A comparative Full Bayesian before-and-after analysis and application to urban road safety countermeasures in New Jersey

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.

Examining the performance of engineering treatment evaluation methodologies using the hypothetical treatment and actual treatment settings

The selection of treatment evaluation methodology is paramount in determining reliable crash modification factors (CMFs) for engineering treatments. A lack of ground truth makes it cumbersome to examine the performance of treatment evaluation methodologies. In addition, a sound methodological framework is critical for evaluating the performances of treatment evaluation methodologies. In addressing these challenges, this study proposed a framework for assessing treatment evaluation methodologies by hypothetical treatments with known ground truth and actual real-world treatments. In particular, this study examined three before-after treatment evaluation approaches: 1) Empirical Bayes, 2) Simulation-based Empirical Bayes, and 3) Full Bayes methods. In addition, this study examined the Cross-Sectional treatment evaluation methodology. The methodological framework utilized five datasets of hypothetical treatment with known ground truth based on the hotspot identification method and a real-world dataset of wide centerline treatment on two-lane, two-way rural highways in Queensland, Australia. Results showed that all the methods could identify the ground truth of hypothetical treatments, but the Full Bayes approach better predicts the known ground truth compared to Empirical Bayes, Simulation-based Empirical Bayes, and Cross-Sectional methods. The Full Bayes approach was also found to provide the most precise estimate for real-world wide centerline treatment along rural highways compared to other methods. Moreover, the current study highlighted that the Cross-Sectional method offers a viable estimate of treatment effectiveness in case the before-period data is limited.

Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters

Most existing efforts to assess safety performance require sufficient crash data, which generally takes a few years to collect and suffers from certain limitations (such as long data collection time, under-reporting issue and so on). Alternatively, the surrogate safety measure (SSMs) based approach that can assess traffic safety by capturing the more frequent "near-crash" situations have been developed, but it is criticized for the potential sampling and measurement errors. This study proposes a new safety performance measure-Risk Status (RS), by fusing crash data and SSMs. Real-world connected vehicle data collected in the Safety Pilot Model Deployment (SPMD) project in Ann Arbor, Michigan is used to extract SSMs. With RS treated as a latent variable, a structural equation model with conditional autoregressive spatial effect and corridor-level random parameters is developed to model the interrelationship among RS, crash frequency, risk identified by SSMs, and contributing factors. The modeling results confirm the proposed interrelationship and the necessity to account for both spatial autocorrelation and unobserved heterogeneity. RS can integrate both crash frequency and SSMs together while controlling for observed and unobserved factors. RS is found to be a more reliable criterion for safety assessment in an implementation case of hotspot identification.

Investigating the impacts of crash prediction models on quantifying safety effectiveness of Adaptive Signal Control Systems

INTRODUCTION

Adaptive Signal Control System (ASCS) can improve both operational and safety benefits at signalized corridors.

METHODS

This paper develops a series of models accounting for model forms and possible predictors and implements these models in Empirical Bayes (EB) and Fully Bayesian (FB) frameworks for ASCS safety evaluation studies. Different models are validated in terms of the ability to reduce the potential bias and variance of prediction and improve the safety effectiveness estimation accuracy using real-world crash data from non-ASCS sites. This paper then develops the safety effectiveness of ASCS at six different corridors with a total of 65 signalized intersections with the same type of ASCS, in South Carolina.

RESULTS

Validation results show that the FB model that accounts for traffic volume, roadway geometric features, year factor, and spatial effects shows the best performance among all models. The study findings reveal that ASCS reduces crash frequencies in the total crash, fatal and injury crash, and angle crash for most of the intersections. The safety effectiveness of ASCS varies with different intersection features (i.e., AADT at major streets, number of legs at an intersection, the number of through lanes on major streets, the number of access points on minor streets, and the speed limit at major streets).

CONCLUSIONS

ASCS is associated with crash reductions, and its safety effects vary with different intersection features. Practical Applications: The findings of this research encourage more ASCS deployments and provide insights into selecting ASCS deployment sites for reducing crashes considering the variation of the safety effectiveness of ASCS.

An examination of the intersection environment associated with perceived crash risk among school-aged children: using street-level imagery and computer vision

While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings provide information useful to providing appropriate and proactive interventions that may reduce the risk of crashes at intersections.

A full Bayes approach for traffic conflict-based before-after safety evaluation using extreme value theory

A full Bayes approach is proposed for traffic conflict-based before-after safety evaluations using extreme value theory. The approach combines traffic conflicts of different sites and periods and develops a uniform generalized extreme value (GEV) model for the treatment effect estimation. Moreover, a hierarchical Bayesian structure is used to link possible covariates to GEV parameters and to account for unobserved heterogeneity among different sites. The proposed approach was applied to evaluate the safety benefits of a left-turn bay extension project in the City of Surrey, Canada, in which traffic conflicts were collected from 3 treatment sites and 3 matched control sites before and after the treatment. A series of models were developed considering different combinations of covariates and their link to different GEV model parameters. Based on the best fitted model, the treatment effects were analyzed quantitatively using the odds ratio (OR) method as well as qualitatively by comparing the shapes of GEV distributions. The results show that there are significant reduction in the expected number of crashes (i.e., OR = 0.409). In addition, there are apparent changes in the shape of GEV distributions for the treatment sites, where GEV distributions shift further away from the risk of crash area after the treatment. Both of these results indicate significant safety improvements after the left-turn bay extension.

Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China

Urban Large-scale Public Spaces (ULPS) are important areas of urban culture and economic development, which are also places of the potential safety hazard. ULPS safety assessment has played a crucial role in the theory and practice of urban sustainable development. The primary objective of this study is to explore the interaction between ULPS safety risk and its influencing factors. In the first stage, an index sensitivity analysis method was applied to calculate and identify the safety risk assessment index system. Next, a Delphi method and information entropy method were also applied to collect and calculate the weight of risk assessment indicators. In the second stage, a Dempster-Shafer Theory (DST) method with evidence fusion technique was utilized to analyze the interaction between the ULPS safety risk level and the multiple-index variables, measured by four observed performance indicators, i.e., environmental factor, human factor, equipment factor, and management factor. Finally, an empirical study of DST approach for ULPS safety performance analysis was presented.

A New Methodology for Before-After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before-after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before-after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red-light-running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.

Speed, speed variation and crash relationships for urban arterials

Speed and speed variation are closely associated with traffic safety. There is, however, a dearth of research on this subject for the case of urban arterials in general, and in the context of developing nations. In downtown Shanghai, the traffic conditions in each direction are very different by time of day, and speed characteristics during peak hours are also greatly different from those during off-peak hours. Considering that traffic demand changes with time and in different directions, arterials in this study were divided into one-way segments by the direction of flow, and time of day was differentiated and controlled for. In terms of data collection, traditional fixed-based methods have been widely used in previous studies, but they fail to capture the spatio-temporal distributions of speed along a road. A new approach is introduced to estimate speed variation by integrating spatio-temporal speed fluctuation of a single vehicle with speed differences between vehicles using taxi-based high frequency GPS data. With this approach, this paper aims to comprehensively establish a relationship between mean speed, speed variation and traffic crashes for the purpose of formulating effective speed management measures, specifically using an urban dataset. From a total of 234 one-way road segments from eight arterials in Shanghai, mean speed, speed variation, geometric design features, traffic volume, and crash data were collected. Because the safety effects of mean speed and speed variation may vary at different segment lengths, arterials with similar signal spacing density were grouped together. To account for potential correlations among these segments, a hierarchical Poisson log-normal model with random effects was developed. Results show that a 1% increase in mean speed on urban arterials was associated with a 0.7% increase in total crashes, and larger speed variation was also associated with increased crash frequency.

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.

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.

An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance.

Estimating the safety effects of lane widths on urban streets in Nebraska using the propensity scores-potential outcomes framework

A sufficient understanding of the safety impact of lane widths in urban areas is necessary to produce geometric designs that optimize safety performance for all users. The overarching trend found in the research literature is that as lane widths narrow, crash frequency increases. However, this trend is inconsistent and is the result of multiple cross-sectional studies that have issues related to lack of control for potential confounding variables, unobserved heterogeneity or omitted variable bias, or endogeneity among independent variables, among others. Using ten years of mid-block crash data on urban arterials and collectors from four cities in Nebraska, crash modification factors (CMFs) were estimated for various lane widths and crash types. These CMFs were developed using the propensity scores-potential outcomes methodology. This method reduces many of the issues associated with cross-sectional regression models when estimating the safety effects of infrastructure-related design features. Generalized boosting, a non-parametric modeling technique, was used to estimate the propensity scores. Matching was performed using both Nearest Neighbor and Mahalanobis matching techniques. CMF estimation was done using mixed-effects negative binomial or Poisson regression with the matched data. Lane widths included in the analysis included 9ft, 10ft, 11ft, and 12ft. Some of the estimated CMFs were point estimates while others were functions of traffic volume (i.e., the CMF changed depending on the traffic volume). Roadways with 10ft travel lanes were found to experience the highest crash frequency relative to other lane widths. Meanwhile, roads with 9ft travel lanes were found to experience the lowest relative crash frequency. While this may be due to increased driver caution when traveling on narrow lanes, it is possible that unobserved factors influenced this result. CMFs for target crash types (sideswipe same-direction and sideswipe opposite-direction) were consistent with the values currently used in the Highway Safety Manual (HSM).

Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models

This study evaluates the effectiveness of changing lane width in reducing crashes on roadway segments. To consider nonlinear relationships between crash rate and lane width, the study develops generalized nonlinear models (GNMs) using 3-years crash records and road geometry data collected for all roadway segments in Florida. The study also estimates various crash modification factors (CMFs) for different ranges of lane width based on the results of the GNMs. It was found that the crash rate was highest for 12-ft lane and lower for the lane width less than or greater than 12ft. GNMs can extrapolate this nonlinear continuous effect of lane width and estimate the CMFs for any lane width, not only selected lane widths, unlike generalized linear models (GLMs) with categorical variables. The CMFs estimated using GNMs reflect that crashes are less likely to occur for narrower lanes if the lane width is less than 12ft whereas crashes are less likely to occur for wider lanes if the lane width is greater than 12ft. However, these effects varied with the posted speed limits as the effect of interaction between lane width and speed limit was significant. The estimated CMFs show that crashes are less likely to occur for lane widths less than 12ft than the lane widths greater than 12ft if the speed limit is higher than or equal to 40mph. It was also found from the CMFs that crashes at higher severity levels (KABC and KAB) are less likely to occur for lane widths greater or less than 12ft compared to 12-ft lane. The study demonstrates that the CMFs estimated using GNMs clearly reflect variations in crashes with lane width, which cannot be captured by the CMFs estimated using GLMs. Thus, it is recommended that if the relationship between crash rate and lane width is nonlinear, the CMFs are estimated using GNMs.

Development of adjustment functions to assess combined safety effects of multiple treatments on rural two-lane roadways

Numerous studies have attempted to evaluate the safety effectiveness of specific single treatment on roadways by estimating crash modification factors (CMFs). However, there is a need to also assess safety effects of multiple treatments since multiple treatments are usually simultaneously applied to roadways. Due to the lack of sufficient CMFs of multiple treatments, the Highway Safety Manual (HSM) provides combining method for multiple CMFs. However, it is cautioned in the HSM and related sources that combined safety effect of multiple CMFs may be over or under estimated. Moreover, the literature did not evaluate the accuracy of the combining method using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) to estimate CMFs and crash modification functions (CM Functions) for two single treatments (shoulder rumble strips, widening (1-9ft) shoulder width) and combination (installing shoulder rumble strips+widening shoulder width) using the observational before-after with empirical Bayes (EB) method and (2) to develop adjustment factors and functions to assess combined safety effects of multiple treatments based on the accuracy of the combined CMFs for multiple treatments estimated by the existing combining method. Data was collected for rural two-lane roadways in Florida and Florida-specific safety performance functions (SPFs) were estimated for different crash types and severities. The CM Functions and adjustment functions were developed using linear and nonlinear regression models. The results of before-after with EB method show that the two single treatments and combination are effective in reducing total and SVROR (single vehicle run-off roadway) crashes. The results indicate that the treatments were more safety effective for the roadway segments with narrower original shoulder width in the before period. It was found that although the CMFs for multiple treatments (i.e., combination of two single treatments) were generally lower than CMFs for single treatments, they were getting similar to the roadway segments with wider shoulder width. The findings indicate that the combined safety effects of multiple treatments using HSM combining method are mostly over-estimated and the accuracy of HSM combining method vary based on crash types and severity levels. Therefore, it is recommended to develop and apply the adjustment factors and functions to predict the safety effects of multiple treatments when the HSM combining method is used.

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.

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.

Measuring safety treatment effects using full Bayes non-linear safety performance intervention functions

Full Bayes linear intervention models have been recently proposed to conduct before-after safety studies. These models assume linear slopes to represent the time and treatment effects across the treated and comparison sites. However, the linear slope assumption can only furnish some restricted treatment profiles. To overcome this problem, a first-order autoregressive (AR1) safety performance function (SPF) that has a dynamic regression equation (known as the Koyck model) is proposed. The non-linear 'Koyck' model is compared to the linear intervention model in terms of inference, goodness-of-fit, and application. Both models were used in association with the Poisson-lognormal (PLN) hierarchy to evaluate the safety performance of a sample of intersections that have been improved in the Greater Vancouver area. The two models were extended by incorporating random parameters to account for the correlation between sites within comparison-treatment pairs. Another objective of the paper is to compute basic components related to the novelty effects, direct treatment effects, and indirect treatment effects and to provide simple expressions for the computation of these components in terms of the model parameters. The Koyck model is shown to furnish a wider variety of treatment profiles than those of the linear intervention model. The analysis revealed that incorporating random parameters among matched comparison-treatment pairs in the specification of SPFs can significantly improve the fit, while reducing the estimates of the extra-Poisson variation. Also, the proposed PLN Koyck model fitted the data much better than the Poisson-lognormal linear intervention (PLNI) model. The novelty effects were short lived, the indirect (through traffic volumes) treatment effects were approximately within ±10%, whereas the direct treatment effects indicated a non-significant 6.5% reduction during the after period under PLNI compared to a significant 12.3% reduction in predicted collision counts under the PLN Koyck model.