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.…”

Section: Can Driver Support Systems Help Address Older Driver Chalmentioning

confidence: 99%

“…This limitation stems from the benefit of social interactions surfacing attitudes, and explanations that individual interviews and observations may not reveal, and so represents a tradeoff associated with methods used to understand driver behavior. Such tradeoffs should be addressed with future work to further our understanding of the challenges of older drivers using naturalistic driving data, which provides an objective representation of the driving challenges faced by older drivers in urban, suburban, and rural settings [47].…”

Section: Can Driver Support Systems Help Address Older Driver Chalmentioning

confidence: 99%

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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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Section: Can Driver Support Systems Help Address Older Driver Chalmentioning

confidence: 99%

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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“…Karim and Sayed [24] established the shortest path model integrating travel time and safety by analyzing the relationship between traffic conflict and collision. Payyanadan et al [25] used the collision accident data of elderly drivers to quantify accident risk influencing factors, such as left turn, U-turn, and travel distance. ey evaluated the safety of the path on this basis to help elderly drivers choose a safer route and reduce their accident risk.…”

Section: Introductionmentioning

confidence: 99%

Driver Route Planning Method Based on Accident Risk Cost Prediction

Liao

Zhou

Wang

et al. 2022

Journal of Advanced Transportation

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The number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method based on accident risk cost prediction for connected and automated vehicles. According to the entropy weight method and an improved algorithm of K shortest paths, a route planning model with accident risk as the main optimization objective was established. Firstly, an accident risk evaluation system was built based on traffic accident data, and a quantitative prediction model of accident risk cost based on driver-, vehicle-, road-, and environment-related factors was constructed. Secondly, the entropy weight method was used to calculate the weights of each indicator to determine accident risk considering the aforementioned factors. Then, the route planning model was established, and the solution algorithm based on K shortest paths was designed to solve the optimal route by comprehensively considering accident risk cost and travel time. The accident risk index of each road section in the example road network was assigned, and the risk of the road section was quantified according to the accident risk cost model. Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. After comparison, the optimal path and two alternative paths are obtained. The results show that the accident risk cost prediction model based on historical accident data can be used to quantify driving risk. The proposed method can help drivers in the connected and automated environment choose the optimal travel route with the lowest risk and shortest travel time and improve overall traffic safety and efficiency.

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“…From the perspective of route choice, Payyanadan et al (2017a) [28] developed a route risk measure, and quantified the risk of driving challenges using older driver crash statistics. Results showed that the low-risk alternative reduced risk for 77.7% of the older drivers' trips on average by 61.4%.…”

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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Assessing Route Choice to Mitigate Older Driver Risk

Cited by 11 publications

References 24 publications

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

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

Driver Route Planning Method Based on Accident Risk Cost Prediction

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

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