The crash reduction effectiveness of a network-wide traffic police deployment system.
Random Road Watch (RRW) is a traffic policing program in operation in Queensland, Australia. It differs from conventional traffic policing in that an explicit resource management technique is used which randomly schedules low levels of police enforcement in a manner intended to provide long-term, widespread coverage of a road network and hence maximise road safety benefits. Implementation of the program studied in Queensland covered 55% of total crashes within the state. This study aimed to measure the crash effects of the RRW program in Queensland. A quasi-experimental study design was used for the evaluation incorporating Poisson regression statistical analysis techniques. Analysis of the effects of the Queensland RRW program on crash frequency has shown the program to be effective overall. Estimated program effects were largest on fatal crashes, with an estimated reduction of 31%. Estimated aggregate program crash effects reduced with crash severity and increased with time after program introduction. Crash reductions in the third year after program introduction translated into savings, at state level, of some 12% of the state's crashes of all severities and some 15% of the state's fatal road crashes. Overall, the program produced a significant 11% reduction in total crashes in areas outside of metropolitan Brisbane. The opportunity-cost benefit/cost ratio for the program was estimated to be 55:1.
Accident Research Centre, Monash University, Victoria, Australia. stuart.newstead@general.monash.edu.au
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Newstead
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Cameron
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Leggett
D000063:Accidents, Traffic / Q000191:economics / Q000517:prevention & control* / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis; D006801:Humans; D010561:Personnel Staffing and Scheduling; D016012:Poisson Distribution; D016495:Police / Q000458:organization & administration*; D015397:Program Evaluation / Q000379:methods; D011793:Queensland / Q000453:epidemiology; D012044:Regression Analysis