Time Reliability Research Articles

Development and validation of a novel Barrett's oesophagus patient reported outcome measure (B-PROM)

Patients with Barrett's oesophagus (BO) carry significant cancer worry, burden of symptoms, and lack disease-specific knowledge. Currently there is no validated BO patient reported outcome measure (PROM) to measure these factors for use in clinical practice and research, hence the aim of this study was to devise a novel, validated BO-specific tool, B-PROM. Literature review, quantitative and qualitative research informed the initial item generation. The item bank was refined through a modified Delphi process between May and August 2021. The PROM was then tested through cognitive interviews and validated via multicentre testing between September 2021 and February 2023 with the aim to create a succinct tool which addresses the key important factors to BO patients and has strong psychometric properties. B-PROM covers key themes of disease-specific knowledge, trust in clinicians, burden of symptoms, cancer worry and burden of surveillance. Validation results from 387 participants (response rate 40.8%) showed 93.3% of participants completed >95% of B-PROM. All individual items scored a completion rate of >95%. Mean completion time was 5mins 34s for a sample group. Nineteen items showed a ceiling effect, 3 items showed a floor effect. Internal consistency overall demonstrated a Cronbach Alpha of 0.846, while predetermined subsections showed Cronbach alphas of 0.335, 0.718, 0.736, and 0.896. Inter-item analysis found 2 pairs of items with strong correlation, with only 6 items correlating weakly. Item-total correlation showed 19 items correlated well. Exploratory Factor analysis (EFA) with principal component analysis produced 5 components with Eigenvalues >1 of which 4/5 had satisfactory Cronbach alphas. Test-retest reliability showed no significant differences across single and average measures (p ≤ 0.001). B-PROM is the first BO-specific PROM to be systematically evaluated. Validation findings show strong internal consistency, short completion time, low missingness and excellent test-retest reliability. Medtronic Limited ISR-2016-1077.

Travel Time Reliability Estimation in Urban Road Networks: Utilization of Statistics Distribution and Tensor Decomposition

The travel time reliability (TTR) is crucial for evaluating the reliability of road networks, but real traffic data is often incomplete and sparse. This study validates that road network TTR conforms to a normal distribution and devises a quantification approach for road network TTR. Two reliability estimation methods are tailored for two data sources: section detectors and mobile detectors. Simulation experiments have confirmed the effectiveness of these methods. The study emphasizes that the TTR estimation method using traffic section data (S-TTR), which is based on the verified normal distribution assumption, maintains average absolute errors below 10%. On the other hand, the TTR estimation method that utilizes sparse trajectory data (T-TTR), which relies on tensor decomposition, proficiently fills in all missing data with an average error of 0.0059.

Psychometric Evaluation of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Mixed Methods Study.

With the rapid aging of the global population, the prevalence of mild cognitive impairment (MCI) and dementia is anticipated to surge worldwide. MCI serves as an intermediary stage between normal aging and dementia, necessitating more sensitive and effective screening tools for early identification and intervention. The BrainFx SCREEN is a novel digital tool designed to assess cognitive impairment. This study evaluated its efficacy as a screening tool for MCI in primary care settings, particularly in the context of an aging population and the growing integration of digital health solutions. The primary objective was to assess the validity, reliability, and applicability of the BrainFx SCREEN (hereafter, the SCREEN) for MCI screening in a primary care context. We conducted an exploratory study comparing the SCREEN with an established screening tool, the Quick Mild Cognitive Impairment (Qmci) screen. A concurrent mixed methods, prospective study using a quasi-experimental design was conducted with 147 participants from 5 primary care Family Health Teams (FHTs; characterized by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants included health care practitioners, patients, and FHT administrative executives. Individuals aged ≥55 years with no history of MCI or diagnosis of dementia rostered in a participating FHT were eligible to participate. Participants were screened using both the SCREEN and Qmci. The study also incorporated the Geriatric Anxiety Scale-10 to assess general anxiety levels at each cognitive screening. The SCREEN's scoring was compared against that of the Qmci and the clinical judgment of health care professionals. Statistical analyses included sensitivity, specificity, internal consistency, and test-retest reliability assessments. The study found that the SCREEN's longer administration time and complex scoring algorithm, which is proprietary and unavailable for independent analysis, presented challenges. Its internal consistency, indicated by a Cronbach α of 0.63, was below the acceptable threshold. The test-retest reliability also showed limitations, with moderate intraclass correlation coefficient (0.54) and inadequate κ (0.15) values. Sensitivity and specificity were consistent (63.25% and 74.07%, respectively) between cross-tabulation and discrepant analysis. In addition, the study faced limitations due to its demographic skew (96/147, 65.3% female, well-educated participants), the absence of a comprehensive gold standard for MCI diagnosis, and financial constraints limiting the inclusion of confirmatory neuropsychological testing. The SCREEN, in its current form, does not meet the necessary criteria for an optimal MCI screening tool in primary care settings, primarily due to its longer administration time and lower reliability. As the number of digital health technologies increases and evolves, further testing and refinement of tools such as the SCREEN are essential to ensure their efficacy and reliability in real-world clinical settings. This study advocates for continued research in this rapidly advancing field to better serve the aging population. RR2-10.2196/25520.

Time–Frequency Signal Integrity Monitoring Algorithm Based on Temperature Compensation Frequency Bias Combination Model

To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals’ output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring.

Travel time reliability evaluation using fuzzy-possibility approach: a case study of an Indian city

ABSTRACT Due to growing traffic congestion, significant variations in travel time can be seen in Indian cities. This has led to inconsistency and uncertainty about reaching destinations on time. Therefore, average speeds cannot be the only factor in assessing traffic performance. Travel time reliability is a more crucial parameter to evaluate traffic performance considering the inconsistency and uncertainty involved in travel times. The probe vehicle technique is often employed to measure the travel time. However, this technique can yield only a small sample unless huge resources are deployed. Therefore, this paper attempts to develop a travel time reliability-consistency evaluation model (TTRC-EM) for limited and ambiguous information using a novel fuzzy-possibility approach. Two urban corridors, representing arterial and sub-arterial situations, are taken as a case study in Surat, India. The developed model can be applied to evaluate corridor traffic performance and assign a level of service under mixed traffic conditions.

Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study.

Digitalizing oral health data through an app can help manage the extensive data obtained through oral health surveys. The Tooth Memo app collects data from oral health surveys and personal health information. This study aims to evaluate the evaluate the time efficiency, reliability, and user satisfaction of the Tooth Memo app. There are 2 sections in the Tooth Memo app: oral health survey and personal oral health record. For the oral health survey section of the Tooth Memo app, different data entry methods were compared and user satisfaction was evaluated. Fifth-year dental students had access to the oral health survey section in the Tooth Memo app during their clinical work. The time required for data entry, analysis, and summary of oral health survey data by 3 methods, that is, pen-and-paper (manual), Tooth Memo app on iOS device, and Tooth Memo app on Android device were compared among 3 data recorders who entered patients' information on decayed, missing, and filled permanent teeth (DMFT) index and community periodontal index (CPI), which were read aloud from the database of 103 patients by another dental personnel. The interobserver reliability of the 3 different data-entering procedures was evaluated by percent disagreement and kappa statistic values. Laypeople had access to the personal oral health record section of this app, and their satisfaction was evaluated through a Likert scale questionnaire. The satisfaction assessments for both sections of the Tooth Memo app involved the same set of questions on the app design, usage, and overall satisfaction. Of the 103 dental records on DMFT and CPI, 5.2% (177/3399) data points were missing in the manual data entries, but no data on tooth status were missing in the Android and iOS methods. Complete CPI information was provided by all 3 methods. Transferring data from paper to computer took an average of 55 seconds per case. The manual method required 182 minutes more than the iOS or Android methods to clean the missing data and transfer and analyze the tooth status data of 103 patients. The users, that is, 109 fifth-year dental students and 134 laypeople, expressed high satisfaction with using the Tooth Memo app. The overall satisfaction with the oral health survey ranged between 3 and 10, with an average (SD) of 7.86 (1.46). The overall satisfaction with the personal oral health record ranged between 4 and 10, with an average (SD) of 8.09 (1.28). The Tooth Memo app was more efficacious than manual data entry for collecting data of oral health surveys. Dental personnel as well as general users reported high satisfaction when using this app.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

A comparison of respiratory oscillometry and spirometry in idiopathic pulmonary fibrosis: performance time, symptom burden and test-retest reliability.

In large multinational patient surveys, spirometry (which requires repeated, reproducible maximal efforts) can be associated with cough, breathlessness and tiredness, particularly in those with idiopathic pulmonary fibrosis (IPF). Oscillometry is an effort-independent test of airways resistance and reactance. We hypothesised that oscillometry would take less time to perform and would be associated with reduced symptom burden than spirometry. Spirometry and oscillometry were performed in 66 participants with IPF and repeated 2 weeks later. We compared time taken to perform tests, symptom burden and test-retest reliability with Bland-Altman plots and intraclass correlation coefficients (ICCs). Oscillometry took significantly less time to perform than spirometry (mean -4.5 (99% CI -6.0 to -3.0) min) and was associated with lower symptom burden scores for cough (-1.3, 99% CI -1.7 to -0.8), breathlessness (-1.0, 99% CI -1.4 to -0.5), and tiredness (-0.5, 99% CI -0.9 to -0.2). On Bland-Altman analysis, all measures showed good agreement, with narrow limits of agreement and the mean bias lying close to 0 in all cases. The ICCs for forced expiratory volume in 1 s and forced vital capacity were 0.94 and 0.89, respectively, and ranged between 0.70 and 0.90 for oscillometry measures. Oscillometry is quicker to perform and provokes less symptoms than spirometry in patients with IPF.

Response Latencies During Confrontation Picture Naming in Aphasia: Are Proxy Measurements Sufficient?

Anomia, or word-finding difficulty, is a prevalent and persistent feature of aphasia, a neurogenic language disorder affecting millions of people in the United States. Anomia assessments are essential for measuring performance and monitoring outcomes in clinical settings. This study aims to evaluate the reliability of response time (RT) annotation based on spectrograms and assess the predictive utility of proxy RTs collected during computerized naming tests. Archival data from 10 people with aphasia were used. Trained research assistants phonemically transcribed participants' responses, and RTs were generated from the onset of picture stimulus to the initial phoneme of the first complete attempt. RTs were measured in two ways: hand-generated RTs (from spectrograms) and proxy RTs (automatically extracted online). Interrater agreement was evaluated based on interclass correlation coefficients and generalizability theory tools including variance partitioning and the φ-coefficient. The predictive utility of proxy RTs was evaluated within a linear mixed-effects framework. RT annotation reliability showed near-perfect agreement across research assistants (φ-coefficient = .93), and the variance accounted for by raters was negligible. Furthermore, proxy RTs significantly and strongly predicted hand-annotated RTs (R2 = ~0.82), suggesting their utility as an alternative measure. The study confirms the reliability of RT annotation and demonstrates the predictive utility of proxy RTs in estimating RTs during computerized naming tests. Incorporating proxy RTs can enhance clinical assessments, providing additional information for cognitive measurement. Further research with larger samples and exploring the impact of using proxy RTs in different psychometric models could optimize clinical protocols and improve communication interventions for individuals with aphasia.

A multi-objective optimization model for medical waste recycling network design under uncertainties

Transporting medical waste (MW) generated by medical institutions (MIs) is a process that poses potential threats to the environment and public safety. Therefore, it is vital to find a safe and efficient way to transport this type of waste to disposal centers (DCs). However, there are challenges in the transportation of MW due to random factors such as the generation of waste in an unpredictable manner and unforeseen travel times. In this paper, we propose a bi-level optimization model to minimize site selection costs, transportation costs, time-window penalties costs, and transportation risks under uncertainties. The concepts of “loading reliability”, “travel time reliability” and “transportation risk” are adopted in the proposed optimization model. The simulated annealing algorithm (SA) is employed to address the optimal location problem (Upper level), which involves minimizing the construction cost, transportation cost (DC-CP-DC), and transportation risk (estimated using Bayesian method). To tackle the capacity-constrained vehicle routing problem considering transportation risk and time window penalties (Lower level), we propose an improved genetic algorithm named harmony search algorithm (IHSGA). Subsequently, the results from the lower level are looped back to the upper level, fostering mutual influence between the two stages. We demonstrate the effectiveness and correctness of the proposed model and algorithm using S city in China as an illustrative example. Furthermore, a series of sensitivity analyses were conducted to examine the impact of various factors. The findings highlight the pivotal roles of both travel time reliability and loading reliability in designing the medical waste recycling network. In comparison to the general Genetic Algorithm (GA) and CPLEX solver, the modified IHSGA presented in this paper exhibits superior performance.

How to build a more sustainable passenger air transport system: multimodal experience

A fully integrated passenger multimodal system is a new concept in the transport industry, and consequently, data on system interoperability is lacking. In this paper, a qualitative research method is applied, to get the transport experts’ opinions and information on how the multimodal system should be designed. Although they are aware that the collaboration should result in an increase in the volume of passengers, the most challenging task is to convince transport operators to share the data with each other, due to legal issues. To determine the passengers’ perspective on making multimodal choices in their journeys, the travel demand model is developed, based on online survey data. The proposed (Binary Logistic Regression) model reveals the importance for policymakers and transport operators serving the airport to address access time reliability by carefully considering the passengers’ preferences in terms of their needs and age.

Analysis of factors affecting ultrasound examination time: A quantitative study.

Numerous studies have focused on reducing patient absences and effectively scheduling exams. However, very few studies have analyzed the factors influencing examination time and predicted examination time. To investigate the factors affecting ultrasound examination visit length and provide a reference for interventions to optimize ultrasound appointments. This cross-sectional study was conducted at a fertility clinic in China. Ultrasound examination time and clinical characteristics were obtained from the electronic records. Univariate and multivariate analyses used 33,432 patients who attended our clinic center between August 1 and October 30, 2018. A quantile regression model was constructed to examine associations between ultrasound examination time and statistically significant variables in the univariate analysis. Of the 33,432 patients included in this study, 29,085 (87%) were female and 4,347 (13%) were male. Their mean examination time was 6 ± 3 minutes. The doctor's title and gender, equipment, and patient's age, examination site, gender, and origin were all statistically significant. Physical examination and outpatient clinic patients had shorter examination times than inpatients. Female physicians had longer examination times than male physicians. Examination time was positively correlated with thyroid, breast, liver, gallbladder, spleen, pancreas, kidney, heart, vascular, adrenal, gynecological, early pregnancy, nuchal translucency, prostate, scrotum, and mid-to-late pregnancy fetal sites. Moreover, NT and mid-to-late pregnancy fetal sites showed a clear and continuous positive trend with increasing examination time. The length of the ultrasound examination was correlated with the examination site, physician title, physician gender, patient age, patient gender, patient origin, and instrumentation. The reliability of inspection time predicted by variables such as the physicians' title, sex, sites examined, and the number of sites examined was higher when they were longer.

Reference values and reliability of occlusal force distribution and occlusal time measured by the T-Scan system in adults with healthy dentition.

Reference values of occlusal characteristics are needed to interpret the data obtained using the T-Scan System. This study aimed to establish reference values for and to assess the reliability of, occlusal force distribution in the maximal intercuspal position and the occlusion time in young adults with healthy dentition. In total 178 adults with natural dentition participated in this retrospective cross-sectional study, of whom 76 performed a retest session. Several occlusal recordings were obtained from each participant using the T-Scan system while asking them to bite two or three consecutive times (multi-bite) or only once (single-bite) at the maximal intercuspal position. The lateral and anteroposterior occlusal force distribution were determined as percentages in the right and posterior teeth . Occlusion time was measured in seconds. After the occlusal force distribution and occlusion time percentiles were calculated, reliability was assessed by the intraclass correlation coefficient. The 5th-95th percentiles for occlusal force distribution were 34%-67% on the right teeth and 55%-94% on the posterior teeth. The 90th percentile for multi-bite occlusion time was 0.17 s and for single-bite occlusion time was 0.27 s. The intraclass correlation coefficients for lateral occlusal force distribution, anteroposterior occlusal force distribution, multi-bite occlusion time, and single-bite occlusion time were 0.70, 0.68, 0.58, and 0.67, respectively. This study generated reference values for key occlusal characteristics (occlusal force distribution and occlusion time) when using the T-Scan system. These values showed moderate reliability.

Reliability Analysis of Highway Network Travel Time Based on Information Entropy Model and Electronic Toll Collection Data

Travel time reliability (TTR) holds significant importance as an evaluation index in the transportation industry. To simplify TTR calculations at the network level in engineering applications, this paper re-examines the highway network from the perspective of system theory, regards it as a road network system consisting of multiple subsystems, and proposes a framework for calculating the TTR of the highway network based on the information entropy model. The information entropy model is employed to quantify the level of disorder within the temporal distribution, thereby substituting the need for fitting or deducing the distribution function as practiced in conventional methodologies. The design of roadway indicators considers the social attributes of the transportation network, making reliability not solely dependent on temporal data. For the case study, we used data from the electronic toll collection gantry system on the Xi’an bypass highway. The results indicate that the TTR of the inner ring within the ring network is significantly higher than that of the outer ring. Furthermore, the reliability of the network is lower than that of any sub-section, during both peak and off-peak hours. The applicability of the proposed computational framework is demonstrated and the nature of the results is explored in comparisons with traditional metrics and time prediction attempts. The approach to the TTR evaluation problem from a novel perspective presented in this research omits the procedure of fitting the time distribution function and avoids being affected by its heterogeneity, making TTR calculation and extension more efficient and convenient.

Application of Data Augmentation Techniques in Predicting Travel Time Reliability: Evidence from England

Application of Data Augmentation Techniques in Predicting Travel Time Reliability: Evidence from England

Acute Aromatase Inhibition Impairs Neural and Behavioral Auditory Scene Analysis in Zebra Finches.

Auditory perception can be significantly disrupted by noise. To discriminate sounds from noise, auditory scene analysis (ASA) extracts the functionally relevant sounds from acoustic input. The zebra finch communicates in noisy environments. Neurons in their secondary auditory pallial cortex (caudomedial nidopallium, NCM) can encode song from background chorus, or scenes, and this capacity may aid behavioral ASA. Furthermore, song processing is modulated by the rapid synthesis of neuroestrogens when hearing conspecific song. To examine whether neuroestrogens support neural and behavioral ASA in both sexes, we retrodialyzed fadrozole (aromatase inhibitor, FAD) and recorded in vivo awake extracellular NCM responses to songs and scenes. We found that FAD affected neural encoding of songs by decreasing responsiveness and timing reliability in inhibitory (narrow-spiking), but not in excitatory (broad-spiking) neurons. Congruently, FAD decreased neural encoding of songs in scenes for both cell types, particularly in females. Behaviorally, we trained birds using operant conditioning and tested their ability to detect songs in scenes after administering FAD orally or injected bilaterally into NCM. Oral FAD increased response bias and decreased correct rejections in females, but not in males. FAD in NCM did not affect performance. Thus, FAD in the NCM impaired neuronal ASA but that did not lead to behavioral disruption suggesting the existence of resilience or compensatory responses. Moreover, impaired performance after systemic FAD suggests involvement of other aromatase-rich networks outside the auditory pathway in ASA. This work highlights how transient estrogen synthesis disruption can modulate higher-order processing in an animal model of vocal communication.

A Reliability-Based Network Equilibrium Model with Electric Vehicles and Gasoline Vehicles

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods

Travel time reliability (TTR) serves as a crucial indicator for evaluating the efficiency and service quality of a road traffic network. This paper proposes a multi-task spatio-temporal generative adversarial network (MTST-GAN) model that simultaneously predicts the TTR in morning and evening peak hour periods. The model incorporates multi-graph convolutional networks to extract spatial correlations from travel time data, while long short-term memory neural networks are employed to consider temporal correlations. Additionally, self-attention mechanisms are applied to the proposed MTST-GAN model to further capture spatial and temporal features. A feature fusion bridge is constructed to integrate the spatial and temporal features learned by each task. Through a numerical experiment conducted on a road network in a Chinese city, our findings demonstrate that the proposed model outperforms several state-of-the-art approaches in terms of Jensen-Shannon divergence, mean, standard deviation, and buffer time indices. Finally, we provide conclusions and suggest areas for further research.

Two Repetitions May Be Enough! Reliability of Movement Timing in Physical Fitness Exercises Performed by Young, Trained Adults Using Inertial Sensors

This study aimed to determine the minimum number of repetitions for a high reliability of movement timing in fundamental physical fitness exercises using inertial sensors. Fifteen young men and fifteen women performed eight exercises (two-leg hop, forward lunge, squat, sit-up, shoulder abduction, hip abduction, back extension, and push-up) (preferred tempo, 3 trials, 20 repetitions per trial). The movement timing (cycle of movement in seconds and its phases in seconds and %tcycle) was tested for intra- and inter-trial reliability (SPSS 28.0, p ≤ 0.05). Just two repetitions were adequate for excellent intra- and inter-trial relative reliability (ICCs ≥ 0.75, isolated exceptions only for durations expressed as %tcycle, in only three out of the eight exercises: hip abduction, back extension, and push-up), as well as for high absolute intra- and inter-trial reliability (average SEM% at 5.9%, respectively, and 6.8% and average MDC95% at 13.7% and 15.9%, respectively, which was consistently higher than the upper boundary limit of SEM%, and a rather low CV% ranging from 1.5% to 4.9% and averaging at 3.1%). A total of four repetitions, excluding the initial and the final one, appears adequate for high overall reliability of movement timing in the eight physical fitness exercises examined.

Data-Driven Statewide Prioritization of Corridors for Signal Retiming Projects

While the introduction of emerging technologies has substantially improved the way traffic signals are operated, most agencies still rely on the traditional approach of retiming, which is typically initiated by “trigger” events (e.g., signal retiming occurs due to citizen calls, a major change in land use or roadway geometry, sustained oversaturation, safety concerns). The approach of using triggers can work effectively for small agencies, however, such an approach may not be sustainable for agencies that operate a high number of corridors. Additionally, agencies are often limited in terms of their budget to retime signals. Therefore, strategic selection of corridors is important to effectively utilize existing resources. Recently data-driven methods for prioritization of corridors for retiming were proposed. However, these methods either rely on datasets that are not widely available or only account for corridor performance (e.g., speed) without considering other relevant factors that can be important during prioritization. Further, most prior attempts estimated corridor performance based on the simple average performance of individual intersections. Such an approach may distort the results for corridors with largely dispersed intersection performance. To overcome these challenges, this study proposes a novel ranking approach based on data that is available to most agencies and incorporates normalized corridor delay, corridor travel time reliability, annual average daily traffic, and time since last retiming. The proposed method was applied to 120 corridors (consisting of 756 signals) across the State of Ohio in the US. It was found that ranking results are highly aligned with agency representatives’ expectations, as most of the highly ranked corridors have already been identified by agency representatives for future retiming projects. Moreover, the before-and-after analysis from recent retiming studies on highly ranked corridors revealed substantial benefits, confirming the effectiveness of the proposed ranking method. The proposed ranking and prioritization method discussed in this paper could be beneficial for agencies with large number of corridors and limited budget and resources so they can effectively prioritize their corridors for future signal retiming projects.