Modeling Multilevel Data in Traffic Safety: A Bayesian Hierarchical Approach

Hoong Chor Chin and Helai Huang
Department of Civil Engineering, National University of Singapore, Crescent, Singapore

Series: Transportation Infrastructure – Roads, Highways, Bridges, Airports and Mass Transit, Systems Engineering Methods, Developments and Technology
BISAC: TEC009140

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Volume 10

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Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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Road safety is a socio-economic concern. With rapid increase in motorization in the last 50 years, road traffic crashes have also become a major global health problem. Worldwide, an estimated 1.2 million people are killed in road crashes each year and as many as 50 million are injured. Without increased efforts and new initiatives to improve road safety, by 2020, the number of road casualties worldwide will increase by some 60% and in low- and middle-income countries by as much as 80%. From an economic perspective, the magnitude of road traffic crashes also places a huge economic burden on society. This book presents a comprehensive literature review on the historical evolution of traffic safety and state of the art crash prediction models. Based on critical review and potential multilevel data structure, development of an innovative Bayesian hierarchical approach in properly modeling the potential heterogeneities due to multilevel crash data structure is discussed. (Imprint: Novinka )

Preface

1. Introduction

2. Review on Crash Prediction Models

3. Multilevel Data Structure in Traffic Safety

4. Bayesian Hierarchical Approach on Multilevel Crash Data

5. Illustrative Examples

6. Conclusion

References

Index

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