Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression
Authors
Keywords
-
Journal
Transportmetrica A-Transport Science
Volume 15, Issue 2, Pages 1867-1884
Publisher
Informa UK Limited
Online
2019-08-12
DOI
10.1080/23249935.2019.1652867
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Unbiased Estimation Methods of Nonlinear Transport Models Based on Linearly Projected Data
- (2019) Wai Wong et al. TRANSPORTATION SCIENCE
- Comparative evaluation of temporal correlation treatment in crash frequency modelling
- (2018) Wen Cheng et al. Transportmetrica A-Transport Science
- The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach
- (2018) Huiying Wen et al. JOURNAL OF ADVANCED TRANSPORTATION
- Bootstrap standard error estimations of nonlinear transport models based on linearly projected data
- (2018) Wai Wong et al. Transportmetrica A-Transport Science
- Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation
- (2017) Qiang Zeng et al. JOURNAL OF ADVANCED TRANSPORTATION
- Incorporating temporal correlation into a multivariate random parameters Tobit model for modeling crash rate by injury severity
- (2017) Qiang Zeng et al. Transportmetrica A-Transport Science
- Changes in novice motorcyclist safety in Hong Kong after the probationary driving license scheme
- (2017) Connor Y. H. Wu et al. Transportmetrica A-Transport Science
- Evaluation of the impact of traffic incidents using GPS data
- (2016) Wai Wong et al. PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT
- Biased standard error estimations in transport model calibration due to heteroscedasticity arising from the variability of linear data projection
- (2016) Wai Wong et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Analyzing the effectiveness of implemented highway safety laws for traffic safety across U.S. states
- (2016) Chunjiao Dong et al. Transportmetrica A-Transport Science
- Predicting crash frequency using an optimised radial basis function neural network model
- (2016) Helai Huang et al. Transportmetrica A-Transport Science
- Analyzing injury crashes using random-parameter bivariate regression models
- (2016) Chunjiao Dong et al. Transportmetrica A-Transport Science
- Systematic bias in transport model calibration arising from the variability of linear data projection
- (2015) Wai Wong et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Role of street patterns in zone-based traffic safety analysis
- (2015) Qiang Guo et al. Journal of Central South University
- Geographical unit based analysis in the context of transportation safety planning
- (2013) Mohamed Abdel-Aty et al. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
- A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections
- (2011) Marisol Castro et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK
- (2011) Chao Wang et al. Transportmetrica A-Transport Science
- Empirical Evaluation of Alternative Approaches in Identifying Crash Hot Spots
- (2009) Helai Huang et al. TRANSPORTATION RESEARCH RECORD
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now