## 2 Background  
Numerous network screening techniques have been used in practice or proposed in the literature with each having its own strengths and limitations. Compared with the large number of studies focused on the development of various hotspot identifications methods, considerably less research has been dedicated to evaluating the performance of various methods [10]. In this section, the major studies that evaluated the performance of different network screening techniques are summarized and presented. The Empirical Bayes estimate obtained from the EB method is the weighted sum of the expected value and the actual value, where the weights are determined based on the number of actual measurements for a certain variable. The potential for safety improvements (PSI) is estimated as the difference between the EB expected number of crashes and the predicted number of crashes found from the respective using safety performance function (SPF). A segment is said to have potential for improvement if the observed number of crashes is greater than the predicted number of crashes for that segment. [PERSON] et al. [11] examined the performance of three network screening methods for identifying potential sites for safety improvements on highways. The three network screening methods are Sliding Moving Window (SMW), Peak Searching (PS), and Continuous Risk Profile (CRP). The crash data from segmented sites were used to calculate the excess expected average crash frequency using Empirical Bayes adjustment based on two distinct sets of Safety Performance Functions (SPFs). These estimations from each approach were subsequently used to prioritize sites for safety investigations, comparing them against confirmed high crash location. The results revealed that the CRP method demonstrated the least occurrence of false positives, effectively identifying sites warranting safety investigations. A study by [PERSON] et al. [12] investigated the difference between network screening results based on multivariate and simple crash prediction models. This study first compared the list of segments using [PERSON]'s rank correlation coefficient between the two models. Secondly, this study assessed the equality of statistical distributions of potential for safety improvement (PSI) value, which was obtained as a difference between predicted crash frequency and EB estimate. The third comparison was the percentage of segments identified in both lists. The findings suggested that the results from both methods were comparable. A study conducted in Brazil [13] compared different safety performance measures and their practical limitations using a sample of signalized intersections located in Fortaleza City, Brazil. The safety performance measures evaluated in this study average crash frequency (ACF), crash rate (CR), Equivalent Property Damage Only (EPDO), Level of Service of Safety (LOSS), Excess Predicted Average Crash Frequency using Safety Performance Functions (SPFs), Expected Average Crash Frequency with EB Adjustments (EB), and Excess Expected Average Crash Frequency with EB Adjustment (EEB). The difference in rank between each safety performance measure assessed and the Excess Expected Average Crash Frequency with EB Adjustment (EEB) was used to evaluate the performance of the subjected method. The findings suggested that the most comprehensive measure was EEB, and that fundamental measures like crash frequency and crash rate displayed reasonably similar rankings. The effectiveness of several network screening techniques was examined in an Italian study [14] using a set of criteria. These included reliability in detecting hazardous sites over a period, efficiency in finding sites with poor safety performance, and consistency in ranking. The potential for improvements (PFI) was examined along with the crash frequency, equivalent property damage only (EPDO), crash rate, proportion method, and EB estimates for total and severe crashes.