Safety evaluation of continuous green T intersections: A propensity scores-genetic matching-potential outcomes approach

Crash modification factors of rumble strips on horizontal curves of two-lane rural roads: A propensity scores potential outcomes approach

Horizontal curves are known to be more crash-prone than tangent sections particularly with respect to roadway departure crashes. Rumble strips are an effective countermeasure to mitigate various types of roadway departure crashes. While existing studies on the safety effectiveness of rumble strips have primarily used before-after study designs or cross-sectional methods for crash modification factor (CMF) estimation, these methods often suffer from imbalanced datasets and larger standard errors, especially when the sample size is small. To address this, this study applies the propensity score potential outcome (PSPO) framework to estimate CMFs for centerline rumble strips, shoulder rumble strips, and their combined application on horizontal curves. In addition to contributing to the development of CMFs by crash severity, this study also examines the effects of rumble strips on collision types, highlighting their impact on vehicle maneuvering and collision characteristics. The analysis is conducted on horizontal curves on two-lane rural roads in Pennsylvania, utilizing crash data from 2017 to 2021. The PSPO method effectively reduces bias between sites with and without rumble strips, and the resulting statistical models align with engineering judgment. The findings indicate that centerline rumble strips reduce opposite direction sideswipe and head-on crashes but increase run off the road and hit fixed object crashes. Shoulder rumble strips, either alone or in combination with centerline rumble strips, decrease crash frequencies for most types except opposite direction sideswipe and head-on crashes. However, shoulder rumble strips alone are more effective at reducing crash frequencies on horizontal curves than when combined with centerline rumble strips.

An integrated intelligent intersection control system for preventing rear-end and angled crashes: System design and deployment results

In view of the dynamic all-red extension (DARE) system's effectiveness in preventing angled crashes (Park et al., 2018), this study has further enhanced its function to contend with rear-end collisions with dynamic green extension (DGE). With such a function, the enhanced Integrated Intelligent Intersection control system (III-CS) is capable of dynamically terminating the green at the interval of the lowest rear-end collision risk, so as to prevent undesirable "max-out" under actuated signal control which often traps some vehicles in the dilemma zone during high-volume traffic conditions. To ensure its effectiveness in practice, the proposed III-CS has been designed with the following new features: (i) executing the DGE within a customized time window of the green phase to ensure the signal's effective coordination with its neighboring intersections; (ii) adopting the comparison-based heuristic for the DGE's real-time risk prediction so as to circumvent the computing and communications delays. The results of two after-deployment assessments show that the system's DARE has perfectly detected all red-light runners; 66.7 percent of the decisions by the DGE module were observed to achieve the control objective during the first field assessment. The DGE's performance in making optimal decisions has improved over time and reached the level of 81.3% in the second field evaluation. Other measures of effectiveness, such as the number of vehicles trapped in the dilemma zone and the average deceleration rate of the driving populations approaching the target intersection, have also evolved to the anticipated trend after the deployment.

Road safety evaluation with multiple treatments: A comparison of methods based on simulations

This paper focuses on ex-post road safety evaluation with multiple treatments. The potential outcome framework for causal inference is introduced to formalize the causal estimands of interest. Various estimation methods are compared via performing simulation experiments based on semi-synthetic data constructed from a London 20 mph zones dataset. The methods under evaluation include regressions, propensity score (PS) based methods, and a machine learning-based method termed generalized random forests (GRF). Both PS-based methods and GRF show higher flexibility with respect to functional specifications of outcome models. Moreover, GRF shows great superiority in the cases where road safety treatments are assigned following specific criteria and/or where there are heterogeneous treatment effects. Considering the ex-post evaluation of combined effects of multiple treatments has significant practical value, the potential outcome framework and the estimation methods presented in this paper are highly recommended for road safety studies.

Estimating heterogeneous treatment effects in road safety analysis using generalized random forests

Numerous evaluation studies have been conducted on a variety of road safety measures. However, the issue of treatment heterogeneity, defined as the variation in treatment effects, has rarely been investigated before. This paper contributes to the literature by introducing generalized random forests (GRF) for estimation of heterogeneous treatment effects (HTEs) in road safety analysis. GRF has high functional flexibility and is able to search for complex treatment heterogeneity. We first perform a series of simulation experiments to compare GRF with three causal methods that have been used in road safety studies, i.e., outcome regression method, propensity score method, and doubly robust estimation method. The simulation results suggest that GRF is superior to these three methods in terms of model specification, especially with the existence of nonlinearity and nonadditivity. On the other hand, a large dataset is required for accurate GRF estimation. Then we conduct a case study on the UK's speed camera program. Our results indicate significant reductions in the number of road accidents at speed camera sites. And the heterogeneity in treatment effects is found to be statistically significant. We further consider the associations between the baseline accident records, traffic volume, local socio-economic characteristics, and the safety effects of speed cameras. In general, the effect of speed cameras is larger at the sites with more baseline accident records, higher traffic volume, and in more densely-populated and deprived areas. Several policy suggestions are provided based on these findings. The evaluation of HTEs likely offers more comprehensive information to local authorities and policy makers, and improves the performance of speed camera programs. Moreover, GRF can be a promising approach for revealing treatment effect heterogeneity in road safety analysis.

Propensity score methods for road safety evaluation: Practical suggestions from a simulation study

The propensity score (PS) based method has been increasingly used in road safety evaluation studies. However, several major considerations regarding its implementation arise when using the PS method. First, as is well known, the PS method is 'data hungry' in terms of the number of treated and control units, however, it is sometimes difficult and time-consuming to construct a large sample in road safety studies. It would be helpful to better understand how to choose a proper sample size, as well as the ratio of the number of treated units to the control ones. Second, the criteria used for covariates selection of the PS model were not fully consistent across the existing road safety evaluation studies. Due to the complicated mechanisms behind the implementation of road safety measures and policies, including all relevant covariates that affect both the selection into treatment (i.e., implementation of road safety measures) and the outcomes (i.e., road accidents) is impossible. In this paper, we conduct a simulation study to investigate such issues and provide some practical suggestions for using PS methods in road safety evaluations. The estimator considered in this study is the inverse probability weighting estimator based on the PS. Our results suggest that the bias and variance of the estimated treatment effect will remain stable when the sample size reaches a certain level. A proper sample size is the one that ensures relevant covariates achieve acceptable balance. Regarding the issue of covariates selection, including the covariates that significantly affect the road accidents is recommended, regardless of whether they affect the implementation of road safety measures. This study also proposes practical procedures for using the weighting approach to evaluate the effects of road safety treatments.

Evaluating the speed camera sites selection criteria in the UK

INTRODUCTION

Speed cameras have been implemented to improve road safety over recent decades in the UK. Although the safety impacts of the speed camera have been estimated thoroughly, the criteria for selecting camera sites have rarely been studied. This paper evaluates the current speed camera sites selection criteria in the UK based on safety performance.

METHOD

A total of 332 speed cameras and 2,513 control sites with road traffic accident data are observed from 2002 to 2010. Propensity score matching method and empirical Bayes method are employed and compared to estimate the safety effects of speed cameras under different scenarios.

RESULTS

First, the main characteristics of speed cameras meeting and not meeting the selection criteria are identified. The results indicate that the proximity to school zones and residential neighborhoods, as well as population density, are the main considerations when selecting speed camera sites. Then the official criteria used for selecting camera sites are evaluated, including site length (a stretch of road that has a fixed speed camera or has had one in the past), previous accident history, and risk value (a numerical scale of the risk level). The results suggest that a site length of 500 m should be used to achieve the optimum safety effects of speed cameras. Furthermore, speed cameras are most effective in reducing crashes when the requirement of minimum number of historical killed and seriously injured collisions (KSIs) is met. In terms of the risk value, it is found that the speed cameras can obtain optimal effectiveness with a risk value greater than or equal to 30, rather than the recommended risk value of 22.

A causal analysis of time-varying speed camera safety effects based on the propensity score method

INTRODUCTION

Speed limit enforcement cameras provide an effective approach to reduce vehicle speeds and the number of road accidents. However, it is still unclear whether the safety effects of speed cameras show durability over long periods of time. This paper analyses how the effects of speed cameras on road accidents change over time. A total number of 771 camera sites and 4787 potential control sites are observed for a period of 18 years (1999-2016) across England.

METHOD

Covariates such as road class, crash history, speed limit, and annual average daily traffic (AADT) are included in the data set. A difference in difference (DID) based propensity score matching (PSM) method is employed to select proper control sites and estimate the treatment effects. The safety effects of speed cameras are then evaluated from a long-term perspective. The post-treatment period is divided into four equal-length periods: early, medium 1 and 2, and late.

RESULTS AND CONCLUSIONS

The results show that speed cameras have significantly reduced the number of road accidents near the camera sites. However, the effects vary across different time periods. The safety effects of speed cameras experienced a sharp decrease during the medium periods after an initial period of highly reduced accidents (medium 1: -53.1%, medium 2: -40.7%) and recovered slightly during the late period. In addition, to evaluate the criteria for selecting camera sites in the UK, we further investigated whether speed cameras at high risk sites have better safety performance. The results show that while safety effects at high risk camera sites also decreased during the medium periods, the reduction was smaller (medium 1: -20.8%, medium 2: -2.1%). Practical Applications: Appropriate road traffic regulations and management, as well as proper camera sites selection criterion, are important to maintain the effectiveness of speed cameras.

Incorporating Bayesian methods into the propensity score matching framework: A no-treatment effect safety analysis

The propensity score matching method has been used to estimate safety countermeasure (treatment) effects from observational crash data. Within the counterfactual framework, propensity score matching is used to balance the covariates between treatment and control groups. Recent studies in traffic safety research have demonstrated the strength of this method in reducing the bias caused by treatment site selection. However, several general issues associated with safety effect estimates may still influence the effectiveness and robustness of this method. In the present study, Bayesian methods were integrated into the propensity score matching method. Bayesian models are known for their ability to capture heterogeneity and modeling uncertainty. This may help mitigate unobserved variable effects in the roadway and crash data. Furthermore, the sampling-based algorithm used for Bayesian estimation yields more consistent estimates in small region analysis than estimates from frequentist modeling. In this study, a dataset that was used to evaluate the safety effects of the dual application of shoulder and centerline rumble strips on two-lane rural highways was acquired. Only data from the before treatment period were used in a no-treatment effect analysis in order to compare the results of a Bayesian propensity score analysis to a frequentist propensity score analysis. Because no treatment was applied during the analysis period, it was assumed that there would be no treatment effect, or a crash modification factor equal to 1.0. The Bayesian propensity score matching method nominally outperformed the frequentist propensity score matching method in the largest sample and produced near-identical results in the medium sample, but neither method closely approximated the assumed, true crash modification factor in the small sample analysis. A simulation study is recommended to further study the effects of sample size and confounding factors when comparing the Bayesian and frequentist propensity score matching methods.

Are multiple speed cameras more effective than a single one? Causal analysis of the safety impacts of multiple speed cameras

Most previous studies investigate the safety effects of a single speed camera, ignoring the potential impacts from adjacent speed cameras. The mutual influence between two or even more adjacent speed cameras is a relevant attribute worth taking into account when evaluating the safety impacts of speed cameras. This paper investigates the safety effects of two or more speed cameras observed within a specific radius which are defined as multiple speed cameras. A total of 464 speed cameras at treated sites and 3119 control sites are observed and related to road traffic accident data from 1999 to 2007. The effects of multiple speed cameras are evaluated using pairwise comparisons between treatment units with different doses based on the propensity score methods. The spatial effect of multiple speed cameras is investigated by testing various radii. There are two major findings in this study. First, sites with multiple speed cameras perform better in reducing the absolute number of road accidents than those with a single camera. Second, speed camera sites are found to be most effective with a radius of 200 m. For a radius of 200 m and 300 m, the reduction in the personal injury collisions by multiple speed cameras are 21.4 % and 13.2 % more than a single camera. Our results also suggest that multiple speed cameras are effective within a small radius (200 m and 300 m).

Effects of transit signal priority on traffic safety: Interrupted time series analysis of Portland, Oregon, implementations

Transit signal priority (TSP) has been implemented to transit systems in many cities of the United States. In evaluating TSP systems, more attention has been given to its operational effects than to its safety effects. Existing studies assessing TSP's safety effects reported mixed results, indicating that the safety effects of TSP vary in different contexts. In this study, TSP implementations in Portland, Oregon, were assessed using interrupted time series analysis (ITSA) on month-to-month changes in number of crashes from January 1995 to December 2010. Single-group and controlled ITSA were conducted for all crashes, property-damage-only crashes, fatal and injury crashes, pedestrian-involved crashes, and bike-involved crashes. Evaluation of the post-intervention period (2003-2010) showed a reduction in all crashes on street sections with TSP (-4.5%), comparing with the counterfactual estimations based on the control group data. The reduction in property-damage-only crashes (-10.0%) contributed the most to the overall reduction. Fatal and injury crashes leveled out after TSP implementation but did not change significantly comparing with the control group. Pedestrian and bike-involved crashes were found to increase in the post-intervention period with TSP, comparing with the control group. Potential reasons to these TSP effects on traffic safety were discussed.

Causal inference framework for generalizable safety effect estimates

This study integrates a causal inference framework to the Empirical Bayes (EB) before-after method to develop generalizable safety effect estimates (i.e., crash modification factor (CMF)). The method considers approaches to estimate the average treatment effect for the treated (ATT), average treatment effect for the untreated (ATU), and average treatment effect (ATE). The current EB method is shown to estimate ATT while ATE is what is typically desired in traffic safety research. Modifications to the current EB method to estimate ATU and ATE are provided. The method is then applied to a dataset with a "no-treatment" scenario where the treatments were: 1) randomly selected and 2) selected based on crash history. Given the "no-treatment" outcome, it is known that the CMFs should have a value of 1 in order to be considered accurate. The standard negative binomial and mixed effects negative binomial regression models were applied in the analysis. It was found that, of the two regression methods, the ATE CMFs developed using the standard negative binomial were the most accurate. Finally, potential sources of bias in the EB method are discussed.

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.