Factors affecting the injury severity of out-of-control single-vehicle crashes in Singapore.

Single-vehicle (SV) crashes are of major concerns because of their high fatality rates. To understand the proneness of high injury severity for vehicle operators brought about by SV crashes without the confounding influence of other road users, this study focuses on those SV crashes without colliding with pedestrians, which are defined as out-of-control SV crashes given the general consequence of involved vehicles. Moreover, to compare the influence of contributory factors (including driver-vehicle/rider-vehicle, roadway, and environmental characteristics) by vehicle types, the injury severity for riders of motorized two-wheelers and drivers of other motorized vehicles are investigated separately using two disaggregated ordered probit models. The results show that for both riders and drivers, variables such as age (65 and above), drink driving, error type of failing to have proper control, driving maneuvers of left and right turns as well as driving after midnight are associated with more severe injuries whereas factors such as wet, oily or sandy surfaces are related to less severe injury. Four other variables, i.e., foreign vehicle registration, probation or expired license, high speed-limit roads, and type of median lane, have different influences on riders and drivers on injury severity. Additionally, factors such as road traffic type and nationality are only found to significantly influence only riders and drivers respectively. The results shed light on both the similar and different causes of high injury severity for riders and drivers involved in out-of-control SV crashes. Based on the findings, targeted countermeasures may be introduced from multiple perspectives such as driver education and policy development to improve non-traffic-interactive safety.

A study of at-fault older drivers in light-vehicle crashes in Singapore.

A number of studies on motor vehicle crashes have suggested that older drivers are more likely to be at-fault compared to younger drivers. The objective of this paper is to identify factors that contribute to older drivers (aged 65 and above) being at fault in light vehicle crashes in Singapore. Based on 3 years of crash data, the calibrated binary logit model shows that older drivers are more likely to be at fault during peak periods and festive seasons between November to February, as well as at gore areas of expressways, intersections. Curb lanes of multi-lane roads and single-lane roads are also found to increase the odds of older drivers being at fault. Furthermore, older drivers appear to have more problems on roads with wet surfaces and speed limits of 60 km/h and 70 km/h. In the light of an aging population in Singapore, it is imperative that more targeted countermeasures be taken from multiple perspectives to lower such risks.

Buckling up in Singapore: residency and other risk factors for seatbelt non-compliance - a cross-sectional study based on trauma registry data.

BACKGROUND: Seatbelt non-compliance is a problem in middle income countries, and little is known about seatbelt compliance in populations with a high proportion of non-residents. This study analyses the profile of seatbelt non-compliance in Singapore based on trauma registry data from five of the six public hospitals. METHODS: This is a cross-sectional study of seatbelt compliance of patients aged over 18 years, attending the emergency departments of five public hospitals in Singapore after road collisions from 2011-2014. Seatbelt data was obtained from paramedic and patient history. RESULTS: There were 4,576 patients studied. Most were Singapore citizens (83.4 %) or permanent residents (2.4 %), with the largest non-resident groups from Malaysia, India, and China. Overall seatbelt compliance was 82.1 %. On univariate analysis, seatbelt compliance was higher in older patients (OR 1.02, 95 % CI 1.001-1.021, p < 0.0001); drivers, followed by front passengers (OR 0.65, 95 % CI 0.51-0.83, p < 0.0001), were more compliant than rear passengers (OR 0.08, 0.06-0.09, p < 0.0001); occupants of larger vehicle types (buses, heavy transport vehicles, minibuses and vans) were more non-compliant compared to occupants of private cars and taxis. Morning peak travel (0700 h-0900 h) and being a non-resident were other risk factors for non-compliance. On multivariable analysis, older age (OR 1.01, 95 % CI 1.001-1.014, p = 0.03) was associated with compliance, while non-residents from China (OR 0.43, 95 % CI 0.18-0.99, p = 0.05), seat position (front passenger compared to driver, OR 0.64, 95 % CI 0.48-0.85, p = 0.002; rear passenger compared to driver, OR 0.067, 95 % CI 0.05-0.09, p < 0.0001), vehicle type (bus compared to car, OR 0.04, 95 % CI 0.017-0.11, p < 0.0001, van compared to car, OR 0.55, 95 % CI 0.36-0.83, p = 0.004), and travel at morning peak periods were independent predictors of seatbelt non-compliance. When the sub-group of drivers was analysed, only vehicle type was a significant predictor of seatbelt compliance, with bus drivers least likely to be compliant to seatbelts (multivariable analysis, OR 0.057 compared to cars, 95 % CI 0.019-0.18, p < 0.0001). CONCLUSIONS: While overall seatbelt compliance in our study is high, efforts can be made to increase compliance for morning rush hour passengers, rear seat passengers, and occupants of buses, heavy transport vehicles, and vans or pickups.

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections.

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag-1 dependence specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

Modeling fault among motorcyclists involved in crashes.

Singapore crash statistics from 2001 to 2006 show that the motorcyclist fatality and injury rates per registered vehicle are higher than those of other motor vehicles by 13 and 7 times, respectively. The crash involvement rate of motorcyclists as victims of other road users is also about 43%. The objective of this study is to identify the factors that contribute to the fault of motorcyclists involved in crashes. This is done by using the binary logit model to differentiate between at-fault and not-at-fault cases and the analysis is further categorized by the location of the crashes, i.e., at intersections, on expressways and at non-intersections. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Time trend effect shows that not-at-fault crash involvement of motorcyclists has increased with time. The likelihood of night time crashes has also increased for not-at-fault crashes at intersections and expressways. The presence of surveillance cameras is effective in reducing not-at-fault crashes at intersections. Wet-road surfaces increase at-fault crash involvement at non-intersections. At intersections, not-at-fault crash involvement is more likely on single-lane roads or on median lane of multi-lane roads, while on expressways at-fault crash involvement is more likely on the median lane. Roads with higher speed limit have higher at-fault crash involvement and this is also true on expressways. Motorcycles with pillion passengers or with higher engine capacity have higher likelihood of being at-fault in crashes on expressways. Motorcyclists are more likely to be at-fault in collisions involving pedestrians and this effect is higher at night. In multi-vehicle crashes, motorcyclists are more likely to be victims than at-fault. Young and older riders are more likely to be at-fault in crashes than middle-aged group of riders. The findings of this study will help to develop more targeted countermeasures to improve motorcycle safety and more cost-effective safety awareness program in motorcyclist training.

A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes.

Leaving the scene of a crash without reporting it is an offence in most countries and many studies have been devoted to improving ways to identify hit-and-run vehicles and the drivers involved. However, relatively few studies have been conducted on identifying factors that contribute to the decision to run after the crash. This study identifies the factors that are associated with the likelihood of hit-and-run crashes including driver characteristics, vehicle types, crash characteristics, roadway features and environmental characteristics. Using a logistic regression model to delineate hit-and-run crashes from nonhit-and-run crashes, this study found that drivers were more likely to run when crashes occurred at night, on a bridge and flyover, bend, straight road and near shop houses; involved two vehicles, two-wheel vehicles and vehicles from neighboring countries; and when the driver was a male, minority, and aged between 45 and 69. On the other hand, collisions involving right turn and U-turn maneuvers, and occurring on undivided roads were less likely to be hit-and-run crashes.

Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis.

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using intra-class correlation coefficient (ICC) and deviance information criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time and in good street-lighting condition as well as those involving pedestrian injuries tend to be less severe. But crashes that occur in night time, at T/Y type intersections, and on right-most lane, as well as those that occur in intersections where red light cameras are installed tend to be more severe. Moreover, heavy vehicles have a better resistance on severe crash and thus induce less severe injuries, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections.

Poisson and negative binomial (NB) models have been used to analyze traffic accident occurrence at intersections for several years. There are however, limitations in the use of such models. The Poisson model requires the variance-to-mean ratio of the accident data to be about 1. Both the Poisson and the NB models require the accident data to be uncorrelated in time. Due to unobserved heterogeneity and serial correlation in the accident data, both models seem to be inappropriate. A more suitable alternative is the random effect negative binomial (RENB) model, which by treating the data in a time-series cross-section panel, will be able to deal with the spatial and temporal effects in the data. This paper describes the use of RENB model to identify the elements that affect intersection safety. To establish the suitability of the model, several goodness-of-fit statistics are used. The model is then applied to investigate the relationship between accident occurrence and the geometric, traffic and control characteristics of signalized intersections in Singapore. The results showed that 11 variables significantly affected the safety at the intersections. The total approach volumes, the numbers of phases per cycle, the uncontrolled left-turn lane and the presence of a surveillance camera are among the variables that are the highly significant.

An analysis of motorcycle injury and vehicle damage severity using ordered probit models.

PROBLEM: Motorcycles constitute about 19% of all motorized vehicles in Singapore and are generally overrepresented in traffic accidents, accounting for 40% of total fatalities. METHOD: In this paper, an ordered probit model is used to examine factors that affect the injury severity of motorcycle accidents and the severity of damage to the vehicle for those crashes. Nine years of motorcycle accident data were obtained for Singapore through police reports. These data included categorical assessments of the severity of accidents based on three levels. Damage severity to the vehicle was also assessed and categorized into four levels. Categorical data of this type are best analyzed using ordered probit models because they require no assumptions regarding the ordinality of the dependent variable, which in this case is the severity score. Various models are examined to determine what factors are related to increased injury and damage severity of motorcycle accidents. RESULTS: Factors found to lead to increases in the probability of severe injuries include the motorcyclist having non-Singaporean nationality, increased engine capacity, headlight not turned on during daytime, collisions with pedestrians and stationary objects, driving during early morning hours, having a pillion passenger, and when the motorcyclist is determined to be at fault for the accident. Factors leading to increased probability of vehicle damage include some similar factors but also show some differences, such as less damage associated with pedestrian collisions and with female drivers. In addition, it was also found that both injury severity and vehicle damage severity levels are decreasing over time.