Research Article Transportation Research Record 2023, Vol. 2677(8) 148–157 � National Academy of Sciences: Transportation Research Board 2023 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/03611981231155899 journals.sagepub.com/home/trr Capacity at All-Way Stop Control Intersections: Case Study Ahmed Al-Kaisy1 and Dorukhan Doruk1 Abstract This paper presents an empirical investigation into the capacity of all-way stop-controlled (AWSC) intersections. Video data was collected over four days at an AWSC intersection site in Bozeman, Montana. The site is characterized by single-lane approaches and high level of vehicular and pedestrian traffic. Using strict protocols, video records were processed at the indi- vidual vehicle level and several information metrics were extracted for each vehicle in the data set on all approaches. Study results indicate that the total intersection capacity at the study site varied between 400 and 1,400 vehicles per hour. The study suggests that the wide range of capacity observations is largely associated with the pedestrian crossing activity at the study site. Statistical tests confirmed that both pedestrian crossing activity and the level of conflict have significant effects on intersection capacity at the 95% confidence level. For movement type, the right-turn movement was not found to have a sig- nificant effect on intersection capacity while left-turn movement was found to negatively affect the intersection capacity. The results presented in this paper offer valuable information on AWSC intersection capacity, given the limited amount of infor- mation in the literature and the dated nature of those empirical observations. Keywords operations, highway capacity and quality of service, capacity, Highway Capacity Manual (HCM), interrupted flow, intersections Intersections are an essential component of the highway network both from operations and safety perspectives. Specifically, the capacity of intersections primarily con- trols the capacity of corridors and networks in the high- way system because of the many movements (traffic streams) they serve. At the same time, intersections are usually associated with a high percentage of crashes, especially in urban areas, caused by conflicting traffic movements and traffic stream interruptions. Various traffic controls are used at the location of intersections depending on traffic conditions, highway functional classification, sight distance, area setting, and other considerations. One of the important traffic con- trols used at intersections is all-way stop control (AWSC) which requires all vehicles to stop before entering the intersection. This type of traffic control is usually used when traffic volumes on the crossing roadways are rela- tively low and when both roadways belong to the same functional classification (typically local or collector roadways). The operation of an AWSC intersection is quite com- plex. The Highway Capacity Manual (HCM) defines capacity at AWSC intersections as ‘‘the maximum throughput on an approach given the flow rates on the other intersection approaches’’ (1). Consequently, the capacity and delay at any given approach to the intersec- tion is a function of traffic volumes on all other approaches. Vehicles on other approaches, their intended movements, and the subject vehicle’s movement (through, right, or left) determine the number of vehicles that are in conflict with the movement of the subject vehicle, and thus the time it takes the subject vehicle to perform the intended maneuver. Figure 1 shows the sub- ject approach to an AWSC intersection and the opposing and conflicting traffic streams. If no turning movement is banned at the location of interest, then vehicles enter- ing the intersection from any of the available approaches may perform one of the three movements: left-turn, through, or right-turn movement. 1Department of Civil Engineering, Montana State University, Bozeman, MT Corresponding Author: Ahmed Al-Kaisy, alkaisy@montana.edu us.sagepub.com/en-us/journals-permissions https://doi.org/10.1177/03611981231155899 https://journals.sagepub.com/home/trr http://crossmark.crossref.org/dialog/?doi=10.1177%2F03611981231155899&domain=pdf&date_stamp=2023-03-13 Historically, vehicles from the conflicting (crossing) or opposing traffic streams have been treated using case classification (case number) to represent the level of con- flict on other approaches at the time when the subject vehicle arrives at the stop bar on the subject approach. It is this level of conflict (and the type of movement of the subject vehicle) that determines the time it takes the vehi- cle to legally enter the intersection and perform the intended maneuver. The HCM 1994 update (2) intro- duced four case numbers for the level of conflict, which was then extended to five case numbers in the HCM 2000 edition (3), as shown in Figure 2. Background The literature review conducted in the course of this research found few studies on AWSC intersection capacity and they were mostly outdated, that is, most studies were conducted between the 1960s and 1990s. Hebert (4) investigated the capacity at three AWSC intersections with a single lane on each approach in the Chicago metropolitan area. The data were collected using video cameras at the study sites. The study esti- mated capacity using the average departure headway and investigated the effects of turning movements and the volume split between the two crossing roadways. Total intersection capacity values as high as 1,900 vehi- cles per hour (vph) were reported by the study. The study found that right-turning vehicles are associated with smaller departure headways thus increasing intersection capacity. The study also found that a balanced volume split between the two crossing roadways contributes to higher capacities. Later in 1987, Richardson (5) drew attention to the issue of the previously published methods and the lack of any analytical model for capacity estimation. The author introduced a new model that had the ability to predict the level of performance at the intersection over a broader range of traffic conditions. They calculated the capacity based on the service time (departure headway) and the time that each vehicle occupies the intersection, which was based on Hebert’s work (using Hebert’s mini- mum departure headways). In addition, Richardson used this model to forecast intersection capacity and delay under different scenarios of volume split between the crossing roadways. Kyte and Marek (6) investigated the capacity and delay at single-lane AWSC intersections. They collected data for nearly 25 h of operation from eight different sites in the northwest region. The primary motivation for this research was the need to improve the 1985 HCM AWSC intersection procedures. The study presented two meth- ods for estimating the capacity of an AWSC intersection using the highest flow rate observations and departure headway data. The study reported that the highest single observed flow rate was 2,016 vph, whereas the maximum observed flow rate for an intersection approach was 732 vph. A regression model was developed for departure headway that could be used in estimating approach capacity. Since the data collected represent a limited range of traffic conditions, the developed model may not be applicable to the wide range of traffic conditions in real-world applications. The study also reported values for departure headways under capacity operation for dif- ferent scenarios of vehicles using other approaches (level of conflict) and the corresponding approach and total intersection capacity values. Kyte (7) estimated the factors affecting the capacity of AWSC intersections and developed a procedure for esti- mating the capacity. Kyte used 30h of data and more than 7,000 departure headways from 20 study sites Figure 2. The Highway Capacity Manual (HCM) 2000 level of conflict case numbers (3). Figure 1. Approaches at four-way stop-controlled intersection (2). Al-Kaisy and Doruk 149 including single-lane and multilane AWSC intersections. The main variables used in his capacity model are the number of lanes on each approach, the distribution of traffic among different approaches, and the proportion of turning movements on each approach. The study found that the AWSC capacity increases with an increase in the proportion of right-turning vehicles and decreases with an increase in the proportion of left-turning vehicles, pedestrian traffic, and heavy vehicle traffic. In a follow- up study, Kyte et al. (8) investigated saturation headway using an AWSC intersection site in Portland, Oregon. The study examined saturation headway under the effect of vehicle movement from the subject approach, and the presence of vehicles on the conflicting and opposing approaches. The capacity of an approach was found to vary between 525 vph when the subject driver faces a con- tinuous queue of vehicles on both the opposing and con- flicting approaches and 1,100 vph when the subject driver faces no opposing or conflicting vehicles. Another study by Wu (9) developed a new model for estimating total AWSC intersection capacities (with single lane approaches) using the so-called Addition-Conflict- Flow (ACF) technique. The study claimed that the effect of turning streams or movements was not modeled in suffi- cient detail in the HCM 2000 procedures. The study com- pared total capacities from the HCM 2000 procedures with those found using the ACF technique. Further, the study also suggested modifications to the HCMmodel that would make results more realistic (more consistent with older studies). Both the ACF technique and the modified HCM model yielded capacities that are notably higher than those from the HCM 2000 procedures. Motivation The limited empirical research on the capacity at AWSC intersections was the main driver behind the current research. Further, the few studies that are found in the literature are generally very old and may not necessarily reflect the current-day vehicle performance, driver beha- vior, and road geometry standards. Therefore, it was important to update the literature with recent observa- tions of AWSC intersection capacities and to examine, to the extent practical, other factors that are known to have an effect on the capacity at this type of intersection. Study Site The study site used in this research is located in Bozeman, Montana at the 11th Avenue and West Grant Street intersection. The 11th Avenue runs north–south, connecting downtown Bozeman with the southern part of the town, passing through the Montana State University (MSU) campus, while Grant Street is a minor collector running east–west, providing access to the MSU campus from the east. The speed limit on both streets is 25mph. At the location of the intersection, both streets have standard lane widths, and bicycle lanes are provided in all directions. The site was selected for its rel- atively high traffic levels and significant pedestrian activ- ity, and the suitability of the site for data collection setup. The intersection has four single-lane approaches and crosswalks in all directions. A street view of the study site is shown in Figure 3. Data Collection and Processing The data used in this research were collected using a sur- veillance camera on a mobile traffic monitoring trailer set at a height overlooking the study site. Video records of the study site showing all intersection approaches were acquired on four different workdays in November 2017 (see video footage images in Figure 4). The camera was set up so that all vehicles entering and exiting the intersection could be viewed simultane- ously, including the queue presence on the subject approach. A total of 84 h of video recordings were Figure 3. Street view of the study site. Source: Google Maps. Figure 4. Video footage showing the study site. 150 Transportation Research Record 2677(8) collected at the study site. The video footage was then analyzed using digital video recording software and a very systematic manual procedure for processing the data. Specifically, to be consistent in extracting the required data from video records, a set of rules (proto- col) was developed to extract accurately all the variables of interest for each vehicle entering the intersection from the subject approach. A pilot study was performed in developing these rules before processing the complete data sets. The information that was extracted for each vehicle entering the intersection from any of the intersec- tion approaches involved the following: - Date - Arrival time: this is the time when the subject vehi- cle arrives at the stop bar of the subject approach. - Departure time: this is the time when the front edge of the subject vehicle crosses the stop bar location and enters the intersection. - Clearance time: this is the time when the rear edge of the subject vehicle clears the physical area of the intersection. - Wait time: this is the time during which the subject vehicle was waiting at the stop location before entering the intersection. It is measured as the time lapse between the arrival and departure times. - Occupancy time: this is the time when the subject vehicle occupies the physical area of the intersec- tion. It is measured as the time lapse between the departure and clearance times. - Departure headway: this is the time between two consecutive departures on the subject approach, measured as the time lapse between the departure times of the subject vehicle and the preceding vehi- cle on the same approach. - Movement type (through, right, or left). - Case number: the case number indicates the vehi- cles on all other intersection approaches that are present when the subject vehicle arrives at the stop bar as classified by the HCM procedures. The HCM case number classification is shown in Figure 1. - Queue presence on subject approach. - Pedestrian crossing(s) and type: this field indicates if pedestrian(s) are crossing the intersection using the near, far, or both crosswalks conflicting with the vehicle movement. - Number of pedestrians involved in the crossing maneuvers. Data processing was completed for each subject approach independently before data analysis. It should be noted that data processing also marked heavy vehi- cles, bicyclists crossing the intersection, and vehicles violating the all-way-stop-control rules. These observa- tions were not considered in the analysis to control on their potential effect on intersection capacity (these instances are very few overall). Further, intersection operation at capacity for any duration less than 60 s was not considered in the analysis. The 60 s duration was deemed an appropriate tradeoff between ensuring sus- tained capacity operations at the intersection and the ability to obtain a reasonable number of capacity obser- vations for analysis purposes. Methodology The processed video recording data described in the pre- vious section was used in extracting the intersection capacity observations measured in vehicles per hour. The underlying assumption is that the intersection operates at capacity when there is at least one vehicle stopped wait- ing to be served on any of the intersection approaches. This study utilized total intersection capacity as opposed to approach capacity used by the AWSC intersection capacity analysis procedures of the HCM. In this study, total intersection capacity refers to the maximum throughput expressed as an hourly rate for all vehicles entering the intersection from any of the intersection approaches. Two methods were utilized in extracting total intersection capacity observations. Method I: This method identified intervals of time when the subject approach to the intersection was queued for a ‘‘tangible’’ period of time, that is, when many queued vehicles are discharged consecutively from the subject approach. This approach focuses on the queuing on one approach regardless of the pres- ence of queue(s) on other approaches. The time inter- val was measured as the time lapse between the first vehicle entering the intersection and the last vehicle in the interval clearing the intersection. To account for traffic condition on other approaches, case number was used in this method to account for traffic entering the intersection from other approaches. The numbers of vehicles entering from the subject approach as well as those entering from other approaches are counted over the time interval and the information is used to calculate capacity as an hourly flow rate. This process was repeated for the different intersection approaches. Method II: This method identified time intervals where at least one vehicle was waiting to be served at any of the four intersection approaches. Similar to the previous method, intervals should be long enough to include many waiting (mostly queued) vehicles enter- ing the intersection consecutively from any of the intersection approaches. To apply this method, the Al-Kaisy and Doruk 151 data from all intersection approaches have to be com- bined first (as different approaches were processed independently) before time intervals for capacity operations are identified and marked. For each inter- val, the number of vehicles entering the intersection and the duration of time were used in calculating capacity observations. The time duration is calculated as the time lapse between the first vehicle entering the intersection and the last vehicle in the interval clearing the intersection. Analysis of Results This section presents the results of the capacity analysis at the study site using the methodologies described in the previous section. The analyses using each of the two methods for estimating the intersection capacity are pre- sented separately. Method I Analysis Using the procedure described in the previous section, 104 intervals were identified and used to estimate the intersection capacity over the four days of data collection for all intersection approaches. As shown in Table 1, the mean capacity value using this method is around 892 vph with a standard deviation of around 164 vph. The mini- mum capacity observed was just 432vph while the maxi- mum observed capacity was around 1,345 vph. The range of capacity observations is wide, which can largely be attributed to pedestrian activities during the period of interest. Specifically, slow walking speed for some pedes- trians and pedestrians crossing in large numbers are typi- cal situations that could significantly affect the number of vehicles entering the intersection during a given interval. It was of interest to examine the effect of some of the variables that are thought to affect the capacity at AWSC intersections. The variables that were investi- gated in this study are discussed below. 1. Number of crosswalks in use: it is well known that pedestrian crossing activity at an AWSC intersec- tion is a major determinant of intersection capac- ity. At the study site, where pedestrian crossings are allowed on all approaches, a vehicle may have conflict with pedestrians using the near crosswalk on the incoming approach, pedestrians using the crosswalk on the far (outgoing) approach, or both. This variable assumed a value of 1 if either cross- walk is in use and 2 if both crosswalks are in use. 2. Number of crossing pedestrians: this variable accounts for the total number of crossing pedes- trians at the intersection that are in conflict with the subject vehicle movement. 3. Number of vehicles on other approaches: this is the number of vehicles that are already waiting on other approaches when the subject vehicle arrives at the stop sign. This number is a function of the HCM case number described earlier. The number of vehicles corresponding to each case number is shown in Table 2. This variable is expected to affect the AWSC intersection capacity as vehicles entering from different approaches increases the possibility of two vehicles using the intersection simultaneously (for non-conflicting movements). 4. Proportion of right-turn movement: this is the pro- portion of right-turning vehicles during the time interval when the intersection was operating at capacity. As the right-turn maneuver has fewer conflicts with other movements than other maneu- vers, it is expected that higher proportion of right- turning vehicles may contribute to higher capaci- ties. Specifically, higher proportion of right-turning vehicles may increase the possibility of two vehicles using the intersection simultaneously. Such an effect is also reported in older studies (4, 7). 5. Proportion of left-turn movement: this is the pro- portion of left-turning vehicles during the time interval when the intersection was operating at capacity. As the left-turn maneuver has more potential conflicts with other movements, it is expected that higher proportion of left-turning vehicles may lead to lower capacities. Such an effect is also reported in the literature (7). Table 2. Number of Conflicts Using the Highway Capacity Manual Case Numbers Case number Number of vehicles Case 1 0 Case 2 1 Case 3 1 Case 4 2 Case 5 3 Table 1. All-Way Stop-Controlled (AWSC) Intersection Capacity Descriptive Statistics—Method I AWSC intersection capacity descriptive statistics—Method I Mean 892.02 Median 880.08 Standard deviation 164.26 90th percentile 1,093.82 Minimum 432 Maximum 1,344.58 95% confidence interval 31.94 Sample size 104 152 Transportation Research Record 2677(8) To examine the effect of the above variables on AWSC intersection capacity, scatterplots were estab- lished showing the relationships between the AWSC intersection capacity and the five variables described ear- lier. These scatterplots are shown in Figures 5 to 9. The scatterplot in Figure 5 exhibits a declining pattern where intersection capacity decreases with the increase in the number of crosswalks in use. This pattern is expected given the longer time a vehicle would need to cross the intersection when more crosswalks are occupied with pedestrians. A similar declining pattern is shown in Figure 6 where the intersection capacity decreases with the increase in the average number of crossing pedestrians using the conflicting crosswalks. The scatterplot shown in Figure 7 exhibits a slight rising pattern between the inter- section capacity and the average number of vehicles on other approaches (when the subject vehicle arrives at the stop sign). This is consistent with the expectation of higher capacities (discussed earlier) when vehicles enter the inter- section from different approaches. With regard to move- ment type, no clear patterns can be discerned in Figures 8 and 9 to confirm the effect of the proportion of right-turn and left-turn movements on AWSC intersection capacity. To closely examine the effect of the five variables on intersection capacity, the multivariate linear regression was performed, and the results are shown in Figure 10. Figure 5. Intersection capacity versus number of crosswalks in use. Figure 6. Intersection capacity versus average number of crossing pedestrians. Figure 7. Intersection capacity versus average number of vehicles on other approaches. Figure 8. Intersection capacity versus proportion of right- turning vehicles. Figure 9. Intersection capacity versus proportion of left-turning vehicles. Al-Kaisy and Doruk 153 The coefficient of determination (R-square) value in the regression output indicates that around 46% of the variability in intersection capacity is explained by the five variables that are part of the regression model. The stan- dard error of estimate is around 124vph and the F value from the ANOVA test indicates that the overall regres- sion model is significant. By examining the t-test results, all independent variables except the proportion of right- turning vehicles were found to have significant effect on intersection capacity. Method II Analysis This section presents the results of the intersection capac- ity analysis using Method II described earlier. We identi- fied 110 intervals and used them to estimate the intersection capacity over the four days of data collec- tion. Descriptive statistics of AWSC intersection capacity are provided in Table 3. The mean capacity value using Method II is around 783 vph. The standard deviation for capacity observations is around 161 vph. The minimum capacity observed was around 420vph while the maxi- mum observed capacity was around 1,292 vph. Similar to Method I, the range of capacity observations is wide, which can largely be attributed to the variation in pedes- trian activities during the periods of interest. Similar to the previous analysis for Method I, Figures 11 to 15 show scatterplots for the AWSC intersection capacity versus the average number of crosswalks in use, average number of crossing pedestrians, average number of vehicles on other approaches, the proportion of right- turning vehicles, and the proportion of left-turning vehicles respectively. Figures 11 and 12 clearly exhibit a declining pattern where intersection capacity decreases with the increase in the average number of crosswalks in use and the average number of crossing pedestrians. This is very consistent with the patterns shown in Figures 5 and 6 of Method I analysis. The scatterplot shown in Figure 13 reveals no discernable relationship between the AWSC intersection capacity and the average number of vehicles present on other approaches when the subject vehicle arrives at the stop sign (a surrogate measure for the case number used in the HCM). Similarly, no clear association can be discerned between the AWSC inter- section capacity and the proportions of right-turning and left-turning vehicles using the scatterplots shown in Figures 14 and 15 respectively. To have a better understanding of the association between the AWSC capacity and the five variables of interest, the multivariate linear regression was conducted Figure 10. Regression output for estimating capacity—Method I. Table 3. All-Way Stop-Controlled (AWSC) Intersection Capacity Descriptive Statistics—Method II AWSC intersection capacity descriptive statistics—Method II Mean 782.86 Median 786.2 Standard deviation 161.16 90th percentile 976.27 Minimum 420.23 Maximum 1,292.3 95% confidence interval 10.31 Sample size 111 154 Transportation Research Record 2677(8) using the intersection capacity as the dependent variable and the five variables of interest as the independent vari- ables. The regression results are presented in Figure 16. The coefficient of determination value indicates that vari- ables included in the model explain around 56% of the variation in the dependent variable, that is, the AWSC intersection capacity. The standard error of estimate is around 110 vph and the F value from the ANOVA test indicates that the overall regression model is significant. The t-test results confirmed that all variables included in the model are statistically significant at the 95% confi- dence level, with the exception of the proportions of right-turning and left-turning vehicles. Discussion of Results In the previous section, the AWSC intersection capacity was calculated and analyzed using the two methods described earlier in this paper. One notable difference is the relatively higher intersection capacity estimated using the first method. Specifically, the AWSC intersection capacity using Method I was 892 vph, which is around 14% higher than the capacity estimated using Method II (783 vph). On careful examination of the data, it was found that the Method I observations are associated with higher percentage of traffic coming from other Figure 11. Intersection capacity versus number of crosswalks in use. Figure 12. Intersection capacity versus average number of pedestrians. Figure 13. Intersection capacity versus number of vehicles on other approaches. Figure 14. Intersection capacity versus proportion of right- turning vehicles. Figure 15. Intersection capacity versus proportion of left- turning vehicles. Al-Kaisy and Doruk 155 approaches (higher average number of vehicles on other approaches) which may partly explain the higher capac- ity observations for Method I. Specifically, the higher the percentage of traffic on the subject approach, the lower the likelihood of multiple vehicles using the inter- section simultaneously, and the lower the intersection capacity, and vice versa. The minimum and maximum capacity values are generally higher for Method I com- pared with Method II which is somewhat expected given the higher mean value. The standard deviation is very close (almost the same) for the two methods. The regres- sion analyses for the two methods yielded results that are slightly different. Specifically, the two models found the number of crosswalks in use, the number of crossing pedestrians, and the number of vehicles on other approaches to have statistically significant effect on intersection capacity at the 95% confidence level. Further, the two analyses found the proportion of right- turning vehicles to have no significant effect on intersec- tion capacity. However, the proportion of left-turning vehicles was found to have significant effect on intersec- tion capacity according to Method I model, while the effect was not found significant using Method II model. It is also important to mention that Method II yielded a model with a higher coefficient of determination, and thus a better fit for capacity observations. In general, the total intersection capacity observations in this study using either method are notably different from the capacity observations that are reported in the literature. However, it should be mentioned that those reported capacity observations either come from a couple of older studies and are considered dated (with the most recent study around three decades ago) or estimated using theoretical models and are not based on field obser- vations. To the knowledge of the authors, no recent AWSC intersection capacity observations are reported in the literature. The other aspect that is evident in other studies in the literature is the lack of notable pedestrian traffic at the study sites (pedestrian traffic was not a major study variable in any of these studies). Therefore, the effect of pedestrians on AWSC intersection capacity is lacking from all the studies published on this subject including those discussed in the background section. Finally, the protocol used in data processing may partly explain the lower capacity estimates observed in this study compared with some values reported in the litera- ture. Specifically, in this study, capacity operations at the intersection have to be sustained for at least one minute to be included in capacity observations. Shorter intervals, often associated with smaller departure (saturation) headways and higher capacities, were excluded from analysis as they do not represent sustained capacity oper- ations (e.g., very short headways associated with a few vehicle departures only). Summary of Findings and Recommendations This paper presents an empirical investigation into AWSC intersection capacity and the effect of some of the variables that are believed to affect intersection capacity. Specifically, field data from a busy AWSC intersection in Bozeman, Montana was used in this Figure 16. Regression output for estimating capacity—Method II. 156 Transportation Research Record 2677(8) investigation. Four days of video records were acquired at the study site using a traffic surveillance camera on a mobile trailer deployed at the study site. Capacity obser- vations were estimated using two methods and the effect of the following variables were examined: level of conflict (HCM case number), pedestrian crossings at the intersec- tion, and the type of movement for the subject vehicle. The major findings of this study are provided below. � In general, capacity observations using the two methods ranged roughly between 400 vph and 1,400 vph. The wide range of capacity observa- tions is primarily related to the varying conditions at the study site, especially pedestrian traffic. These observations are generally lower than those reported in some of the older studies in the literature. � Capacity observations using the two methods are somewhat different, but overall comparable. The difference in the mean capacity values could be related to Method I being more associated with lower percentage of traffic on the subject approach resulting in slightly higher capacity observations. � Pedestrian activity at the study site was found to have profound effect on saturation headways and consequently on total intersection capacity. Average number of vehicles on other approaches, a surrogate measure for the HCM case number, was also found to have significant effect on total intersection capacity. The proportion of right-turn movement was not found to have significant effect on intersection capacity, which is inconsistent with some of the findings from the older literature. Further, the proportion of left-turning vehicles was found to have significant effect on intersec- tion capacity using Method I analysis only. Given the scarcity of empirical studies on AWSC intersection capacity, and the many variables affecting traffic operations at this type of intersection, the authors recommend further research into this subject using multi- ple study sites and diverse traffic and geometric condi- tions. This is especially important given the lack of consistency in capacity estimates among the few pub- lished studies as well as the HCM capacity estimates. Acknowledgment The authors would like to thank Taylor Lonsdale of the Western Transportation Institute (WTI) (currently affiliated with the City of Bozeman) for his help in field data collection. Author Contributions The authors confirm contribution to the paper as follows; study conception and design: A. Al-Kaisy; data collection: D. Doruk; analysis and interpretation of results: A. Al-Kaisy, D. Doruk; draft manuscript preparation: A. Al-Kaisy, D. Doruk. All authors reviewed the results and approved the final version of the manuscript. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD Ahmed Al-Kaisy https://orcid.org/0000-0003-1198-0975 References 1. Highway Capacity Manual: A Guide for Multimodal Mobility Analysis, 6th ed. Transportation Research Board, Washing- ton, D.C., 2016. 2. Highway Capacity Manual, 3rd ed. Transportation Research Board, National Research Council, Washington, D.C., 1994 Update. 3. Highway Capacity Manual, 4th ed. Transportation Research Board, National Research Council, Washington, D.C., 2000. 4. Hebert, J. A Study of Four-Way Stop Intersection Capaci- ties. Highway Research Record, Vol. 27, 1963, pp. 130–147. 5. Richardson, A. J. Delay Model for Multiway Stop-Sign Intersections. Transportation Research Record: Journal of the Transportation Research Board, 1987. 1112: 107–114. 6. Kyte, M., and J. Marek. Estimating Capacity and Delay at a Single-Lane Approach, All-Way Stop-Controlled Intersec- tion. Transportation Research Record: Journal of the Trans- portation Research Board, 1989. 1225: 73–82. 7. Kyte, M. Estimating Capacity of an All-Way-Stop-Con- trolled Intersection. Transportation Research Record: Jour- nal of the Transportation Research Board, 1990. 1225: 73–82. 8. Kyte, M., Z. Tian, J. Kuhn, H. Poffenroth, M. Butorac, and B. Robertson. Saturation Headways at Stop-Controlled Intersections. Transportation Research Record: Journal of the Transportation Research Board, 1994. 1457: 111–117. 9. Wu, N. Total Capacities at All-Way Stop-Controlled Inter- sections: Validation and Comparison of Highway Capacity Manual Procedure and Addition-Conflict-Flow Technique. Transportation Research Record: Journal of the Transporta- tion Research Board, 2002. 1802: 54–61. Al-Kaisy and Doruk 157 https://orcid.org/0000-0003-1198-0975