Crash Frequency Models Research Articles

Rule extraction from an optimized neural network for traffic crash frequency modeling

This study develops a neural network (NN) model to explore the nonlinear relationship between crash frequency and risk factors. To eliminate the possibility of over-fitting and to deal with the black-box characteristic, a network structure optimization algorithm and a rule extraction method are proposed. A case study compares the performance of the trained and modified NN models with that of the traditional negative binomial (NB) model for analyzing crash frequency on road segments in Hong Kong. The results indicate that the optimized NNs have somewhat better fitting and predictive performance than the NB models. Moreover, the smaller training/testing errors in the optimized NNs with pruned input and hidden nodes demonstrate the ability of the structure optimization algorithm to identify the insignificant factors and to improve the model generalization capacity. Furthermore, the rule-set extracted from the optimized NN model can reveal the effect of each explanatory variable on the crash frequency under different conditions, and implies the existence of nonlinear relationship between factors and crash frequency. With the structure optimization algorithm and rule extraction method, the modified NN model has great potential for modeling crash frequency, and may be considered as a good alternative for road safety analysis.

A method to account for and estimate underreporting in crash frequency research

Underreporting is a well-known issue in crash frequency research. However, statistical methods that can account for underreporting have received little attention in the published literature. This paper compares results from underreporting models to models that account for unobserved heterogeneity. The difference in the elasticities between the negative binomial underreporting model and random parameters negative binomial models, which accounts for unobserved heterogeneity in crash frequency models, are used as the basis for comparison. The paper also includes a comparison of the predicted number of unreported PDO crashes based on the negative binomial underreporting model with crashes that were reported to police but were not considered reportable to PennDOT to assess the ability of the underreporting models to predict non-reportable crashes.The data used in this study included 21,340 segments of two-lane rural highways that are owned and maintained by PennDOT. Reported accident frequencies over an eight year period (2005⿿2012) were included in the sample, producing a total of 170,468 segment-years of data. The results indicate that if a variable impacts both the true accident frequency and the probability of accidents being reported, statistical modeling methods that ignore underreporting produce biased regression coefficients. The magnitude of the bias in the present study (based on elasticities) ranged from 0.00⿿16.79%. If the variable affects the true accident frequency, but not the probability of accidents being reported, the results from the negative binomial underreporting models are consistent with analysis methods that do not account for underreporting.

Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models.

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling.

Ordered Fractional Split Approach for Aggregate Injury Severity Modeling

In crash frequency models, frequency by severity level is examined with multivariate count models. In these multivariate approaches the impact of exogenous variables is quantified through the propensity component of count models. The main interaction between variables across severity levels is sought through unobserved effects; that is, there is no interaction of observed effects across the multiple count models. Although this is not necessarily a limitation, it could be beneficial to evaluate the impact of exogenous variables in a framework that directly relates a single exogenous variable to all severity count variables simultaneously. An alternative approach to examining crash frequency by severity is proposed. Specifically, instead of modeling the number of crashes, a fractional split modeling approach is used to study the fraction of crashes by each severity level on a road segment. Given the ordered nature of injury severity, an ordered probit fractional split model is used to study crash proportion by severity levels. The model is estimated for roadway segment data for single-vehicle and multivehicle crashes in Florida for 2009 through 2011. The model estimation results highlight the effect of traffic volume, lane width, shoulder width, proportion of divided segments, and speed limit on crash proportion by severity. The model results are used to predict hot spots for various crash types. The results highlight how the ordered probit fractional split models can be used for highway safety screening.

Microscopic Safety Evaluation and Prediction for Freeway-to-Freeway Interchange Ramps

Freeway-to-freeway interchange ramps are critical components of freeway networks. The safety of interchange ramps is a concern because of their complex horizontal and vertical alignment. For a better understanding of the crash mechanisms of interchange ramps, this work built multilevel Poisson–lognormal models to estimate 3-h-interval crash frequencies and built multilevel logistic regression models to predict real-time crash risks. All models were applied to both single-vehicle and multivehicle crashes. In addition, this study explored the feasibility of the use of crash reports to identify roadway surface conditions at study sites. The crash frequency models revealed that the logarithm of 3-h traffic volume and average turning angle were positive significant parameters in estimating single-vehicle crash frequency and that high traffic volume, sag, or downgrade vertical curve increased multivehicle crash frequency. The crash risk models revealed that the average turning angle had a positive impact on single-vehicle crash risk. Multivehicle crash risk increased if lane occupancy increased or interchange ramp vertical alignment was a downgrade. Furthermore, the crash risk estimation models indicated that roadway surface condition was one of the most important parameters: wet roadway surfaces increased the single-vehicle crash ratio by 8.87 and the multivehicle crash ratio by 2.82. This study proves that implementing crash reports is an effective method for providing a study event’s weather information. After the weather information from crash reports is added, 36.8% more studied events get roadway surface condition information, and the accuracy increases by 7.4%.

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.

A multivariate spatial crash frequency model for identifying sites with promise based on crash types

Many studies have proposed the use of a systemic approach to identify sites with promise (SWiPs). Proponents of the systemic approach to road safety management suggest that it is more effective in reducing crash frequency than the traditional hot spot approach. The systemic approach aims to identify SWiPs by crash type(s) and, therefore, effectively connects crashes to their corresponding countermeasures. Nevertheless, a major challenge to implementing this approach is the low precision of crash frequency models, which results from the systemic approach considering subsets (crash types) of total crashes leading to higher variability in modeling outcomes. This study responds to the need for more precise statistical output and proposes a multivariate spatial model for simultaneously modeling crash frequencies for different crash types. The multivariate spatial model not only induces a multivariate correlation structure between crash types at the same site, but also spatial correlation among adjacent sites to enhance model precision. This study utilized crash, traffic, and roadway inventory data on rural two-lane highways in Pennsylvania to construct and test the multivariate spatial model. Four models with and without the multivariate and spatial correlations were tested and compared. The results show that the model that considers both multivariate and spatial correlation has the best fit. Moreover, it was found that the multivariate correlation plays a stronger role than the spatial correlation when modeling crash frequencies in terms of different crash types.

Re-visiting crash–speed relationships: A new perspective in crash modelling

Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is explained by highly aggregated average measures that may be inappropriate, especially for time-varying variables such as speed and volume. This paper re-examines crash–speed relationships by creating a new crash data aggregation approach that enables improved representation of the road conditions just before crash occurrences. Crashes are aggregated according to the similarity of their pre-crash traffic and geometric conditions, forming an alternative crash count dataset termed as a condition-based approach. Crash–speed relationships are separately developed and compared for both approaches by employing the annual crashes that occurred on the Strategic Road Network of England in 2012. The datasets are modelled by injury severity using multivariate Poisson lognormal regression, with multivariate spatial effects for the link-based model, using a full Bayesian inference approach. The results of the condition-based approach show that high speeds trigger crash frequency. The outcome of the link-based model is the opposite; suggesting that the speed–crash relationship is negative regardless of crash severity. The differences between the results imply that data aggregation is a crucial, yet so far overlooked, methodological element of crash data analyses that may have direct impact on the modelling outcomes.

Work Zone Safety Analysis and Modeling: A State-of-the-Art Review

Objective: Work zone safety is one of the top priorities for transportation agencies. In recent years, a considerable volume of research has sought to determine work zone crash characteristics and causal factors. Unlike other non–work zone–related safety studies (on both crash frequency and severity), there has not yet been a comprehensive review and assessment of methodological approaches for work zone safety. To address this deficit, this article aims to provide a comprehensive review of the existing extensive research efforts focused on work zone crash-related analysis and modeling, in the hopes of providing researchers and practitioners with a complete overview.Methods: Relevant literature published in the last 5 decades was retrieved from the National Work Zone Crash Information Clearinghouse and the Transport Research International Documentation database and other public digital libraries and search engines. Both peer-reviewed publications and research reports were obtained. Each study was carefully reviewed, and those that focused on either work zone crash data analysis or work zone safety modeling were identified. The most relevant studies are specifically examined and discussed in the article.Results: The identified studies were carefully synthesized to understand the state of knowledge on work zone safety. Agreement and inconsistency regarding the characteristics of the work zone crashes discussed in the descriptive studies were summarized. Progress and issues about the current practices on work zone crash frequency and severity modeling are also explored and discussed. The challenges facing work zone safety research are then presented.Conclusions: The synthesis of the literature suggests that the presence of a work zone is likely to increase the crash rate. Crashes are not uniformly distributed within work zones and rear-end crashes are the most prevalent type of crashes in work zones. There was no across-the-board agreement among numerous papers reviewed on the relationship between work zone crashes and other factors such as time, weather, victim severity, traffic control devices, and facility types. Moreover, both work zone crash frequency and severity models still rely on relatively simple modeling techniques and approaches. In addition, work zone data limitations have caused a number of challenges in analyzing and modeling work zone safety. Additional efforts on data collection, developing a systematic data analysis framework, and using more advanced modeling approaches are suggested as future research tasks.

A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections

Crash frequency and crash severity models have explored the factors that influence intersection safety. However, most of these models address the frequency and severity independently, and miss the correlations between crash frequency models at different crash severity levels. We develop a two-stage bivariate logistic-Tobit model of the crash severity and crash risk at different severity levels. The first stage uses a binary logistic model to determine the overall crash severity level. The second stage develops a bivariate Tobit model to simultaneously evaluate the risk of a crash resulting in a slight injury and the risk of a crash resulting in a kill or serious injury (KSI). The model uses 420 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during 2002 and 2003. The results obtained from the first-stage binary logistic model indicate that the overall crash severity level is significantly influenced by the annual average daily traffic and number of pedestrian crossings. The results obtained from the second-stage bivariate Tobit model indicate that the factor that significantly influences the numbers of both slight injury and KSI crashes is the proportion of commercial vehicles. The existence of four or more approaches, the reciprocal of the average turning radius and the presence of a turning pocket increase the likelihood of slight injury crashes. The average lane width and cycle time affect the likelihood of KSI crashes. A comparison with existing approaches suggests that the bivariate logistic-Tobit model provides a good statistical fit and offers an effective alternative method for evaluating the safety performance at signalized intersections.

Application of generalized estimating equations for crash frequency modeling with temporal correlation

Traditional crash frequency modeling uses crash frequency data averaged across multiple years. When data size is small, crash data in each year are used in the modeling to extend the size of the samples. The extension of sample size could create a temporal correlation among crash frequencies of the different years, which could affect the modeling accuracy. The primary objective of this study is to evaluate the application of the generalized estimating equation (GEE) procedures to account for the temporal correlation in the longitudinal crash frequency data. Four-year crash data at exit ramps on a freeway in China were collected for modeling. Based on the same data, traditional generalized linear models (GLMs) were estimated for model com- parison. Results showed that traditional GLM underestimated the standard errors of coefficients for explanatory variables. The GEE procedure with an exchangeable correlation structure successively captured the temporal correlation among the crash fre- quencies of the different years. The GLM with GEE outperformed the traditional GLM in providing a good fit for the crash fre- quency data. Results of this study can help researchers better understand how various factors affect the crash frequencies at freeway divergent areas and propose effective countermeasures.

Assessing the Safety Effects of Removing Highway Mainline Barrier Toll Plazas

AbstractToll plazas create a number of bottlenecks on highways. They can induce motor vehicle crashes and also put workers such as toll collectors at risk. Enhancing safety at toll plazas is crucial to improving safety on tolled roadways. This study aims to evaluate the safety effects of removing mainline barrier toll plazas on highways using empirical Bayesian (EB) methodology. Recent removals of barrier toll plazas on the Garden State Parkway in New Jersey were used as a case study. Multiple-year traffic and crash data before and after the removals of the barrier toll plazas were used for evaluation. Toll plaza crash frequency models as a function of traffic flow and other relevant factors were developed, with the modeling results suggesting that there is a nonlinear relationship between toll plaza crash occurrences and both traffic flow as well as tollbooth configurations. The EB approach is also used to predict crash frequency assuming that the barrier tollbooths were not removed. These EB-based estim...

Crash frequency modeling for signalized intersections in a high-density urban road network

Conventional crash frequency models rely on an assumption of independence among observed crashes. However, this assumption is frequently proved false by spatially related crash observations, particularly for intersection crashes observed in high-density road networks. Crash frequency models that ignore the hierarchy and spatial correlation of closely spaced intersections can lead to biased estimations. As a follow-up to our previous paper (Xie et al., 2013), this study aims to address this issue by introducing an improved crash frequency model. Data for 195 signalized intersections along 22 corridors in the urban areas of Shanghai was collected. Moran׳s I statistic of the crash data confirmed the spatial dependence of crash occurrence among the neighboring intersections. Moreover, Lagrange Multiplier test was performed and it suggested that the spatial dependence should be captured in the model error term. A hierarchical model incorporating a conditional autoregressive (CAR) effect term for the spatial correlation was developed in the Bayesian framework. A deviance information criterion (DIC) and cross-validation test were used for model selection and comparison. The results showed that the proposed model outperformed traditional models in terms of the overall goodness of fit and predictive performance. In addition, the significance of the corridor-specific random effect and CAR effect revealed strong evidence for the presence of heterogeneity across corridors and spatial correlation among intersections.

Crash frequency modeling using negative binomial models: An application of generalized estimating equation to longitudinal data

The prediction of crash frequency models can be improved when several years of crash data are utilized, instead of three to five years of data most commonly used in research. Crash data, however, generates multiple observations over the years that can be correlated. This temporal correlation affects the estimated coefficients and their variances in commonly used crash frequency models (such as negative binomial (NB), Poisson models, and their generalized forms). Despite the obvious temporal correlation of crashes, research analyses of such correlation have been limited and the consequences of this omission are not completely known. The objective of this study is to explore the effects of temporal correlation in crash frequency models at the highway segment level.In this paper, a negative binomial model has been developed using a generalized estimating equation (GEE) procedure that incorporates the temporal correlations amongst yearly crash counts. The longitudinal model employs an autoregressive correlation structure to compare to the more traditional NB model, which uses a Maximum Likelihood Estimation (MLE) method that cannot accommodate temporal correlations. The GEE model with temporal correlation was found to be superior compared to the MLE model, as it does not underestimate the variance in the coefficient estimates, and it provides more accurate and less biased estimates. Furthermore, an autoregressive correlation structure was found to be an appropriate structure for longitudinal type of data used in this study. Ten years (2002–2011) of Missouri Interstate highway crash data have been utilized in this paper. The crash data comprises of traffic characteristics and geometric properties of highway segments.

Direct Spatial Correlation in Crash Frequency Models: Estimation of the Effective Range

Spatial correlation has shown to be present in highway safety data, yet the distance at which sites should be considered correlated is largely unknown. The purpose of this research is to explore the effect of direct spatial correlation structures on crash frequency models at the road segment level and compare them to spatial conditional models. By using direct spatial correlation structures the “effective range” (i.e., the distance at which there is no lingering spatial correlation) is estimated. Full Bayes hierarchical models with direct spatial correlation effects and conditional autoregressive (CAR) spatial effects are estimated. The model of crash, traffic, and roadway inventory data from Pennsylvania shows an average effective range of around 168 m which is in line with previous findings from conditional models. The direct spatial correlation model has a better goodness of fit than the random effects and the CAR model. Furthermore, the proportion of variation in the data explained by the spatial correlation term is almost the same for both spatial models. The standard deviations in coefficient estimates are slightly lower for the direct spatial correlation model compared to the random effects model but significantly lower than the CAR model.

Investigating the different characteristics of weekday and weekend crashes

IntroductionThis study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes. MethodWeekend crashes were defined as crashes occurring between Friday 9p.m. and Sunday 9p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed. ResultsIt was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety. Impact on IndustryUtilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.

Modeling work zone crash frequency by quantifying measurement errors in work zone length

Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.

야간 및 일출몰 시간대 교통안전에 영향을 미치는 고속도로 기하구조 특성분석

고속의 주행속도로 고속도로를 주행하는 운전자는 교통사고 예방을 위하여 일반도로 운전자와 비교하여 도로선형, 교통상황, 환경요인에 보다 신속하고 능동적으로 반응해야 한다. 특히 야간 및 일출몰 시간대에는 운전자의 도로선형변화 인지 능력이 주간 보다 떨어질 수 있으므로, 이러한 시간대에 발생하는 교통사고와 도로기하구조 특성을 체계적으로 분석하고 대응방안을 도출 것은 고속도로 교통안전 제고를 위해 대단히 중요한 작업이다. 본 연구에서는 교통안전과 고속도로의 기하구조의 특성과의 관계를 주간과 야간으로 비교하여 분석하였다. 또한 일출 및 일몰과 같이 태양의 고도가 운전자의 전방 상황주시에 영향을 주는 환경에서 도로의 선형과 교통안전과의 관계를 추가로 분석하여 위험한 도로조건을 찾고자 하였다. 본 연구에서는 이를 위해 사고발생 시간대를 주간, 야간, 일출몰 3가지로 나누어 포아송회귀분석과 음이항회귀분석을 활용하여 고속도로 교통사고빈도 모형을 도출하였다. 분석대상 구간은 전국 고속도로 중 제한속도를 110km/h로 운영 중인 서해안선, 중부선, 중부내륙선으로 설정하였으며, 분석구간의 2007년~2010년 4년간 교통사고 자료를 활용하였다. 분석결과 시간대에 따라 교통사고에 영향을 미치는 요인들은 차이가 있는 것으로 나타났다. 본 연구결과는 고속도로 교통사고 예방을 위해 야간 및 일출몰 시간대에 운전자의 안전운전을 지원할 수 있는 다양한 대응방안을 수립하는데 효과적으로 활용될 수 있을 것으로 기대된다. Driver's capability of identifying the change in freeway alignments and environments is one of important factors associated with traffic safety on freeways. In particular, driver's visibility and recognition capability are highly dependent on the altitude of the sun by sunset, sunrise, and nighttime. The purpose of this study is to identify the characteristics of geometric conditions affecting crash occurrences at sunset, sunrise, and nighttime. Poisson and negative binomial regressions were adopted to predict freeway crash frequency in this study. Freeway crash data during 2007~2010 were used for developing the crash frequency models. A set of variables representing the characteristics of geometric conditions were identified as significant ones affecting crash occurrences. The results of this study would be useful in deriving effective countermeasures for preventing traffic crashes that mainly occur at sunset, sunrise, and nighttime on freeways.

Crash frequency analysis of different types of urban roadway segments using generalized additive model

IntroductionThis paper utilizes generalized additive model to explore the potential non-linear relationship between crash frequency and exposure on different types of urban roadway segments. MethodsGeneralized additive models are used to analyze crash frequency data and compared with the commonly used crash rate method and generalized linear models using a five-year crash data set from Houston, Texas. ResultsThe study shows that the relationship between crash frequency and exposure varies by segment type and the linearity may only approximately exist in certain segment types. In addition, the generalized additive modeling results suggest that such relationship curves may not be monotonic. Finally, this study demonstrates that generalized additive models in general provide better flexibility and modeling performance than generalized linear models. Impact on IndustryThe generalized additive model provides a very promising alternative for crash frequency modeling and other safety studies.

Understanding Road Safety Impact of High-Performance Bus Rapid Transit and Busway Design Features

The design choices made in the planning of a new bus rapid transit (BRT) or busway corridor (e.g., use of a center-lane or curbside configuration, counterflow lanes, and open or closed stations) affect not only the operational performance of the system but also the risks of crashes, injuries, and fatalities on the facility over its lifetime. With data from nine BRT systems and busways around the world (including Bogotá, Colombia; Curitiba, Brazil; Mexico City, Mexico; and Delhi, India), some of the road safety impacts of major BRT–busway corridor design characteristics are illustrated. The approach included a combination of crash frequency modeling, road safety inspections, and interviews with transit agency staff and safety experts. Center-lane systems tended generally to be safer than were curbside systems, and counter flow lanes were the most dangerous possible configuration. Some of the features that provide higher passenger capacity (such as multiple bus lanes and multiple docking bays at stations) may introduce new types of conflicts and crashes. In the planning of any bus system, trade-offs often need to be made between capacity, safety, and pedestrian accessibility along the corridor. This study provides the necessary elements for successfully integrating road safety considerations into the design and operation of future BRT systems and busways.