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.
Kwon, T. J., and Fu, L. (2015). RWIS Network Planning: Optimal Density and Location, Final report submitted to Aurora Program (http://www.aurora-program.org), Aurora Project 2010-04, Jan 2016.
Fu, L., Thakali, L., Linkton, M., Kwon, T. J., Usman, T. (2015) Cost-benefit analysis of winter road maintenance standards – a case study. Proceedings of the 2015 CSCE Annual Conference, May 27-30, 2015. Regina, SK. Abstract: This paper describes the result of a study aiming at illustrating how models of winter road maintenance (WRM) performance measures can be applied to investigate the implications of different winter road maintenance level of …Read More
Hosseini, F., Hossain, S. M., Fu, L., Johnson, M., Fei, Y. (2015). Prediction of Pavement Surface Temperature Using Meteorological Data for Optimal Winter Operations in Parking Lots. ASCE Cold Regions Engineering
Hosseini, F., Hossain, S. M., Fu, L., San Gabriel, P., & Van Seters, T. (2015). Field Evaluation of Organic Materials for Winter Snow and Ice Control. In Transportation Research Board 94th Annual Meeting (No. 15-4555).
Muresan, M, Hossain, S. M. K., Fu, L. (2015). A Model-Based Evaluation of an ECO-Driving System Using Connected Vehicle Technologies. 2015 CSCE Annual Conference. Abstract: Connected vehicle technologies are often touted for their potential applications to vehicle safety. However, these technologies also have the potential to contribute to a transportation system’s sustainability. Autonomous and connected vehicles provide traffic engineers with many opportunities for emissions reductions, from vehicle routing to a …Read More
Thakali, L., Kwon, T. J., & Fu, L. (2015). Identification of Crash Hotspots using Kernel Density Estimation and Kriging Methods – A Comparison. Journal of Modern Transportation. DOI: 10.1007/s40534-015-0068-0 Abstract: This paper presents a study aimed at comparing the outcome of two geostatistical based approaches, namely, Kernel Density Estimation (KDE) and Kriging, for identifying crash hotspots in a road network. Aiming at locating high-risk locations for potential intervention, hotspot identification …Read More