Do speed cameras reduce road traffic collisions?
Autoři:
Daniel J. Graham aff001; Cian Naik aff002; Emma J. McCoy aff001; Haojie Li aff003
Působiště autorů:
Imperial College London, London, United Kingdom
aff001; University of Oxford, Oxford, United Kingdom
aff002; Southeast University, Nanjing, China
aff003
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0221267
Souhrn
This paper quantifies the effect of speed cameras on road traffic collisions using an approximate Bayesian doubly-robust (DR) causal inference estimation method. Previous empirical work on this topic, which shows a diverse range of estimated effects, is based largely on outcome regression (OR) models using the Empirical Bayes approach or on simple before and after comparisons. Issues of causality and confounding have received little formal attention. A causal DR approach combines propensity score (PS) and OR models to give an average treatment effect (ATE) estimator that is consistent and asymptotically normal under correct specification of either of the two component models. We develop this approach within a novel approximate Bayesian framework to derive posterior predictive distributions for the ATE of speed cameras on road traffic collisions. Our results for England indicate significant reductions in the number of collisions at speed cameras sites (mean ATE = -15%). Our proposed method offers a promising approach for evaluation of transport safety interventions.
Klíčová slova:
Engineering and technology – Civil engineering – Transportation infrastructure – Roads – Transportation – Medicine and health sciences – Epidemiology – Medical risk factors – Traumatic injury risk factors – Road traffic collisions – Public and occupational health – Safety – Traffic safety – Physical sciences – Mathematics – Probability theory – Random variables – Research and analysis methods – Simulation and modeling – Computer and information sciences – Earth sciences – Geography – Geoinformatics – Geographic information systems – Social sciences – Economics – Economic models – People and places – Geographical locations – Europe – European Union – United Kingdom
Zdroje
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Článek vyšel v časopise
PLOS One
2019 Číslo 9
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