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Posted: October 29th, 2022
Safety Performance Indicator for Signalized Intersections Review
Abstract: Road safety performance indicators have been the subject of research over the years. This follows continuous endeavors to make roads safer. Understanding safety performance indicators begins with the determination of what safety on the road needs. In recent years a significant potential has been recognized in improving roads safety and different approaches have been developed to improve road safety. Various models have been developed to help explain safety issues in road systems and to help in the identification of safety performance indicators. In this research, the three perspectives of safety performance models; crash based models, conflict models, and non-crash non-conflict models will be reviewed. The application of these models to other areas requires modification factors that are related to local areas. Most models are applicable to local areas and often they cannot be applied to the analysis of traffic safety in other regions or countries. The application of these models to other areas requires calibration to the local areas except the non-crash non-conflict model because it relies on the expert’s judgment.
Keywords: Safety performance model, Crash based model, Conflict model, Non-crash no-conflict model.
Accidents and traffic incidents often result from the unsafe operational conditions of road traffic systems. Consequently, understanding the potential areas of probable unsafe operational conditions of the road traffic system becomes the beginning point for researchers’ intent on making the road traffic system safer. This research presents the definition of safety to include all factors, events, situations, and incidents that present a potential threat to road users who include pedestrians, riders, drivers, and every other road user. This definition defines road safety from the perspective of actual crashes to include any situations considered as near-miss situations as well as conflicts on the roads.
One of the areas where researchers can reduce the impact of randomness in accidents and traffic incidents is in the signalized intersections considering the possibility of improving the road systems in order to reduce and eliminate road accidents and incidents (Essa & Sayed, 2018). Research confirms that intersections and weaving sections of the road are the weakest points in a road network (Hakim et al., 1991; Buckholz, 1993). It is at this point that many road incidents are likely to occur as a result of both road design factors and human factors. The high levels of traffic at intersections also increase the potential for road accidents and incidents at intersections.
A common issue of consideration is the case of drivers misunderstanding the signals on the signalized sections of the roads. This is particularly caused by the presence of multiple signals heads that direct drivers to different sections on the road network. When designing the signal systems at intersections, traffic engineers consider many factors including the expectations of the drivers. For instance, the lead-lag left turn phasing sequence is an important factor of signal operation enhancement. However, this does not always function in accordance with the expectations of the drivers and it presents high potential for conflicts at the intersections. These are some of the complexities experienced by engineers in the design of traffic management systems. The factors also influence the concept of road safety in such areas (Sawalha & Sayed, 2001).
The high number of possible causes of conflicts, accidents, and incidents on signalized intersections are an indicator of the variety of factors that affect safety (Persaud & Nguyen, 1998). Consequently, safety performance indicators for signalized intersections must put into consideration all the possible causes of conflicts, incidents, and accidents involving people and traffic in such intersections.
At the same time, more objective considerations concerning the design of the road are critical in influencing safety performance indicators in the signalized intersections. Road design factors would include geometrics, road makings and traffics signs, the conditions of pavements, the lighting conditions, and traffic characteristics. This perspective appreciates the fact that alterations to the road designs at intersections, as an enhancement to the signalized traffic lights systems is one of the key considerations in safety performance of signalized intersections (Asalor, 1984).
Empirical review of literature as presented here is important in demonstrating various safety models for signalized intersections. The following section focuses on safety models and frameworks that are used to explain the common causes of accidents and incidents on different sections of a road.
In this research, the three perspectives of safety performance models; crash based models, conflict models, and non-crash non-conflict models will be reviewed. The research is designed as a qualitative study with the method of collection of information being the systematic review of literature on safety performance models. The systematic review of literature allows for the identification of the major empirical findings about the three safety performance models thereby pointing out the strengths and weaknesses of each of the models. For application purposes, the systematic review also entails looking into how the different safety performance models are applicable for the observation of how signalized intersections operate with regards to safety. Additionally, the research methods also encompass a review of difference in applicability of the models under different conditions such as high traffic frequency and low traffic frequency as well as in the environments rural and urban settings.
Various theoretical models have been developed to help explain safety issues in road systems and to help in the identification of safety performance indicators. These models have guided traffic infrastructure planning in reduction of accidents and incidents on the roads.
This section will focus on three theoretical perspectives of the safety performance indicators in traffics systems. The three theoretical perspectives include crash-based models, conflict models, and non-crash non-conflict models. The models are briefly discussed in the following sections.
1) Crash Model
Crash based models draw inspiration from crash data. In the models, the likelihood of an accidents occurring in an area is considered from the perspective of the number of observed accidents in the same or similar area. The causes of such accidents are considered, and the data collected used in improvement and redesigning of the sections as well as in the development of new transport systems and road designs that seek to eliminate the causes of accidents.
There is a wide variety of factors that may lead to accidents or incidents at signalized intersections. From the review of literature, the possible cause of accidents could be human factors road design factor, or vehicle characteristics. The consideration of all such factors in the crash models helps in continuous improvements of the road models with the goal of eliminating the failures in the design of the roads and to reduce the accidents.
Crash frequencies naturally fluctuate over time. The implications are that short-term averages may be significantly different from the long-term data trends. Therefore, short-term average crash frequency may vary significantly compared to the long period average crash frequency. However, the randomness of accident occurrence indicates that short-term crash frequencies alone are not a reliable estimator of long-term crash frequency. Using the crash models, it is not possible to confidently state when and how an accident may occur.
Despite the influence of randomness and human factors elements in crash models, crash models are considered simple and informative in helping to identify the common causes of accidents and how they can be mitigated, mostly by focusing on the design of road design and human cause.
One of the reasons why crash models have remained significant despite the concerns of randomness is that the crash observations are not always independent of each other. Researchers have continued to model different road design related factors such as Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median width, urban/rural, and the section’s length, on the frequency of accident occurrence (Abdel-Aty & Radwan, 2000). Being highly subjective factors, the determination of how such factors influence the probability of accidents in different sections of the roads has been instrumental in influencing road safety. Particularly, these factors have been important in elimination of road design features that are characterized highly related to road accidents. This is one of the areas where crash models have been instrumental in definition of safety performance indicators on the roads.
Poisson models (Jones et al., 1991; Miaou and Lum, 1993) and negative binomial (NB) models (Miaou, 1994; Poch and Mannering, 1996; Abdel-Aty and Radwan, 2000) have been widely used to capture the relationship between traffic crashes and contributing factors. The models rely heavily on the availability of data and correct definition of the contributing factors. When correctly developed the models have been instrumental in establishing probabilities of accidents and incidents occurring at specified spatial points, during specified days of the week or even months, and have been key in identifying areas of safety improvement in road. These models have been key in making road sections safe.
There has been a major concern with the use of crash models where data on crashes is nearly inexistent. The zeros, when used in prediction models, become a challenge. Researchers have bene considering the use of zero-inflated negative binomial models thereby helping in the overcoming of the weaknesses associated with the zero incidents in the predictive models (Kumara & Chin, 2003). While the predictive power of zero-inflated negative binomial models may be low, it helps in the determination of the conditions linked to accidents. In a study conducted in Singapore it was determined that uncontrolled left-turn lane, permissive right-turn phase, existence of a horizontal curve, short sight distances, large number of signal phases, total approach volume, and left-turn volume may increase accident occurrence (Kumara & Chin, 2003).
As it appears in this analysis, the use of crash models is not as important in predicting accidents as it is in helping the identification of the factors linked to accidents. Essentially, the goal is to help in the identification of the safety performance indicators which is why this theoretical framework is considered essential in this research which focuses on safety performance indicators for signalized intersections. In application, crash models would be considered from the aspect of how they have been employed to improve safety at signalized intersections. Using Generalized Estimating Equations (GEE), Wang and Abdel-Aty (2008) showed that there are obvious differences in the factors that cause the occurrence of different left-turn collision patterns.
2) Conflict Models
Traffic conflicts are measures of accident potential and operational problems at a highway location. By definition, a traffic conflict is an observable situation in which two or more road users approach each other in space and time for such an extent that there is a risk of collision if their movements remain unchanged (Amundson and Hyden, 1977). Perkins and Harris define a traffic conflict as the interactions between vehicles, and such interactions can result in actions such as braking and changing direction of movement.
Early attempts by practicing highway engineers to diagnose operational or safety deficiencies included the simple technique of observing erratic driving, unsafe maneuvers and “near misses” at problem locations (Baker, 1972) This method was first formalized by McFarland and Moseley (1954) who observed “near misses”, judged as “emergency situations or critical incidents which could easily have led to an accident” experienced by intercity bus and truck drivers.
Besides “near misses” there are other methods used in identifying traffic conflicts. The traffic conflicts technique (TCT) was developed to objectively measure the accident potential of a highway location without having to wait for a suitable accident history to evolve. Using the virtual trajectories with the relative speeds and angular direction for each trajectory to predict the occurrence and the severity of accidents traffic conflict is evaluated by the term time to collide (TTC).
The first formalized procedure for identifying and recording traffic conflicts at intersections was developed by Perkins and Harris of General Motors Corporation in 1967. Major types of conflicts at intersections include rear-end, left-turn, cross-traffic, red-light violation, and weave conflicts. While crashes may be avoided during many such conflicts, the analysis of the conflicts may help in identifying possible areas of improvement in intersections (Guido, Astarita, Giofré & Vitale, 2011).
The use of conflict models advances the discourses on crashes by recognizing not just the crashes but also the incidents that could have easily resulted in accidents. It is a recognition that there are many incidences that go unreported because there was no actual traffic accident. This aspect ensures that a lot more information is captured in the models thereby providing more accuracy on reasons for accidents and traffic incidents (Flannery, Elefteriadou, Koza, & McFadden, 1998). By studying these aspects, it becomes possible to identify the safety performance indicators more accurately.
Traffic conflict analysis is applicable where data on crashes may not be available such as new road segment. Traffic conflict analysis helps in the identification of near miss instances which can be observed in relatively short period of time. A researcher does not need to wait for long period of time in order to collect data on crashes (Lu, Pan, & Xiang, 2008). The key areas of focus would include the cover conflicting points, number of conflicts, conflict rate, conflict distribution, and conflict forecasting models. The weaknesses of TCA include the fact that TCA uses judgment and determinations of traffic conflicts are more subjective. Different observers may provide different traffic conflict judgments. In addition, TCA is a time-consuming task.
There are different methods of collecting data on traffic conflicts. These include field observation, simulations, and video-recording. Field observations considered to be the first and the basic method of data collection, where the data collector follows the general role of the conflict which is recording all the “near miss” events. The decision to record the near miss situation may vary from person to person. Most of the recordings shows that the results were inconsistent, techniques for analyzing the relevant data either are lacking altogether or differ markedly from study to study and successive reports omit a critical assessment of earlier work. One of the most important aspects to consider when utilizing conflict data is the reliability of data collected by observers (Eisele & Frawley, 2005). There are many factors which will account for variation in conflict counts including awareness, experience, and different driving attitudes of the observers, location of the observer at the site, and traffic volumes. Field observers are not only expensive, but inter- and intra-observer variability is a common challenge for the repeatability and consistency of results (William, 1972).
The second method is simulations. In recent years, traffic conflict simulation models have attempted to analyze traffic safety performance at roadway intersections. (Pirdavani, Brijs, Bellemans & Wets, 2010) considered what they considered micro-simulation in understanding safety indicators in intersections. Model development for traffic conflict simulation needs long-term data accumulation, and simulation model parameters could change as environmental conditions change, limiting its applicability in real situations (Yuan et al., 2008). Focusing on simulation of conflicts for both three leg and four leg unsignalized intersections, Sayed et. al (1994) considered traffic conflicts as critical-event traffic situations and the effect of driver and traffic parameters on the occurrence of conflicts. The simulation was found useful for assessing safety performance and feasible solutions for other unsignalized intersections.
Huang and Pant (1994) simulated measures of effectiveness of traffic control at a high-speed intersection. The model considered three critical elements which included probability of being caught in a dilemma zone, speed of a vehicle in different segments of the intersection approach, and vehicle conflict rate. The inclusion of the three components of the model showed the wide variety of issues that should be considered in safety performance analysis of intersections. Similarly, Rao and Regaraju (1998) focused on intersections and showcased the variety of issues that cause conflicts at intersections.
Persaud and Mucsi (1995) simulating accidents in rural areas under different conditions used regression package that allowed the assumption of a negative binomial error structure. Regression models were calibrated for the different combinations of time periods (24-hour, day hours, and night hours) and geometric (roadway and shoulder width) characteristics. The research showed that the effect of day/night conditions is different for single-vehicle and multivehicle accidents. For single-vehicle accidents, the accident potential was higher during the night, whereas for multivehicle accidents the opposite is true. The simulation indicated the importance of differentiating between single-vehicle and multivehicle accidents and day/night conditions (Persaud & Mucsi, 1995). Importance of mixed conditions was also highlighted in Li, Yue & Wong (2004).
The last method is video-recording. Video sensors are selected as the primary source of data due to advantages of richness in details, inexpensiveness, and universal usage for monitoring purposes. The automated extraction of road users’ positions from video data using techniques in the discipline of computer vision has been advocated as a resource-efficient and potentially more accurate alternative (Ismail, Sayed, Saunier, 2009b). Qu, Kuang, Oh, & Jin, 2014; and Cunto 2008 noted the importance of video recording in observing microscopic indicators.
3) Non-crash non-conflict model
Non-crash non-conflict model is the last theoretical framework for consideration. As the title suggests, the model does not rely on traffic crashes or traffic conflicts data. Instead, the method relies more on professional expertise and experience of road and transport systems’ designers. An important aspect of this framework is that it emphasizes the professional skill and expertise of the analysts and looks into aspects of design that may be invisible to the inexperienced eye (Oh, Park & Ritchie, 2006).
This type of safety evaluation is conducted by the personal judgments of traffic safety experts. In most cases those traffic safety experts are road designers because the focus is on the dimensions that are related directly to the road safety such as, the intersection geometry, characteristics of road and the type of traffic control.
Usually, non-crash- and non-conflict-based evaluation methods can provide a safety evaluation for a roadway facility in a relatively short time; these methods are relatively easy to implement in real applications. This type of approach has advantages; it is low cost, has high efficiency, and is less time-consuming. Furthermore, this type of approach can be better used by field safety engineers to find potential safety problems and corresponding countermeasures can be implemented in a short time so that possible traffic safety problems can be prevented (Oh, Park & Ritchie, 2006).
In evaluation of signalized intersections, the installation of traffic lights systems, width and length, the shoulders, and locations of turning points as well as crossing points become the important indicators that the expert focuses on. The information from the non-crash non-conflict analysis may be analyzed together with the data from the other two models discussed above.
Results and Discussion
The study drew findings from systematic review of literature. The study found differences in the applicability of the safety performance models under different conditions. For all models, the applicability depends on the road conditions including the frequency and intensity of traffic on the roads. High complexity on the road designs and traffic interactions heavily influence the application of the non-conflict non-crash models since the environment of study requires high levels of expert judgment.
There is no single model that is designed for general application. The application of crash based models and conflict models requires modification factors that are related to local areas. Most models are applicable to local areas and often they cannot be applied to the analysis of traffic safety in other regions or countries. The application of these models to other areas requires calibration to the local areas except the non-crash non-conflict model because it relies on the expert’s judgment.
The conflict models are faced by the subjective nature of determining what comprises a conflict. Prior to application, the researcher must clearly define what characterizes a conflict. The types and nature of conflicts must be accurately defined. For statistical rigor, the defined conflicts or near-miss instances must attain a specified level of statistical significance. The goal is to apply scientific approaches in defining conflicts and to eliminate the subjective views of the researchers.
Lastly, in the case of crash models the research must contain measures of eliminating random factors causing accidents. The goal is to ensure that all causes of accidents are positively identified and accurately simulated. The use of video capture of data is the most recommended strategy for the identification of the crashes. For scientific rigor, the data must be collected at the identified location for lengthy periods of time mostly exceeded one year in order to ensure that all crashes are captured and analyzed objectively rather than by using post-crash information that is subjective.
Summary and Conclusion
Understanding safety performance indicators begins with the determination of what road safety needs. Complex analyses involve the use of safety performance models. Those models must put into consideration all the possible causes of conflicts, incidents, and accidents involving people and traffic in such intersections.
Three perspectives of safety performance models have been used to enhance intersection safety; crash-based models, conflict models, and non-crash non-conflict models.
The crash-based model considers the likelihood of an accidents occurring in an area is considered from the perspective of the number of observed accidents in the same or similar area. The use of the model is not as important in predicting accidents as it is in helping the identification of the factors linked to accidents.
Traffic conflict analysis helps in the identification of near miss instances which can be observed in relatively short period of time and it does not need to wait for long period of time in order to collect data on crashes.
Non-crash non-conflict model’s evaluation is conducted by the personal judgments of traffic safety experts. In most cases those traffic safety experts focus on intersection geometry, characteristics of road and the type of traffic control.
Most models are applicable to local areas and often they cannot be applied to the analysis of traffic safety in other regions or countries. The application of these models to other areas requires calibration to the local areas except the non-crash non-conflict model because it relies on the expert’s judgment.
Overall, the study established the need to consider each of the models on merit based on the conditions of roads where the study will occur, the level of traffic, and the duration of the study. The only model that is not subjected to the conditions is the non-conflict non-crash models.
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