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., 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) 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. 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
Several recent transportation safety studies have indicated the importance of accounting for correlated outcomes, for example, among different crash types, including differing injury-severity levels. In this paper, we discuss inference for such data by introducing a flexible Bayesian multivariate model. In particular, we use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. The resulting model collapses into a latent class multivariate model that …Read More
In transportation safety studies, it is often necessary to account for unobserved heterogeneity and multimodality in data. The commonly used standard or over-dispersed generalized linear models (e.g., negative binomial models) do not fully address unobserved heterogeneity, assuming that crash frequencies follow unimodal exponential families of distributions. This paper employs Bayesian nonparametric Dirichlet process mixture models demonstrating some of their major advantages in transportation safety studies. We examine the performance of …Read More
Thakali, L., Fu, L., & Chen, T. (2016). Model Based versus Data-driven Approach for Road Safety Analysis : Does More Data Help? Transportation Research Record: Journal of the Transportation Research Board, No. 2601. Abstract: Crash data for road safety analysis and modeling are growing steadily in size and completeness due to latest advancement in information technologies. This increased availability of large datasets has generated resurgent interest in applying data-driven nonparametric approach as …Read More
Heydari, S., Fu, L., Lord, D., Mallick, B.K. (2016). Multilevel Dirichlet process mixture analysis of railway grade crossing crash data. Analytic Methods in Accident Research, 9, 27-43.