Left-turn Crashes Research Articles

Safety Evaluation of the Combined Effect of Offset Left-Turn Lanes and Flashing-Yellow-Arrow Signals at Signalized Intersections on Multilane Divided Highways in Alabama Using the Empirical Bayes Method

The present study conducted an empirical Bayes (EB) before–after analysis to investigate the combined effects of the offset left-turn lanes and flashing-yellow-arrow (FYA) signals implemented at signalized intersections on multilane, divided highways in Alabama. A total of 35 signalized intersections were selected for the EB safety analysis. Among them, 30 intersections were classified as a reference group and five were classified as a treatment group. The reference group includes intersections which have not undergone any left-turn treatments from the period of 2010 to 2020, while the treatment group includes those improved with offset left-turn lanes and FYA signal implementation concurrently during years in that period. Safety performance functions were developed with data collected at reference-group intersections to predict crashes at such intersections under a no-treatment scenario. A study focus was then given to understanding the change in crash frequency before and after the combined treatments for the treatment-group intersections, using the EB method. Results show that the combined left-turn treatments (i.e., implementing offset left-turn lanes coupled with FYA signals) could reduce different types of crashes effectively. There was a substantial reduction of 27% in total crashes (crash modification factor [CMF] = 0.73), a 43% decrease in left-turn crashes (CMF = 0.57), and a 36% reduction in total injury crashes (CMF = 0.64) after the treatments. These findings were supported by their respective standard errors, which are 0.060, 0.101, and 0.106 for total, left-turn, and total injury crashes, respectively, and all the CMFs are statistically significant at 95% confidence intervals.

Left-turn crashes and motorcycle safety

Objective To provide updated statistics on crashes in which another vehicle turns left in front of an oncoming motorcycle and discuss the potential of left turn assist technology. Methods Motorcycle driver involvements in 2-vehicle fatal and police-reported crashes during 2017-2021 were tabulated by crash type, with a focus on crash types involving vehicles turning. Results Crashes in which another vehicle turned left in front of an oncoming motorcycle were, by far, the most frequent type of fatal 2-vehicle motorcycle crash, at 26%. Conclusion There is a large opportunity to reduce harm by specifically addressing crashes in which another vehicle turns left in front of an oncoming motorcycle – ideally using a variety of countermeasures simultaneously.

A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections

The severity of right-turn crashes (or left-turn crashes for the roads in the US) at signalised intersections tends to be high because of the relatively high conflicting speeds and angle of impact. However, right-turn crash injury severity at signalised intersections was not sufficiently studied. In particular, the effects of signal control strategies on crash injury severity are not known. This study developed crash injury severity models for right-turn crashes at signalised intersections with a novel approach of linking crashes with signal strategies which enabled assessing the effects of signal strategies on crash injury severity. The study provided a comprehensive understanding of the impacts of signal strategies, intersection geometry and traffic factors on crash injury severity of right-turn crashes at signalised intersections. Crash injury severity models were estimated with crash data from 221 signalised intersections in Queensland from 2012 to 2018. To address the hierarchical structure of crash data, two-level hierarchical Multinomial Logit models were applied, hypothesising that the first level includes individual crash characteristics while the second level includes intersection characteristics. The applied hierarchical model accounts for the correlation among crashes within intersections. Results showed that crashes during Lagging right-turn and Diamond overlap turns are likely to be more severe than other signal strategies at intersections, with the Lagging right-turn signal being the most hazardous. The results also illustrate that the probability of severe injuries increases with the number of conflicting lanes, whereas the corresponding probability decreases with the occupancy of the conflicting lane.

A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration

Crash risk models relying on total crash counts are limited in their ability to extract meaningful insights regarding the context of crashes and to identify effective remedial measures. In addition to the typical classification of collisions noted in the literature (e.g., angle, head-on and rear-end), crashes can also be categorised according to vehicle movement configurations (Definitions for Coding Accidents or DCA codes in Australia). This classification presents an opportunity to extract useful insights into road traffic collision causes and contributing factors that are highly contextual. With this aim, this study develops crash-type models by DCA crash movement, with a focus on right-turn crashes (equivalent to left-turn crashes for right-hand traffic) at signalised intersections using a novel approach for linking crashes with signal control strategies. The modelling approach with contextual data enables quantification of the effect of signal control strategies on right-turn crashes, offering potentially unique and novel insights into right-turn crash causes and contributing factors.Crash-type models are estimated with the crash data of 218 signalised intersections in Queensland from 2012 to 2018. Multilevel (Hierarchical) Multinomial Logit Models with random intercepts are employed to capture the hierarchical influence of factors on crashes and unobserved heterogeneities. These models capture upper-level influences on crashes from intersection characteristics and lower-level influences from individual crash characteristics. The models specified in this way account for the correlation among crashes within intersections and influences on crashes across spatial scales. The model results reveal that the probabilities of the opposite approach crash type are significantly higher than the same direction and adjacent approach crash types for all right-turn signal control strategies at intersections except the split approach, for which the opposite is true. The results also suggest that the number of right-turning lanes and occupancy in conflicting lanes are positively associated with the likelihood of crashes for the same direction crash type.

A before-after evaluation of protected right-turn signal phasings by applying Empirical Bayes and Full Bayes approaches with heterogenous count data models

Right-turn crashes (or left-turn crashes for the US or similar countries) represent over 40 % of signalized intersection crashes in Queensland, Australia. Protected right-turn phasings are a widely used countermeasure for right-turn crashes, but the research findings on their effects across different crash types and intersection types are not consistent. Methodologically, the Empirical Bayes and Full Bayes techniques are generally applied for before-after evaluations, but the inclusion of heterogeneous models within these techniques has not been considered much. Addressing these research gaps, the objective of this study is to evaluate the effectiveness of protected right-turn signal phasings at signalized intersections employing heterogeneous count data models with the Empirical Bayes and Full Bayes techniques. In particular, the Empirical Bayes approach based on random parameters Poisson-Gamma models (simulation-based Empirical Bayes), and the Full Bayes approach based on random parameters Poisson-Lognormal intervention models (simulation-based Full Bayes) are applied. A total of 69 Cross intersections (with ten treated sites) and 47 T intersections (with six treated sites) from Southeast Queensland in Australia were included in the analysis to estimate the effects of protected right-turn signal phasings on various crash types. Results show that the change of signal phasing from a permissive right-turn phasing to the protected right-turn phasing at cross and T intersections reduces about 87 % and 91 % of right-turn crashes, respectively. In addition, the effect of protected right-turn phasings on rear-end crashes was not significant. The heterogenous count data models significantly address extra Poisson variation, leading to efficient safety estimates in both simulation-based Empirical Bayes and simulation-based Full Bayes approaches. This study demonstrates the importance of accounting for unobserved heterogeneity for the before-after evaluation of engineering countermeasures.

Engineering Countermeasures for Left Turns at Signalized Intersections: A Review

Left turn crashes can impact the safety of the drivers due to the speed and angle at which they occur. Left turns are specifically reported to affect older drivers more than the other types of crashes. This paper provides a review of the existing engineering countermeasures that have been evaluated to improve driver safety at left turns. Twentyeight studies on left turn signal displays (protected left turns, flashing yellow arrow, and digital countdown timers), intersection geometry (offset left turn lanes, diverging diamond interchange, roundabouts, exit lanes for left turn, left turn bay extension, and contraflow left turn lanes), and driver warning systems (infrastructure warning systems, and in-vehicle warning systems) are reviewed. Eighteen studies were evaluated in the field, nine in laboratory environments, and one online. All countermeasures demonstrated varying levels of effectiveness. We found protected left turns, roundabouts, and warning systems to be the most effective engineering countermeasures. Advantages and disadvantages of each countermeasure and research shortcomings of the evaluation studies are discussed. Review findings may help practitioners and researchers guide more effective countermeasures for left turns for older drivers.

Safety and Operational Effectiveness of Protected Only Versus Protected/Permitted Left-Turn Signal Phase

Roadway intersection fatalities account for around 24% of total traffic fatalities each year, with left-turning vehicles associated with most of these crashes. To manage left-turning movements, various left-turn signal phases like protected only (PO), protected/permitted left-turn (PPLT), and flashing yellow arrow (FYA) are currently in use. However, their overall effectiveness has not been well established. The paper evaluates the safety and operational effectiveness of PO over PPLT left-turn signal phase using sample intersections from Louisiana. A total of 151 signalized intersections with uniform left-turn signal phases at all of their approaches were selected. Crash data recorded over five years at those intersections comprising a total of 13,278 crashes, including 1,275 left-turn crashes, were collected. A crash modification factor (CMF) was developed using the negative binomial model (NBM) to assess the safety benefits of PO over PPLT. The Highway Capacity Manual 6th Edition (HCM 6) control delay field measurement technique was used for the operational analysis. The study first developed CMFs for total crashes and only left-turn crashes at various severity levels. CMF of PO over PPLT revealed that PO significantly reduced all severity levels of left-turn crashes by more than 50%, however, it was only able to reduce injury and fatal total crashes. Second, the study found average control delay at PO (50.69 s/vehicle) significantly higher than at PPLT (46.04 s/vehicle). The difference in delay was only significant during the morning peak hour. Overall, from both safety and operation perspectives, the results indicate that PO performs better than PPLT.

Using SHRP2 NDS data to examine infrastructure and other factors contributing to older driver crashes during left turns at signalized intersections

Drivers age 65 and over have higher rates of crashes and crash-related fatalities than other adult drivers and are especially over-represented in crashes during left turns at intersections. This research investigated the use of SHRP2 Naturalistic Driving Study (NDS) data to assess infrastructure and other factors contributing to left turn crashes at signalized intersections, and how to improve older driver safety during such turns. NDS data for trips involving signalized intersections and crash or near-crash events were obtained for two driver age groups: drivers age 65 and over (older drivers) and a sample of drivers age 30−49, along with NDS pre-screening and questionnaire data. Video scoring of all trips was performed to collect additional information on intersection and trip conditions. To identify the most influential factors of crash risk during left turns at signalized intersections, machine learning and regression models were used. The results found that in the obtained NDS dataset, there was a relatively small volume of crashes during left turns at signalized intersections. Further, model results found the statistically significant variables of crash risk for older drivers were associated more with health and cognitive factors rather than the infrastructure or design of the intersections. The results suggest that a study using only SHRP2 NDS data will not lead to definitive findings or recommendations for infrastructure changes to increase safety for older drivers at signalized intersections and during left turns. Moreover, the findings of this study indicates the need to consider other data sources and data collection methods to address this critical literature gap in older driver safety.

Examining driver injury severity in left-turn crashes using hierarchical ordered probit models

Objective Few existing studies in the literature devoted efforts to examine the driver injury severity in left-turn crashes. To fill this research gap, this paper aims to provide a comprehensive study of the contributing factors of left-turn crashes and the corresponding injury severities. Methods The hierarchical ordered probit (HOPIT) model is first applied to study driver injury severity in left-turn crashes. The HOPIT model can overcome the limitations of traditional ordered probit models since its thresholds are always positive and ordered. It is a function of unique explanatory parameters that do not necessarily affect the ordered probability outcomes directly. Considering the driving condition during the wintertime could be significantly different from other seasons, this study divided the overall crash dataset into “winter” and “other-season” subsets based on the temperature, snowing condition, and other factors. Results With the “other-season” dataset, results demonstrated that contributing factors, such as young drivers, male drivers, clear, light, and ramp intersection with crossroad, are associated with a decrease in injury severity. On the contrary, factors like drug, alcohol, disregard traffic control device, high-speed limit, the protected left-turn signal, etc., are related to an increase in injury severity. In winter, results revealed that only nine contributing factors are significant to the left-turn crash. Alcohol, disregard traffic control device, nighttime, high-speed limit, head-on collision, and state road are associated with an increase in injury severity. Also, two-vehicle involved, snow, ramp intersection with crossroad are related to a decrease in injury severity. Conclusions The HOPIT model is applied to examine contributing factors of left-turn crashes and the corresponding injury severity, based on left-turn crash records from 2010 to 2017 in Utah. Eighteen significant factors of left-turn crash injury severity are identified in the overall dataset. In seasons rather than winter, the significant factors are almost the same as that of the entire year. In the winter, less significant factors and different patterns are found compared with the overall crashes.

Left turn crash risk analysis: Development of a microsimulation modeling approach

The use of traffic simulation to analyze complex transportation issues has become common practice in transportation engineering. The further application of microsimulation to the analysis of traffic safety in a systematic, rigorous, and controlled fashion is becoming increasingly viable as simulation models improve and tools for quantifying surrogate safety measures become readily accessible. Using a calibrated traffic microsimulation model and surrogate safety assessment model analysis, this paper examined how the risk for left-turn crashes varied as traffic conditions changed at a signalized intersection.Safety impacts for 750 unique combinations of intersection geometry, traffic, and signal timing parameters were simulated and the number of left-turn conflicts per hour noted. Results of the simulation analyses were used to develop statistical models that expressed the risk of occurrence of a left-turn crash during a given hour as a function of the left-turn phasing mode and prevailing conditions during that hour. The study was motivated by the recent widespread application of the flashing yellow arrow (FYA) which provides the opportunity to vary left-turn phasing mode by time of day—potentially leading to more efficient traffic operations at signalized intersections. In this regard, the study addresses a basic need for tools that predict how the risk for left-turn crashes might vary at a more disaggregated level than that provided by existing crash prediction models, which typically predict yearly totals of left-turn crashes, often based on annual average daily traffic volumes. Potential application of the model to the implementation of a time-variable safety-based left-turn phasing selection scheme using FYA was successfully demonstrated.

Use of Technology-Based Strategies for Older Driver Risk Mitigation

Older drivers present a unique challenge to transportation system throughout the world. A need for balancing mobility needs and safety issues for elderly drivers has been well documented. Past research has shown that older drivers are more involved in left turn crashes and crashes involving complex maneuvers, in comparison to other driver age groups. Recent integration of mobile technology solutions and increased familiarity of older driver population with technology, especially in younger cohorts, presents a unique opportunity to develop potential solutions. The objective of this research is to identify automated route finding that may be used by older drivers to identify “safest” routes through increased risk assessment and guidance on route choice, time of travel and required maneuvers at intersections, to reduce the overall crash representation of this driver population. This research is based on specific case study and it used the ArcGIS Network Analyst extension to develop a scoring for routes and optimize them based on minimizing exposure to turns while balancing route length. The results showed that it is feasible to identify safer routing choices for older drivers. These “safe” routes are also significantly differentiated from routes optimized by travel time alone. Moreover, safe routing may be quantified easily from readily available information sources, such as by limiting or eliminating the need to make a left turn.

From univariate to bivariate extreme value models: Approaches to integrate traffic conflict indicators for crash estimation

This study develops bivariate extreme value models to integrate traffic conflict indicators for crash estimation. Based on video data collected from four sites of two signalized intersections, the automated traffic conflict analysis system was used to extract the TTC and PET between left-turn vehicles and through vehicles. Bivariate Generalized Extreme Value (BGEV) and Bivariate Generalized Pareto (BGP) models that jointly used the two conflict indicators were then developed, and the number of crashes were derived from the estimated model parameters. Univariate Generalized Extreme Value (UGEV) and Univariate Generalized Pareto (UGP) models were also applied using individual conflict indicators. The developed bivariate models and univariate models were evaluated by comparing model estimated crashes to observed left-turn crashes. The results show that the BGP model performed the best, followed by the BGEV model, UGP model, and UGEV model. It is found that crash estimates from univariate models based on PET and TTC are underestimated and overestimated, respectively. The bivariate models integrating the two indicators improve the crash estimation accuracy and precision. The BGP model outperforming the BGEV model is likely due to the former allowing more efficient use of the collected data. Overall, it is concluded that the bivariate extreme value modeling approach that is capable of integrating traffic conflict indicators with clear boundaries between traffic conflicts and crashes is more promising for crash estimation. Moreover, with the emerging automated traffic conflict analysis and connected vehicle techniques that facilitate the indicators extraction, the bivariate approach can be readily applied to provide accurate crash estimations for proactive road safety analysis.

Crash Modification Factors for the Flashing Yellow Arrow Treatment at Signalized Intersections

This paper presents the results of an evaluation of the flashing yellow arrow (FYA) treatment using data from signalized intersections in Nevada, North Carolina, Oklahoma, and Oregon. The evaluation method was an empirical Bayes before–after analysis. The treatments were divided into seven categories depending on the phasing system in the before period (permissive, protected–permissive, or protected), phasing system in the after period (FYA permissive or FYA protected–permissive), the number of roads where the FYA was implemented (one road or both roads), and the number of legs at the intersections (three or four). The first five treatment categories involved permissive or protected–permissive phasing in the before period. Intersections in these five treatment categories experienced a reduction in the primary target crashes under consideration: left turn crashes and left turn with opposing through crashes. The reduction ranged from 15% to 50%, depending on the treatment category. Intersections that had at least one protected left turn phase in the before period and had FYA protected–permissive left turn phase in the after period experienced an increase in left turn crashes and left turn with opposing through crashes, indicating that replacing a fully protected left turn with FYA will likely cause an increase in left turn crashes.

Can post encroachment time substitute intersection characteristics in crash prediction models?

IntroductionTransportation safety analyses have traditionally relied on crash data. The limitations of these crash data in terms of timeliness and efficiency are well understood and many studies have explored the feasibility of using alternative surrogate measures for evaluation of road safety. Surrogate safety measures have the potential to estimate crash frequency, while requiring reduced data collection efforts relative to crash data based measures. Traditional crash prediction models use factors such as traffic volume, sight distance, and grade to make risk and exposure estimates that are combined with observed crashes, generally using an Empirical Bayes method, to obtain a final crash estimate. Many surrogate measures have the notable advantage of not directly requiring historical crash data from a site to estimate safety. Post Encroachment Time (PET) is one such measure and represents the time difference between a vehicle leaving the area of encroachment and a conflicting vehicle entering the same area. The exact relationship between surrogate measures, such as PET, and crashes in an ongoing research area. MethodThis paper studies the use of PET to estimate crashes between left-turning vehicles and opposing through vehicles for its ability to predict opposing left-turn crashes. By definition, a PET value of 0 implies the occurrence of a crash and the closer the value of PET is to 0, the higher the conflict risk. ResultsThis study shows that a model combining PET and traffic volume characteristic (AADT or conflicting volume) has better predictive power than PET alone. Further, it was found that PET may be capturing the impact of certain other intersection characteristics on safety as inclusion of other intersection characteristics such as sight distance, grade, and other parameters result in only marginal impacts on predictive capacity that do not justify the increased model complexity.

Effectiveness of Experimental Left-Turn Sign Usage in Terms of Crashes and Analyzing Severity of Left Turn Crashes in Alaska

Background: Ensuring safety in the roadways is one of the critical tasks as crashes generally bourgeoning rapidly all over the world. Because the turning movements of vehicles are important to motorist safety, Alaska Department of Transportation and Public Facilities (DOT & PF) experimented with left-turn traffic signs at six signalized intersections in the city of Anchorage, Alaska. Objective: The study aims at evaluating effectiveness of the left turn signs and analyze severity of left turning crashes. Methods: Preliminary statistical analysis and Binomial Logistic regression are applied for analyzing the data. Results: Preliminary analysis of before (1994-1998) and after (2005-2012) crash frequency data showed a reduction of crashes at four of the six intersections studied per 1000 entering vehicles. An analysis of crashes per year indicated that left-turn crashes at five intersections declined significantly after 2009, showing an improvement in safety. Pavement markings with signage were vitally important in contributing to improve compliance rates. Covering the pavement markings with snow and ice in Alaska for long periods signify the importance of installation of left turn signage to reduce crashes at studied intersections. Logistic regression analysis revealed that improper turn and lane usage, careless driving and angle type crashes were the most significant factors contributing to PDO crashes. Dry pavement conditions as well as almost all day except evening peak and off peak period influence PDO crashes in Alaska. Conclusion: Left-turn signs were effective in reducing crashes, and proper maintenance of road markings as well as driver awareness increased the effectiveness of signage and level of safety for left turning vehicles.

Examination of the reliability of the crash modification factors using empirical Bayes method with resampling technique

There have been plenty of studies intended to use different methods, for example, empirical Bayes before–after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before–after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle+Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites.

The influence of attention allocation and age on intersection accidents

Many severe accidents occur in urban areas. As part of the research project UR:BAN, this study investigated the causes of driver errors (e.g., inadequate attention allocation) in urban areas when turning left at intersections. As intersection accidents are especially difficult for older drivers, differences between older and younger drivers were examined as well. In a first step, accident protocols of left turn crashes with pedestrians and bicyclists were analysed in detail, since they are the most dangerous ones. Characteristics of the oncoming traffic and the location of crossing bicyclists and pedestrians were identified as possible causes. Accordingly, critical scenarios were implemented in a static driving simulator, varying the characteristics of the oncoming traffic, the direction and location of crossing vulnerable road users. These factors were examined in a within-subject design, with two different aged groups of participants (12 aged 20–35y, 12 aged 65+y; between-subjects factor).The results revealed that the presence of the oncoming traffic, which was assumed to capture the drivers’ attention, did not lead to more accidents with vulnerable road users. However, this may be because many drivers waited until the oncoming traffic had passed. Unexpectedly, older drivers had fewer accidents. This may be explained by the more cautious behaviour of older drivers, who drove significantly slower and waited significantly longer at the stop line before turning. Further analyses showed that a more cautious behaviour, independently of the age, predicted accident avoidance better than attention allocation. From these results, warning systems for younger and older drivers, especially for those not driving cautious, need to be developed. This idea will be tested in future studies introducing different warning concepts.

Developing Framework to Test Driving Performance at Left-Turn Movement with In-Vehicle Advance Collision Warning Message

Left-turning traffic without a protected left-turn signal is one of the major safety concerns at urban intersections. Though an average of only l0% - 15% of all approaching traffic turns left, significantly a large proportion of left-turn crashes occur involving 21% of all intersection fatal crashes. Where traditional safety countermeasures of signal timing-phasing and use of flashing yellow light have reportedly failed to significantly reduce the rate of crashes, an in-vehicle advance collision warning message can be helpful to reduce left-turn collisions at intersections. In this study, an in-vehicle audio warning application has been designed by providing two safety warning messages (Advance Warning Message and Safe Left-turn Maneuver Message) under the vehicle to vehicle (V2V) communication system, which is triggered based on the acceptable gaps of oncoming opposing vehicles for a safe left-turn. A driving simulator test has been conducted with 30 participants to investigate the impacts of warning messages on performance measures such as speed and acceleration profiles, collision records, brake reaction distance, and intersection clearance time. Statistical results showed that with the help of these messages, all participants were able to reduce speeds and accelerations and chose suitable gaps without potential conflicts. Moreover, the results of questionnaire analysis provide a positive acceptability especially for the Safe Left-turn Maneuver Message. Based on the performance measurements, this type of safety warning messages can be recommended for possible real-road tests for practical applications.

Safety Effects of Protected Left-Turn Phasing at Signalized Intersections: An Empirical Analysis

Left-turn crashes occur frequently at signalized intersections and often lead to severe injuries. This problem can be addressed through the implementation of protected left-turn signals. This gives left-turning vehicles the right to enter the intersection free from conflict with opposing drivers and pedestrians. The present study analyses the effect of this measure on crash occurrence. The study included 103 signalized intersections with left-turn signals in highways in Flanders, Belgium, of which 33 received only changes in the signal control and 70 also received additional changes. The effect on traffic safety is analyzed through an Empirical Bayesian before-and-after study on crashes, in which general trend effects and regression-to-the-mean are controlled. On the 33 intersections that received only changes in the signal control, the number of injury crashes decreased significantly (−46%, 95% CI (−36%; −55%)) during the after-period. This was mainly attributable to a decrease in left-turn crashes: −60%, 95% CI (−39%; −74%). The number of rear-end injury crashes did not change significantly after the implementation of a protected left-turn signal. A larger effect was identified for more severe crashes (involving serious injuries and fatalities) as compared with crashes resulting in lighter injuries: −66%, 95% CI (43%; −80%). Furthermore, the effect of left-turn phasing on the number of injured car occupants, cyclists, moped riders and motor cyclists was examined, and favorable effects were found for each of these groups.

Estimating safety performance trends over time for treatments at intersections in Florida

Researchers have put great efforts in quantifying Crash Modification Factors (CMFs) for diversified treatment types. In the Highway Safety Manual (HSM), CMFs have been identified to predict safety effectiveness of converting a stop-controlled to a signal-controlled intersection (signalization) and installing Red Light Running Cameras (RLCs). Previous studies showed that both signalization and adding RLCs reduced angle crashes but increased rear-end crashes. However, some studies showed that CMFs varied over time after the treatment was implemented. Thus, the objective of this study is to investigate trends of CMFs for the signalization and adding RLCs over time. CMFs for the two treatments were measured in each month and 90-day moving windows respectively. The ARMA time series model was applied to predict trends of CMFs over time based on monthly variations in CMFs. The results of the signalization show that the CMFs for rear-end crashes were lower at the early phase after the signalization but gradually increased from the 9th month. On the other hand, the CMFs for angle crashes were higher at the early phase after adding RLCs but decreased after the 9th month and then became stable. It was also found that the CMFs for total and fatal/injury crashes after adding RLCs in the first 18 months were significantly greater than the CMFs in the following 18 months. This indicates that there was a lag effect of the treatments on safety performance. The results of the ARMA model show that the model can better predict trends of the CMFs for the signalization and adding RLCs when the CMFs are calculated in 90-day moving windows compared to the CMFs calculated in each month. In particular, the ARMA model predicted a significant safety effect of the signalization on reducing angle and left-turn crashes in the long term. Thus, it is recommended that the safety effects of the treatment be assessed using the ARMA model based on trends of CMFs in the long term after the implementation of the treatment.