Red-light cameras for the prevention of road traffic crashes

 

Abstract

Background

Road crashes are a prime cause of death and disability and red-light running is a common cause of crashes at signalised intersections. Red-light cameras are increasingly used to promote compliance with traffic signals. Manual enforcement methods are resource intensive and high risk, whereas red-light cameras can operate 24 hours a day and do not involve high-speed pursuits.

Objectives

To quantify the impact of red-light cameras on the incidence and severity of road crashes and casualties, and the incidence of red-light violations.

Search methods

We searched the following electronic databases: TRANSPORT (NTIS, TRIS, IRRD,TRANSDOC), Cochrane Injuries Group Specialised Register, Cochrane Controlled Trials Register, MEDLINE, EMBASE and the Australian Transport Index. We checked the reference lists of relevant papers and contacted research and advocacy organisations.

Selection criteria

Randomised or quasi-controlled trials and controlled before-after studies of red-light cameras. For crash impact evaluation, the before and after periods each had to be at least one year in length. For violation studies, the after period had to occur at least one year after camera installation.

Data collection and analysis

Two reviewers independently extracted data on study type, characteristics of camera and control areas, and data collection period. Before-after data were collected on number of crashes by severity, collision type, deaths and injuries, and red-light violations. Rate ratio was calculated for each study. Where there was more than one, rate ratios were pooled to give an overall estimate, using a generic inverse variance method and a random-effects model.

Main results

No randomised controlled trials were identified but 10 controlled before-after studies from Australia, Singapore and the USA met our inclusion criteria. We grouped them according to the extent to which they adjusted for regression to the mean (RTM) and spillover effects. Total casualty crashes: the only study that adjusted for both reported a rate ratio of 0.71 (95% CI to 0.55, 0.93); for three that partially adjusted for RTM but failed to consider spillover, rate ratio was 0.87 (95% CI to 0.77, 0.98); one that made no adjustments had a rate ratio of 0.80 (95% CI 0.58 to 1.12). Right-angle casualty crashes: rate ratio for two studies that partially addressed RTM was 0.76 (95% CI 0.54 to 1.07). Total crashes: the study addressing both RTM and spillover reported a rate ratio of 0.93 (95% CI 0.83 to 1.05); one study that partially addressed RTM had a rate ratio of 0.92 (95% CI 0.73 to 1.15); the pooled rate ratio from the five studies with no adjustments was 0.74 (95% CI 0.53 to 1.03). Red-light violations: one study found a rate ratio of 0.53 (95% CI 0.17 to 1.66).

Authors' conclusions

Red-light cameras are effective in reducing total casualty crashes. The evidence is less conclusive on total collisions, specific casualty collision types and violations, where reductions achieved could be explained by the play of chance. Most evaluations did not adjust for RTM or spillover, affecting their accuracy. Larger and better controlled studies are needed.

 

Plain language summary

'Red-light cameras' cut casualty crashes at junctions with traffic lights

Road crashes are a leading cause of death and injury. One common place for these to happen is at junctions (intersections) controlled by traffic signals. 'Red-light cameras' are now widely used to identify drivers that jump ('run') red lights, who can then be prosecuted. This review looked for studies of their effectiveness in reducing the number of times that drivers drive through red lights and the number of crashes. Very little research has been done and much of it has not allowed for the statistical problems that occur when recording this kind of information. However, five studies in Australia, Singapore and the USA all found that use of red-light cameras cut the number of crashes in which there were injuries. In the best conducted of these studies, the reduction was nearly 30%. More research is needed to determine best practice for red-light camera programmes, including how camera sites are selected, signing policies, publicity programmes and penalties.

 

摘要

背景

預防道路交通事故的闖紅燈照相機

道路事故是死亡及失能的主因，且闖紅燈是號誌路口處發生碰撞的常見原因。闖紅燈照相機逐漸被用來推廣遵守交通號誌。人工的執法方式需要密集的資源且具有高風險，然而闖紅燈照相機可以一天24小時運作且與不需要追求高速。

目標

量化闖紅燈照相機對於道路事故與傷亡的發生與嚴重度，以及違法闖紅燈的影響。

搜尋策略

我們檢索以下的電子資料庫：TRANSPORT (NTIS，TRIS，IRRD，TRANSDOC)，Cochrane Injuries Group Specialised Register，Cochrane Controlled Trials Register，MEDLINE，EMBASE及the Australian Transport Index。我們檢閱相關文章的參考文獻，並連絡研究與提倡的組織。

選擇標準

闖紅燈照相機的隨機或類對照試驗及前後對照研究。關於事故影響的評估，前後各期間必須長達至少一年的評估時間。關於違規的研究，照相機安裝後至少一年的期間有發生違規事件。

資料收集與分析

兩名回顧者分別摘錄關於研究類型，照相機特性及對照區域的資料，以及資料收集的時間。蒐集有關前後各種嚴重度，碰撞類型，死亡與傷害，以及闖紅燈事故數量的資料。計算每篇研究的rate ratio。如果有一個以上的rate ratios，則採用通用的倒數變異數方法與隨機效果模式來加總rate ratios，以獲得一個總估計值。

主要結論

沒有找到任何的隨機對照試驗，但有10篇來自澳洲，新加坡與美國的前後對照研究符合我們的納入標準。我們將研究依據調整向平均數迴歸(regression to the mean (RTM))與外溢效果(spillover effect)的程度分組。總傷亡事故：只有一篇研究指出在調整兩者後rate ratio為0.71 (95% CI為0.55至0.93)；而三篇研究在調整RTM，但沒有考量外溢效果之下，rate ratio為0.87 (95% CI為0.77至0.98)；一篇沒有調整過的研究其rate ratio為0.80 (95% CI為0.58至1.12)。直角處的傷亡事故：兩篇採用RTM的研究其rate ratio為0.76 (95% CI為0.54至1.07)。總事故：同時調整RTM與外溢效果的研究其rate ratio為0.93 (95% CI為0.83至1.05)；一篇採用RTM的研究其rate ratio為0.92 (95% CI為0.73至1.15)；五篇沒有調整過的研究其加總rate ratio為0.74 (95% CI為0.53至1.03)。闖紅燈：一篇研究發現rate ratio為0.53 (95% CI為0.17至1.66)。

作者結論

闖紅燈照相機可以有效減少總傷害事故。證據不足以推論總碰撞，特定的傷亡碰撞類型與違規，研究所達到的減少效果可以被解釋為隨機造成的。大部分的評估並未調整RTM或外溢效果，因而影響它們的精確度。需要較大型且良好的對照研究。

翻譯人

本摘要由高雄榮民總醫院金沁琳翻譯。

此翻譯計畫由臺灣國家衛生研究院(National Health Research Institutes, Taiwan)統籌。

總結

闖紅燈照相機可以阻止在交通號誌路口的傷亡事故。交通事故是導致死亡與傷害的主因。這些事故經常發生的地方是由交通號誌控制的路口(十字路口)。“闖紅燈照相機”現在被廣泛地用來辨認穿越(“衝過”)紅燈的駕駛人，這個人可以被起訴。這篇回顧尋找闖紅燈照相機減少駕駛人穿越紅燈的次數以及事故數量的研究。已經完成的研究很少且大部分都有統計上的問題。然而，五篇在澳洲，新加坡與美國的研究皆發現，使用闖紅燈照相機可以阻止傷害事故的數量。這些研究中執行最好的可以減少將近30%。需要更多的研究以確定闖紅燈照相機計畫最好的做法，包括如何選擇照相機的安裝地點，交通號誌政策，宣傳計畫與罰責。