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Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis

Accident analysis and prevention, 2023-03, Vol.181, p.106932-106932, Article 106932

2023

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Exploring the who, what, when, where, and why of automated vehicle disengagements

Accident analysis and prevention, 2020-03, Vol.136, p.105406-105406, Article 105406

2020

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Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity

Accident analysis and prevention, 2017-12, Vol.109, p.132-142

2017

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The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

Accident analysis and prevention, 2019-11, Vol.132, p.105226-105226, Article 105226

2019

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Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Journal of intelligent transportation systems, 2020-10, Vol.25 (2), p.170-187

2020

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Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods

Accident analysis and prevention, 2021-03, Vol.151, p.105949-105949, Article 105949

2021

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Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles

Accident analysis and prevention, 2021-03, Vol.152, p.106006-106006, Article 106006

2021

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How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

Accident analysis and prevention, 2019-06, Vol.127, p.118-133

2019

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Exploring pathways from driving errors and violations to crashes: The role of instability in driving

Accident analysis and prevention, 2023-01, Vol.179, p.106876-106876, Article 106876

2023

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Gate-violation behavior at highway-rail grade crossings and the consequences: Using geo-Spatial modeling integrated with path analysis

Accident analysis and prevention, 2017-12, Vol.109, p.99-112

2017

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Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

Transportation research. Part C, Emerging technologies, 2016-07, Vol.68, p.83-100

2016

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A taxonomy of driving errors and violations: Evidence from the naturalistic driving study

Accident analysis and prevention, 2021-03, Vol.151, p.105873, Article 105873

2021

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Understanding the role of faster emergency medical service response in the survival time of pedestrians

Accident analysis and prevention, 2022-11, Vol.177, p.106829-106829, Article 106829

2022

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Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach

Accident analysis and prevention, 2020-02, Vol.135, p.105354-105354, Article 105354

2020

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Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

Accident analysis and prevention, 2019-11, Vol.132, p.105277-105277, Article 105277

2019

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Heterogeneous ensemble learning for enhanced crash forecasts – A frequentist and machine learning based stacking framework

Journal of safety research, 2023-02, Vol.84, p.418-434

2023

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Driving impairments and duration of distractions: Assessing crash risk by harnessing microscopic naturalistic driving data

Accident analysis and prevention, 2020-10, Vol.146, p.105733-105733, Article 105733

2020

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Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Transportation research. Part C, Emerging technologies, 2017-11, Vol.84, p.48-73

2017

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How is the duration of distraction related to safety–critical events? Harnessing naturalistic driving data to explore the role of driving instability

Journal of safety research, 2023-06, Vol.85 (C), p.15-30

2023

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Crash harm before and during the COVID-19 pandemic: Evidence for spatial heterogeneity in Tennessee

Accident analysis and prevention, 2023-04, Vol.183, p.106988-106988, Article 106988

2023

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Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest

Accident analysis and prevention, 2023-01, Vol.179, p.106880-106880, Article 106880

2023

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How effective are pedestrian crash prevention systems in improving pedestrian safety? Harnessing large-scale experimental data

Accident analysis and prevention, 2022-06, Vol.171, p.106669-106669, Article 106669

2022

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Analyzing drivers’ hazard recognition: Precursors to single-vehicle collisions

Accident analysis and prevention, 2021-09, Vol.160, p.106304-106304, Article 106304

2021

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Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Journal of safety research, 2020-06, Vol.73, p.25-35

2020

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How is driving volatility related to intersection safety? A Bayesian heterogeneity-based analysis of instrumented vehicles data

Transportation research. Part C, Emerging technologies, 2018-07, Vol.92, p.504-524

2018

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Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis

Exploring the who, what, when, where, and why of automated vehicle disengagements

Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity

The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods

Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles

How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

Exploring pathways from driving errors and violations to crashes: The role of instability in driving

Gate-violation behavior at highway-rail grade crossings and the consequences: Using geo-Spatial modeling integrated with path analysis

Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

A taxonomy of driving errors and violations: Evidence from the naturalistic driving study

Understanding the role of faster emergency medical service response in the survival time of pedestrians

Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

Heterogeneous ensemble learning for enhanced crash forecasts – A frequentist and machine learning based stacking framework

Driving impairments and duration of distractions: Assessing crash risk by harnessing microscopic naturalistic driving data

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

How is the duration of distraction related to safety–critical events? Harnessing naturalistic driving data to explore the role of driving instability

Crash harm before and during the COVID-19 pandemic: Evidence for spatial heterogeneity in Tennessee

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest

How effective are pedestrian crash prevention systems in improving pedestrian safety? Harnessing large-scale experimental data

Analyzing drivers’ hazard recognition: Precursors to single-vehicle collisions

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

How is driving volatility related to intersection safety? A Bayesian heterogeneity-based analysis of instrumented vehicles data

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