Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions

Effectiveness of road safety interventions: An evidence and gap map

Background

Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low- and middle-income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high-income countries have successfully reduced RTI by using a public health approach and implementing evidence-based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high-income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.

Objectives

The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.

Search Methods

The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.

Selection Criteria

The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post-crash pre-hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non-fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.

Data Collection and Analysis

The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case-control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.

Main Results

The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post-crash pre-hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non-fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety-six percent of the studies were reported from high-income countries (HIC), 4.5% from upper-middle-income countries, and only 1.4% from lower-middle and low-income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.

Authors' Conclusions

The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence-synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

Travel route safety estimation based on conflict simulation

With the aim of providing travelers information about the safety levels of selectable routes, it is necessary to develop a method that can properly estimate the safety of alternative travel routes. This paper proposes a conflict-based approach for travel route safety estimation (TRSE). It is developed on the basis of the classical safety evaluation model where both the amount of exposure to safety risk and the risk under unit exposure are measured to estimate the route safety. A combination of a set of dynamic and static factors related to traffic flow characteristics and roadway features are selected to estimate conflict exposure and potential conflict risk. A route-based method is employed where two parallel estimations of conflict are conducted for both the component segments links and intersection turning links. Three machine learning models (i.e., random forest, k-nearest neighbor, and support vector machine) are tested in conflict risk estimation. A fuzzy reasoning process based on the fuzzy logic algorithm is employed to conduct the route safety estimation. The proposed TRSE is tested on a four-horizontal and six-vertical network extracted from a real road network in China. Conflict simulation results were obtained by Vissim and SSAM tools. The results illustrate the practicability and effectiveness of the proposed TRSE approach.

Traffic volume and crashes and how crash and road characteristics affect their relationship - A meta-analysis

The present study has investigated the relationship between traffic volume and crash numbers by means of meta-analysis, based on 521 crash prediction models from 118 studies. The weighted pooled volume coefficient for all crashes and all levels of crash severity (excluding fatal crashes) is 0.875. The most important moderator variable is crash type. Pooled volume coefficients are systematically greater for multi vehicle crashes (1.210) than for single vehicle crashes (0.552). Regarding crash severity, the results indicate that volume coefficients are smaller for more fatal crashes (0.777 for all fatal crashes) than for injury crashes but no systematic differences were found between volume coefficients for injury and property-damage-only crashes. At higher levels of volume and on divided roads, volume coefficients tend to be greater than at lower levels of volume and on undivided roads. This is consistent with the finding that freeways on average have greater volume coefficients than other types of road and that two-lane roads are the road type with the smallest average volume coefficients. The results indicate that results from crash prediction models are likely to be more precise when crashes are disaggregated by crash type, crash severity, and road type. Disaggregating models by volume level and distinguishing between divided and undivided roads may also improve the precision of the results. The results indicate further that crash prediction models may be misleading if they are used to predict crash numbers on roads that differ from those that were used for model development with respect to composition of crash types, share of fatal or serious injury crashes, road types, and volume levels.

Safety sensitivity to roadway characteristics: A comparison across highway classes

This paper examined the accident risk factors associated with highway traffic and roadway design, for each of three highway classes in the United States using a bivariate modeling framework involving two levels of accident severity. With regard to the highest class (Interstates), the results suggest that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume and average vertical grade, but less sensitive to the inside shoulder width and the median width. For US Roads, it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume, outside shoulder width, pavement condition, and median width but less sensitive to the average vertical grade. For the relatively lowest-class roads (State Roads), it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to the traffic volume, lane width, outside shoulder width, and pavement condition. Compared to the relatively lower-class highways, accidents at higher-class highways are more sensitive to: changes in traffic volume, average vertical grade, median width, inside shoulder width, and the pavement condition (no-casualty accidents only); but less sensitive to changes in lane width, pavement condition (casualty accidents only), and the outside shoulder width. This variation in sensitivity across the different road classes could be attributed to the differences in road geometry standards across the road classes, as the results seem to support the hypothesis that these standards strongly influence accident occurrence. It is hoped that the developed bivariate negative binomial models can help highway engineers to evaluate their current design standards and policy, and to assess the safety consequences of changes in these standards in each road class.

A full Bayesian approach to appraise the safety effects of pedestrian countdown signals to drivers

Although they are meant for pedestrians, pedestrian countdown signals (PCSs) give cues to drivers about the length of the remaining green phase, hence affecting drivers' behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers, in the cities of Jacksonville and Gainesville, Florida, using crash modification factors (CMFs) and crash modification functions (CMFunctions). A full Bayes (FB) before-and-after with comparison group method was used to quantify the safety impacts of PCSs to drivers. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings indicated that installing PCSs result in a significant improvement of drivers' safety, at a 95% Bayesian credible interval (BCI), for total, PDO, and rear-end collisions. The results of FI and angle crashes were not significant. The CMFunctions indicate that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Nevertheless, the CMFs on rear-end crashes are observed to decline with post-treatment time. In summary, the results suggest the usefulness of PCSs for drivers. The findings of this study may prompt a need for a broader research to investigate the need to design PCSs that will serve the purpose not only of pedestrians, but drivers as well.

Time series trends of the safety effects of pavement resurfacing

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time.

Use of empirical and full Bayes before-after approaches to estimate the safety effects of roadside barriers with different crash conditions

INTRODUCTION

Although many researchers have estimated the crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of prior studies that have explored the variation of CMFs. Thus, the main objectives of this study are: (a) to estimate CMFs for the installation of different types of roadside barriers, and (b) to determine the changes of safety effects for different crash types, severities, and conditions.

METHOD

Two observational before-after analyses (i.e. empirical Bayes (EB) and full Bayes (FB) approaches) were utilized in this study to estimate CMFs. To consider the variation of safety effects based on different vehicle, driver, weather, and time of day information, the crashes were categorized based on vehicle size (passenger and heavy), driver age (young, middle, and old), weather condition (normal and rain), and time difference (day time and night time).

RESULTS

The results show that the addition of roadside barriers is safety effective in reducing severe crashes for all types and run-off roadway (ROR) crashes. On the other hand, it was found that roadside barriers tend to increase all types of crashes for all severities. The results indicate that the treatment might increase the total number of crashes but it might be helpful in reducing injury and severe crashes. In this study, the variation of CMFs was determined for ROR crashes based on the different vehicle, driver, weather, and time information.

PRACTICAL APPLICATIONS

Based on the findings from this study, the variation of CMFs can enhance the reliability of CMFs for different roadway conditions in decision making process. Also, it can be recommended to identify the safety effects of specific treatments for different crash types and severity levels with consideration of the different vehicle, driver, weather, and time of day information.

Evaluation of safety effectiveness of multiple cross sectional features on urban arterials

This research evaluates the safety effectiveness of multiple roadway cross-section elements on urban arterials for different crash types and severity levels. In order to consider the nonlinearity of predictors and obtain more reliable estimates, the generalized nonlinear models (GNMs) were developed using 5-years of crash records and roadway characteristics data for urban roadways in Florida. The generalized linear models (GLMs) were also developed to compare model performance. The cross-sectional method was used to develop crash modification factors (CMFs) for various safety treatments. The results from this paper indicated that increasing lane, bike lane, median, and shoulder widths were safety effective to reduce crash frequency. In particular, the CMFs for changes in median and shoulder widths consistently decreased as their widths increased. On the other hand, the safety effects of increasing lane and bike lane widths showed nonlinear variations. It was found that crash rates decrease as the lane width increases until 12ft width and it increases as the lane width exceeds 12ft. The crash rates start to decrease again after 13ft. It was also found that crash rates decreases as the bike lane width increases until 6ft width and it increases as the bike lane width exceeds 6ft. This paper demonstrated that the GNMs clearly captured the nonlinear relationship between crashes and multiple roadway cross-sectional features, which cannot be reflected by the estimated CMFs from the GLMs. Moreover, the GNMs showed better model fitness than GLMs in general. Therefore, in order to estimate more accurate CMFs, the proposed methodology of utilizing the GNMs in the cross-sectional method is recommended over using conventional GLMs when there are nonlinear relationships between the crash rate and roadway characteristics.