A systematic review and meta-analysis of the impact of curbs on crash outcomes

Road traffic crashes involving vertical curbs are commonly reported to occur on highways and expressways in India. We found a gap in terms of systematically assessing the evidence of the impact of curbs on road safety outcomes in the real world. We conducted a systematic review and meta-analysis of the impact of curbs on the risk of road traffic injuries. We used keywords in a database of records prepared by an earlier evidence gap map (EGM). The EGM used a comprehensive search strategy including 6 academic database, 17 organizational websites, hand searching, contacting experts and back referencing. We found 4 studies that evaluated impact of a curbed median or a curbed shoulder. We found that the presence of a curb on a median increases the risk for all crashes, all single-vehicle crashes, all median-related crashes and median-related injury crashes. The data also indicate that the severity of accidents reduces for curbs on median while it increases for curbs on shoulder, though the latter effect is not statistically significant. All the epidemiological studies were conducted on rural highways and did not report effects for different traffic speeds or vehicle types. However, our review of crash tests and simulation studies indicates that the impact of a curb design may be highly sensitive to speed and vehicle types. The safety impacts of a curb depend on the context of the road. In an urban road, a curb should ensure safety of pedestrians from an errant vehicle. On high-speed rural roads, curbs should be avoided and treatments should facilitate safe departure of the vehicle from the roadway.</p

Developing a national database of police-reported fatal road traffic crashes for road safety research and management in India

Strengthening crash surveillance is an urgent priority for road safety in low- and middle-income countries. We reviewed the online availability and completeness of First Information Reports (FIRs; police reports) of road traffic crashes in India. We developed a relational database to record information extracted from FIRs, and implemented it for one state (Chhattisgarh, 2017–2019). We found that FIRs can be downloaded in bulk from government websites of 15 states and union territories. Another 14 provide access online but restrict bulk downloading, and 7 do not provide online access. For Chhattisgarh, 87% of registered FIRs could be downloaded. Most FIRs reported the date, time, collision-type, and vehicle-types, but important crash characteristics (e.g. infrastructure attributes) were missing. India needs to invest in building the crash surveillance capacity for research and safety management. However, in the interim, maintaining a national database of a sample of FIRs can provide useful policy guidance.</p

Known Data Sources that Could Contribute to the GBD Injuries Estimates If Data Access Were Available

<p>This continuing environmental scan can be tracked on the expert group Web site [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000001#pmed-1000001-b003" target="_blank">3</a>].</p

Comparison of GSV, Census and APS estimates of gender split of cyclists.

Comparison of GSV, Census and APS estimates of gender split of cyclists.</p

Descriptive statistics.

<p>Descriptive statistics.</p

Observed and predicted mode shares using LOOCV with mean absolute error (MAE) and median absolute error (MDAE) for cycling measures (a: Model 1; b: Model 2; c: Model 3; d: Model 4).

<p>Observed and predicted mode shares using LOOCV with mean absolute error (MAE) and median absolute error (MDAE) for cycling measures (a: Model 1; b: Model 2; c: Model 3; d: Model 4).</p

Regression models.

<p>Regression models.</p

Linear relationships of GSV observations with commute share and prevalence of walking and cycling (3(a) and 3(b) include R² using log transformed variables in parentheses to reduce the effect of outliers).

Linear relationships of GSV observations with commute share and prevalence of walking and cycling (3(a) and 3(b) include R2 using log transformed variables in parentheses to reduce the effect of outliers).</p

Observed and predicted average number of days using LOOCV with mean absolute error (MAE) and median absolute error (MDAE) for cycling measure (a: Model 9; b: Model 10).

<p>Observed and predicted average number of days using LOOCV with mean absolute error (MAE) and median absolute error (MDAE) for cycling measure (a: Model 9; b: Model 10).</p

Snapshot of the web-based questionnaire (image is illustrative and not from GSV to comply with copyright issues).

<p>Snapshot of the web-based questionnaire (image is illustrative and not from GSV to comply with copyright issues).</p