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Thakali, L., Fu, L., & Chen, T. (2016). Comparing Crash Prediction Techniques for Ranking of Sites in a Network Screening Process. Proceedings of the 26th Canadian Association of Road Safety Professionals (CARSP) Conference, Halifax, NS, June 5-8, 2016.

Thakali, L., Fu, L., & Chen, T. (2016). Comparing Crash Prediction Techniques for Ranking of Sites in a Network Screening Process. Proceedings of the 26th Canadian Association of Road Safety Professionals (CARSP) Conference, Halifax, NS, June 5-8, 2016. Abstract: Network screening, a process for an effective and efficient management of road safety programs, relies on crash prediction techniques to quantify the relative risks of given sites. The two most commonly used statistical approaches …Read More

Fu, L., Heydari, S. & Thakali, L. (2016). Improvement and Development of Collision Risk and Treatment Effectiveness Models for GradeX. Final Report submitted to Transportation Association of Canada.

Fu, L., Heydari, S. & Thakali, L. (2016). Improvement and Development of Collision Risk and Treatment Effectiveness Models for GradeX. Final Report submitted to Transportation Association of Canada.

Fu, L., Thakali, L., Kwon, T.J., & Usman, T. (2016). Winter Road Condition Classification and Reporting – A Risk Based Approach. Canadian Journal of Civil Engineers.

Fu, L., Thakali, L., Kwon, T.J., & Usman, T. (2016). Winter Road Condition Classification and Reporting – A Risk-Based Approach. Canadian Journal of Civil Engineers. Abstract: This paper presents a risk-based approach for classifying the road surface conditions of a highway network under winter weather events. A relative risk index (RRI) is developed to capture the effect of adverse weather conditions on the collision risk of a highway in reference …Read More

Pan, G., Fu, L., Thakali, L., Muresan, M. (2018).An Improved Deep Belief Network Model for Road Safety Analyses. Proceedings of the Transportation Research Board conference, Jan 2018, US (accepted)

Pan, G., Fu, L., Thakali, L., Muresan, M. (2018).An Improved Deep Belief Network Model for Road Safety Analyses. Proceedings of the Transportation Research Board conference, Jan 2018, US (accepted) Abstract: Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is the application of regression-based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily …Read More

Pan, G., Fu, L., Thakali, L. (2017). Development of a global road safety performance function using deep neural networks. International Journal of Transportation Science & Technology, 6(3):159-173.

Pan, G., Fu, L., Thakali, L. (2017). Development of a global road safety performance function using deep neural networks. Transportation Research Record: International Journal of Transportation Science & Technology, 6(3):159-173. Abstract: This paper explores the idea of applying a machine learning approach to developing a global road safety performance function (SFP) that can be used to predict the expected crash frequencies of different highways from different regions. A deep belief …Read More

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