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
1. Li H, Graham D, Majumdar A. The impacts of speed cameras on road accidents: an application of propensity score matching methods. Accident Analysis & Prevention. 2013;60:148–157. doi: 10.1016/j.aap.2013.08.003
2. Christie S, Lyons R, Dunstan F, Jones S. Are mobile speed cameras effective? A controlled before and after study. Injury Prevention. 2003;9:302–306. doi: 10.1136/ip.9.4.302 14693888
3. Cunningham C, Hummer J, Moon J. Analysis of Automated Speed Enforcement Cameras in Charlotte, North Carolina. Transportation Research Record. 2000;2078:127–134. doi: 10.3141/2078-17
4. De Pauw E, Daniels S, Brijs T, Hermans E, Wets G. An evaluation of the traffic safety effect of fixed speed cameras. Safety Science. 2014;62:168–174. doi: 10.1016/j.ssci.2013.07.028
5. Gains A, Heydecker B, Shrewsbury J, Robertson S. The national safety camera programme 3-year evaluation report. UK Department for Transport; 2004.
6. Gains A, Heydecker B, Shrewsbury J, Robertson S. The national safety camera programme 4-year evaluation report. UK Department for Transport; 2005.
7. Goldenbeld C, van Schagen I. The effects of speed enforcement with mobile radar on speed and accidents. An evaluation study on rural roads in the Dutch province Friesland. Accident Analysis & Prevention. 2005;37:1135–1144. doi: 10.1016/j.aap.2005.06.011
8. Jones AP, Sauerzapf V, Haynes R. The effects of mobile speed camera introduction on road traffic crashes and casualties in a rural county of England. Journal of Safety Research. 2008;39:101–110. doi: 10.1016/j.jsr.2007.10.011 18325421
9. Maher M. A note on the modelling of TfL fixed speed camera data. University College London; 2015.
10. Hauer E, Harwood DW, Council FM, Griffith MS. Estimating safety by the empirical Bayes method: a tutorial. Transportation Research Record. 2002;1784:126–131. doi: 10.3141/1784-16
11. Chen G, Meckle W, Wilson J. Speed and safety effect of photo radar enforcement on a highway corridor in British Columbia. Accident Analysis & Prevention. 2002;34:129–138. doi: 10.1016/S0001-4575(01)00006-9
12. Elvik R. Effects on accidents of automatic speed enforcement in Norway. Transportation Research Record. 1997;1595:14–19. doi: 10.3141/1595-03
13. Hoye A. Safety effects of fixed speed cameras—an empirical Bayes evaluation. Accident Analysis & Prevention. 2015;82:263–269. doi: 10.1016/j.aap.2015.06.001
14. Mountain LJ, Hirst WM, Mahar MJ. Costing lives or saving lives: a detailed evaluation of the impact of speed cameras. Traffic Engineering & Control. 2004;45:280–287.
15. Mountain LJ, Hirst WM, Mahar MJ. Are speed enforcement cameras more effective than other speed management measures? The impact of speed management schemes on 30 mph roads. Accident Analysis & Prevention. 2005;37:742–754. doi: 10.1016/j.aap.2005.03.017
16. Shin K, Washington SP, van Schalkwyk I. Evaluation of the Scottsdale Loop 101 automated speed enforcement demonstration program. Accident Analysis & Prevention. 2009;41:393–403. doi: 10.1016/j.aap.2008.12.011
17. Carnis L, Blais E. An assessment of the safety effects of the French speed camera program. Accident Analysis & Prevention. 2013;51:301–309. doi: 10.1016/j.aap.2012.11.022
18. Hess S, Polak J. Effects of speed limit enforcement cameras on accident rates. Transportation Research Record. 2003;1830:25–34. doi: 10.3141/1830-04
19. Keall MD, Povey LJ, Frith WJ. The relative effectiveness of a hidden versus a visible speed camera programme. Accident Analysis & Prevention. 2001;33:277–284.
20. Robins JM. Robust estimation in sequentially ignorable missing data and causal inference models. In: Proceedings of the American Statistical Association, Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association; 2000. p. 6–10.
21. Robins JM, Rotnitzky A, van der Laan MJ. Comment on the Murphy and Van der Vaart article “On profile likelihood”. Journal of the American Statistical Association. 2000;95:431–435.
22. Robins JM, Rotnitzky A. Comment on “Inference for semiparametric models: some questions and an answer”. Statistical sinica. 2001;11:920–936.
23. van der Laan M, Robins JM. Unified methods for censored longitudinal data and causality. Berlin: Springer; 2003.
24. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine. 2004;23:2937–2960. doi: 10.1002/sim.1903 15351954
25. Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61:962–972. doi: 10.1111/j.1541-0420.2005.00377.x 16401269
26. Kang JDY, Schafer JL. Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. Statistical Science. 2007;22(4):523–539. doi: 10.1214/07-STS227
27. DfT. Reported road casualties in Great Britain: quarterly provisional estimates. London: UK Department for Transport; 2017.
28. DfT. Transport Statistics Great Britain: 2016. London: UK Department for Transport; 2016.
29. Rubin DB. Bayesian inference for causal effects: the role of randomization. Annals of Statistics. 1978;6(1):34–58. doi: 10.1214/aos/1176344064
30. Rubin DB. Comment on ‘Randomization analysis of experimental data in the Fisher randomization test’ by Basu. Journal of the American Statistical Association. 1980;75(371):591–593.
31. Rubin DB. Comment: which ifs have causal answers? Journal of the American Statistical Association. 1986;81(396):961–962.
32. Rubin DB. Neyman (1923) and causal inference in experiments and observational studies. Statistical Science. 1990;5(4):472–480. doi: 10.1214/ss/1177012032
33. Tsiatis AA, Davidian M. Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. Statistical Science. 2007;22(4):569–573. doi: 10.1214/07-STS227 18516239
34. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association. 1952;47:663–685. doi: 10.1080/01621459.1952.10483446
35. Graham DJ, McCoy EJ, Stephens DA. Approximate Bayesian Inference for Doubly Robust Estimation. Bayesian Anal. 2016;11(1):47–69. doi: 10.1214/14-BA928
36. Rubin DB. The Bayesian Bootstrap. The Annals of Statistics. 1981;9(1):130–134. doi: 10.1214/aos/1176345338
37. Newton MA, Raftery AE. Approximate Bayesian Inference with the Weighted Likelihood Bootstrap (with discussion). Journal of the Royal Statistical Society Series B (Methodological). 1994;56(1):pp. 3–48.
38. Muliere P, Secchi P. Bayesian nonparametric predictive inference and bootstrap techniques. Annals of the Institute of Statistical Mathematics. 1996;48(4):663–673. doi: 10.1007/BF00052326
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- Jak a kdy u celiakie začíná reakce na lepek? Možnou odpověď poodkryla čerstvá kanadská studie
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- Spermie, vajíčka a mozky – „jednohubky“ z výzkumu 2024/38
- Infekce se v Americe po příjezdu Kolumba šířily nesrovnatelně déle, než se traduje
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy