SUNY at Buffalo
Published in: Analytic Methods in Accident Research
Presented at: TRB Annual Meeting 2022 (Best Paper Award TRB AED60 Committee on Statistical Methods)
A temporal instability analysis of environmental factors affecting accident occurrences during snow events: The random parameters hazard-based duration model with means and variances heterogeneity
The present paper introduces the time between the start of a snowfall and the occurrence of a motor vehicle accident as a novel measure for evaluating motor vehicle safety during snowfalls. Detailed information of accidents that occurred during snowfalls between 2017 and 2020 in the state of New York are used to explore the accelerating or delaying effect of different factors on the time between the start of a snowfall and the occurrence of an accident. The temporal stability of the factors across the study period is investigated. The findings from this paper are anticipated to offer insights to winter maintenance teams, transportation system operators, and users regarding accident-prone periods and locations during snowfalls.
Published in: Behaviour & Information Technology
Evaluating the Cognitive and Psychological Effects of Real-Time Auditory Travel Information on Driver using EEG
Real-time travel information design with inadequate consideration of human factors can lead to driver distraction and diminish road safety. This study measures drivers’ brain electrical activity patterns to evaluate multiple aspects of driver cognition and psychology under real-time information provision using insights from the neuroscience domain on the localisation of brain functions. The impacts of real-time auditory travel information characteristics (amount, sufficiency, and content) and different time stages of interaction with information provision (before, during, and after) on the frequency band powers of electroencephalogram signals in different brain regions are analyzed using linear mixed models. The study findings can aid information providers, both private and public, as well as auto manufacturers to incorporate driver cognition in designing safer real-time information and their delivery systems.
Published in: Journal Maritime Economics & Logistics (2018 Editor’s Choice Award)
A Binary Probit Model to Analyze Freight Transportation Decision-Maker Perspectives for Container Shipping on the Northern Sea Route
The predicted decrease of ice presence in the Arctic Ocean may allow commercial container shipping to use the Northern Sea Route (NSR) throughout the year starting by 2050. This paper conducts a stated preference survey of freight transportation decision-makers in East Asia and Europe to understand their perspectives towards the use of the NSR to ship cargo. A binary probit model is used to investigate the correlation between the operational and behavioral characteristics of freight transportation decision-makers and their attitudes towards maritime freight carriers operating through the NSR.