Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates
| Autor: | Tarek Sayed, Shewkar E. Ibrahim |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Computer science Geography Planning and Development Bayesian probability TJ807-830 Management Monitoring Policy and Law TD194-195 Renewable energy sources crime rates Data-driven multivariate traffic safety collision rates 0502 economics and business Econometrics Mobile Automated Enforcement GE1-350 0501 psychology and cognitive sciences Tobit model Duration (project management) Enforcement photo radar 050107 human factors 050210 logistics & transportation Environmental effects of industries and plants Renewable Energy Sustainability and the Environment 05 social sciences random parameter Collision Environmental sciences Software deployment |
| Zdroj: | Sustainability Volume 13 Issue 11 Sustainability, Vol 13, Iss 6422, p 6422 (2021) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su13116422 |
| Popis: | The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is a limited understanding of the impact of Mobile Automated Enforcement (MAE) on crime or the impact of changing a deployment strategy on collision and crime dates. For this reason, this study categorized MAE deployment into three different clusters. A random-parameter multivariate Tobit model was developed under the Bayesian framework to understand the impact of changing the deployment on collision and crime rates in a neighborhood. Firstly, the results of the analysis quantified the high correlation between collision and crime rates (0.86) which suggest that locations with high collision rates also coincide with locations with high crime rates. The results also demonstrated the safety effectiveness (i.e., reduced crime and collision rates) increased for the clusters that are associated with an increased enforcement duration at a neighborhood level. Understanding how changing the deployment strategy at a macro-level affects collision and crime rates provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources. |
| Databáze: | OpenAIRE |
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