All-way Stop Research Articles

Capacity at All-Way Stop Control Intersections: Case Study

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 individual 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 significant 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 information in the literature and the dated nature of those empirical observations.

Capacity at All-Way Stop Control Intersections: Case Study

Capacity at All-Way Stop Control Intersections: Case Study

Do stop-signs improve the safety for all road users? A before-after study of stop-controlled intersections using video-based trajectories and surrogate measures of safety

Converting minor-approach-only stop (MAS) intersections to all-way-stop (AWS) intersections is a prevailing safety countermeasure in North American urban areas. Although the general population positively perceives the installation of stop-signs in residential areas, little research has investigated the impact of AWS on road safety and road user behaviour. This paper investigated the safety effectiveness of converting MAS to AWS intersections using an observational before and after approach and surrogate measures of safety. More specifically, the safety impacts of AWS conversion were investigated using multiple indicators, including vehicle speed measures, vehicle–pedestrian, vehicle-cyclist, vehicles-vehicle interactions as well as yielding rates before and after the treatment implementation. A multi-level regression approach was adopted to determine the effect of stop signs controlling for built environments, traffic exposure, and intersection geometry factors as well as site-specific unobserved heterogeneity.A unique sample of 31 intersections were used in this before-after study. From this sample, video data were collected before and after implementing AWS. In total, 245 h of video were automatically processed and corrected using a specialized computer vision software. More than 68,000 (37,668 before and 31,305 after AWS treatment) road user trajectories were obtained from 104 approaches. The results show that the conversion of MAS to AWS intersections significantly decreased vehicle speed and increased post-encroachment time. This work also shows that implementing AWS significantly increased the yielding rates from 45.7% to 76.7% in MAS conditions and reduced the average speed of motor-vehicles. Using multi-level regression model, it is estimated that when the intersection was converted from MAS to AWS, the minimum speed in the major approaches was reduced by 60.0%.

Examining the factors effecting severity of two-wheeler crashes at intersections

For short-distance travellers, two-wheelers have become a standard transportation mode and an ideal alternative (compact size, economical, easy parking), to four-wheelers particularly in developing countries. However, two-wheelers are associated with different safety concerns. In India, two-wheelers are more vulnerable to fatal and serious injuries per kilometres of travel in contrast to drivers of other vehicles. Most of these accidents occur on junctions and are likely to be fatal and more severe in contrast to nonjunction accidents. This research article aims to examines different risk variables associated with motorized two-wheeler crashes at intersections (signalized and un-signalized) in Haridwar and Dehradun districts of Uttarakhand state, India. A multinominal logistic approach was used to analyse a dataset of 680 two-wheeler crashes occurred within four years (2016–2019). The accident severities were categorized into three groups, i.e. fatal injury, serious injury and normal injury. The risk variables were categorized into five significant classes, i.e. riders' attributes, environmental attributes, junction crash attributes and land-use attributes. However, the majority of this study's findings endorses the concepts derived from earlier researches that used alternative (for instance; random forest, ordered logit, etc.) analytical methodologies in crash severity analysis. The model results showed that some variables (e.g. age, gender, adverse environmental conditions, time of crash and location of the junction) increases the possibility of mortal injuries. In contrast, some variables (day of crash, type of intersection and type of controls at intersections) decrease the probability of mortal injuries compared with minor or severe injuries. Based on the model's findings, policy recommendations are made, such as enforcing rules, advanced traffic management systems and road safety campaigns. For instance, education of young drivers can be an effective way to reduce crash severity as they are physiologically less sensitive towards the risk associated with crashes, thus, their involvement results in more severe crashes. Additionally, the results suggested that treatment measures such as transforming an un-controlled junction into signalized or all-way stop controls can be a productive approach for improving the safety of uncontrolled junctions.

Modeling and Simulation of Cascading Failures in Transportation Systems during Hurricane Evacuations

Effective and timely evacuation is critical in alleviating the impact of hurricanes. As such, evacuation models are often sought to support the preparedness of evacuations. One important task in the modeling process is to evaluate exogenous factors that cause transportation system capacity loss during evacuation. Typical factors include direct damage to the roadway network due to storm surge and cascading impacts because of other facilities failures. For example, power outage can lead to signal failure and subway suspension. This paper aims to develop a macroscopic simulation-based approach to study the capacity loss of the roadway network in evacuation due to signal loss as a consequence of power outage. In particular, to simulate the case in which traffic signals lose power, a capacity-reduction model from signalized intersections to unsignalized (all-way stop control) intersections was developed and calibrated using microscopic model created in SUMO and Synchro. We used the downtown Manhattan as a case study area and created a hypothetical power-grid network in terms of neighborhoods. Six scenarios were built to simulate power loss of different neighborhoods. The simulation results give insights on how cascading failures of power network affect roadway network and evacuation process.

Roadway features associated with elderly drivers in motor vehicle crashes.

As the number of older US drivers has increased over the past decades, so has the number of injuries, hospitalizations, and deaths from motor vehicle crashes (MVCs) involving elderly drivers. We seek to identify personal, environmental, and roadway features associated with increased crashes involving elderly drivers. We hypothesize that elderly drivers are more likely to be involved in MVCs at intersections with more complex signage and traffic flow. This is a retrospective observational study using 2015 to 2019 police traffic crash reports and a Department of Public Health database of built-environment variables from a single urban center. Demographics and environmental/road features were compared for vehicle-only MVCs involving elderly (≥65 years) and younger drivers. χ2 and nonparametric tests were used to analyze 36,168 drivers involved in MVCs. There were 2,575 (7.1%) elderly drivers involved in MVCs during the study period. Left turns and all-way stop signs were associated with increased crash risk among elderly drivers compared with younger drivers. Elderly-involved MVCs were less likely to occur at intersections with left-turn restrictions, traffic lights, only one-way streets, and bike lanes compared with MVCs with younger drivers. Elderly drivers were more likely to be involved in MVCs on weekdays, less often intoxicated at the time of the crash, and less frequently involved in fatal MVCs compared with younger drivers. However, elderly drivers were more frequently the at-fault party, especially after the age of 75 years. Updates to roadway features have potential to decrease injury and death from MVCs involving elderly adults. Left turn restrictions or other innovative safety treatments at all-way stops or where left turns are permitted may mitigate road crashes involving older adults. Education may increase awareness of higher-risk driving tasks such as turning left, and driving alternatives including public transportation/paratransit may offer alternate means to maintain activities of daily living. Prognostic/Epidemiological, level IV.

Estimate of the Safety Effect of All-Way Stop Control Conversion in Washington, DC

This study evaluated safety effects associated with converting traditional stop control (TSC) to all-way stop control (AWSC) at 53 intersections in Washington, DC. The study utilized an observational treatment group and a randomly selected comparison group. Negative binomial regression modeling was used to estimate the effect of AWSC conversion on crash outcomes, control for confounding factors, and check its statistical significance. The study also examined potential covariates that could influence AWSC crash outcomes, such as the number of legs of the intersection and the functional classification of the intersecting roads. This study found an overall 36% reduction in all crashes and a 42% reduction in injury crashes associated with converting intersections from TSC to AWSC. In addition, the study revealed a statistically significant reduction in right-angle crashes along with a statistically significant increase in straight hit pedestrian crashes. For all the other collision types, including right turn, left turn, rear-end, sideswipes, and bicycle crashes, no statistically significant coefficients were found. With many “Vision Zero” cities considering increased use of AWSC to help achieve their safety goals, it is important to understand and communicate the safety effects of AWSC.

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.

TurnsMap

Left turns are known to be one of the most dangerous driving maneuvers.1 An effective way to mitigate this safety risk is to install a left-turn enforcement --- e.g., a protected left-turn signal or all-way stop signs --- at every turn that preserves a traffic phase exclusively for left turns. Although this protection scheme can significantly increase the driving safety, information on whether or not a road segment (e.g., intersection) has such a setting is not yet available to the public and navigation systems. This paper presents a system, called TurnsMap, that exploits mobile crowdsensing and deep learning to classify the protection settings of left turns. One of our key findings is that crowdsensed IMU sensor (i.e., gyroscope and accelerometer) data from onboard mobile devices can be used to recognize different types of left-turn protection. TurnsMap first collects IMU sensor data from mobile devices or smartphones carried by the driver/passenger(s) in a moving car. It then feeds the data to an analytics engine powered by (1) a data mining engine for extracting and clustering left turns by processing raw IMU data, and (2) a deep-learning pipeline for learning the model from the IMU data to identify the protection type of each left turn. We have built and used a large-scale real-world driving dataset to evaluate TurnsMap, demonstrating its capability of identifying different left-turn enforcements with 90.3% accuracy. A wide range of automotive apps can benefit (e.g., enhancing traffic safety) from the left-turns information unearthed by TurnsMap.

Predicting STOP-Sign Compliance at All-Way Stop Intersections in Close Proximity to Signalized Intersections

Predicting STOP-Sign Compliance at All-Way Stop Intersections in Close Proximity to Signalized Intersections

Developing and Implementing Manual on Uniform Traffic Control Devices Criteria for Selecting Traffic Control for Unsignalized Intersections

The 2009 United States Manual on Uniform Traffic Control Devices (MUTCD) includes guidance for the use of various types of traffic control at unsignalized intersections. Despite changes and advances in traffic engineering in recent decades, the MUTCD content related to selection of traffic control in Part 2B has seen only minor changes since 1971. The types of unsignalized traffic control addressed in the current research included no control, yield control, two-way stop control, and all-way stop control. The research team developed recommendations using information available from reviews of existing literature, policies, guidelines, and findings from an economic analysis along with the engineering judgment of the research team and panel. The researchers then developed recommended language for the next edition of the MUTCD for unsignalized intersections. This includes consideration of high-speed (rural) and low-speed (urban) conditions along with the number of legs at the intersection. Because the number of expected crashes at an intersection is a function of the number of legs, the decision on appropriate traffic control should also be sensitive to the number of legs present. The proposed language includes introductory general considerations, discusses alternatives to changing right-of-way control, and steps through the various forms of unsignalized control from least restrictive to most restrictive, beginning with no control and concluding with all-way stop control.

Investigating secondary pedestrian-vehicle interactions at non-signalized intersections using vision-based trajectory data

Vision-based trajectory data provides great details for investigating microscopic behavior and road safety at the level of interactions. Most studies investigating pedestrian-vehicle interactions at non-signalized intersections have focused on interactions at the crosswalk on the same approach where the vehicle is coming from, which are referred to as primary interactions in this study. However, secondary interactions, defined as interactions between vehicles exiting the intersection and crossing pedestrians, have not been adequately studied. Second interactions can pose more dangers to pedestrians due to the driver’s unclear knowledge of right-of-way, acceleration attempts to recover the speed, and the complex situation the driver faces in the intersection. This paper aims at studying the safety issue of secondary pedestrian-vehicle interactions at non-signalized intersections. For that purpose, a methodology is proposed based on surrogate measures of safety and behavioral measures derived from vision-based trajectories. This methodology is implemented through a case study involving ten all-way stop intersections from Montreal, Canada. Road user trajectory data are automatically extracted from the videos. Different measures are used in the study: from the interaction analysis that determines vehicle-pedestrian interactions based on a Distance-Velocity (DV) model, average crossing speeds and vehicle approaching behaviors in terms of speed and acceleration. Computer tools are developed to extract these measures from the trajectory data. Based on these measures, a comparative analysis is carried out between primary and secondary interactions. Results show that secondary interactions are more dangerous than primary interactions. Among the three secondary interaction types, secondary through interactions are the most dangerous.

Safety Evaluation of Stop-Sign Mounted Beacons—A Cross-Sectional Study

Agencies in Iowa have utilized both overhead flashing beacons and stop-sign mounted beacons. Although several studies have shown that overhead flashing beacons are effective, some concerns have been raised about driver confusion. The main concern is that a driver may interpret a multiface flashing beacon with a red indication for their approach as an all-way stop control. As a result, the Iowa DOT has been advocating use of stop-sign mounted beacons rather than overhead flashing beacons. Since little information is available about this countermeasure, data for intersections with (treatment) and without (control) stop-sign mounted beacons were identified and a cross-sectional analysis conducted (due to few confirmable installation dates). Rural stop-controlled intersections with stop-sign mounted beacons in Iowa (USA) were identified (40 in total). Intersection characteristics such as number of approaches, intersection angle etc. were extracted. Additionally, characteristics of individual approaches such as roadway surface (gravel/paved), advanced stop-sign rumble strips, and advance signing were recorded. One or more control locations were manually selected for each treatment intersection based on matching roadway configuration, presence of lighting, advance stop line rumble strips, number of approaches, channelization, traffic volume, and proximity. Propensity scores were estimated to match 40 control locations for comparison. Negative binomial models for different injury combinations at nighttime and daytime were developed with an indicator variable for presence and absence of stop-sign mounted beacons. Presence of stop-sign mounted beacons was associated with a 5% - 54% reduction in nighttime crashes. Injury nighttime crashes decreased by 54% and total nighttime crashes reduced by 18%.

Real-time optimal intersection control system for automated/cooperative vehicles

Abstract Automated vehicles (AVs) are an emerging technology in the automotive industry. The numerous research efforts that are being dedicated to the development of these systems makes them achievable in the near future. The need to make this technology mature is justified by potential safety gains from the elimination of human error, the enhancement of mobility through the reduction of congestion, and the protection of the environment through the reduction in vehicle emissions. Algorithms are needed to deliver optimal and/or sub-optimal solutions for situations and/or scenarios that AVs would encounter in the field. In this paper, an attempt to optimize the movement of AVs through intersections is developed. The developed model is a real-time optimization problem subjected to dynamic constraints (i.e., ordinary differential equations governing the motion of a vehicle) and static constraints (i.e., maximum achievable velocities). By virtue of the Lagrangian formulation used in Pontryagin’s minimum principle and convex optimization, the solution that minimizes the trip time is obtained. This logic is simulated and compared to the operation of a roundabout, an all-way stop sign, and a traffic-signal-controlled intersection. The results demonstrate that a 55% reduction in delay is achievable compared to the best of these three intersection control strategies, on average. An interesting byproduct of this new logic is a 43% reduction in fuel consumption (reduction from an average of 200 mL to 115 mL) and CO2 emissions (a reduction from 445 g for the roundabout to 265 g).

An Estimate of Potential Savings From Removing Traffic Signals in a Depopulating Urban Area

A common rule of thumb for signalization in a city is one traffic signal for every 1,000 residents. When a city is depopulating, does this ratio apply when determining how many signals can be removed? The City of Detroit has lost 61% of its population since its peak. If signals were installed to serve the peak population, applying this rule of thumb, 61% of the signal inventory should be able to be removed. An operational analysis was performed on a random sample of the signal inventory of the City. Based on this sample, it was determined that 30% of the signal inventory could be removed and replaced with either two-way or all-way stop control, while still maintaining adequate levels-of-service for all approaches, resulting in a savings of US$460,000 to US$3,700,000 annually in operating costs. Other cities with declining populations should conduct similar analyses to reveal the extent of potential savings.

Intersection Management via Vehicle Connectivity: The Intersection Cooperative Adaptive Cruise Control System Concept

Since the introduction of the vehicle infrastructure integration (VII) and connected vehicle (CV) initiatives in the United States, numerous in-vehicle technologies based on wireless communications are currently being deployed. One of these technologies is cooperative adaptive cruise control (CACC) systems, which provide better connectivity, safety, and mobility by allowing vehicles to travel in denser platoons through vehicle-to-vehicle (V2V) communication. Accordingly, the research presented in this article develops a simulation/optimization tool that optimizes the movement of CACC-equipped vehicles as a replacement for traditional intersection control. This system, which is named iCACC, assumes that the intersection controller receives vehicle requests to travel through an intersection and advises each vehicle on the optimum course of action ensuring no crashes occur while at the same time minimizing the intersection delay. Four intersection control scenarios are compared, namely: a traffic signal, an all-way stop control (AWSC), a roundabout, and the iCACC controller. The results show that the proposed iCACC system significantly reduces the average intersection delay and fuel consumption level by 90 and 45%, respectively. Additionally, the article investigates the impact of vehicle dynamics, weather conditions, and level of market penetration of equipped vehicles on the future of automated vehicle control.

An MPC-Based Approach to Provable System-Wide Safety and Liveness of Autonomous Ground Traffic

An important area of cyber-physical systems research is the development of smart ground transportation systems due to their potentially significant impact on safety, the economy, and the environment. We propose an approach based on model predictive control (MPC) for the development of provably collision free autonomous ground transportation systems, and present an autonomous intersection management framework. The MPC approach enables a vehicle to generate its own motion locally in time based on an optimization framework, incorporating constraints based on the states of other vehicles in the neighborhood, the speed limit of a road, the maximum values of acceleration and deceleration, etc. Safety and liveness of the traffic are however system-wide properties, not merely neighborhood properties, and the challenge is to augment this distributed optimization with coordination rules that guarantee overall system-wide safety as well as liveness of the traffic. We design two vehicle-to-vehicle (V2V) coordination rules, along with a vehicle-to-infrastructure rule, and establish the system-wide safety and liveness of the autonomous traffic based on each vehicle's MPC motion planner, operating in conjunction with an algorithm that orders vehicles according to their runtime properties. We also conduct a comparative simulation study of the throughput performance at an intersection of the above approach against another popular algorithm, the All-Way STOP.

Evaluation of the Conversion from Two-Way Stop Sign Control to All-Way Stop Sign Control at 53 Locations in North Carolina

Due to the perceived safety benefits, relative low cost, and ease of implementation of all-way stop control, there is growing interest in the conversion of intersections from two-way to all-way stop control in North Carolina. However, there are few current, up-to-date studies quantifying their safety benefits, especially for higher speed rural locations or locations equipped with flashing beacons. The purpose of this project was to develop crash reduction factors for the conversion from two-way to all-way stop control at a diverse group of intersections that reflect North Carolina conditions and decision making. Other places with similar conditions might benefit from the findings as well. A total of 53 treatment sites located in urban, suburban, and rural areas were chosen for analysis in this project. The treatment locations were divided into three groups based upon presence of an overhead and/or sign mounted flashing beacon. Group 1 consisted of 33 intersections without flashing beacons; Group 2 consisted of 8 intersections with flashing beacons in the before and after period; and Group 3 consisted of 8 intersections where the flashing beacon was installed with the all-way stop control. The results show a substantial decrease in total, injury, and frontal-impact crashes in the after period when analyzing all locations and Groups 1, 2, and 3. The recommended crash reduction factors for conversion from two-way to all-way stop control are the factors calculated by the empirical Bayes method with consideration for traffic increase. The recommended crash reduction factors from the overall group are a 68% reduction in total crashes, a 77% reduction in injury crashes, a 75% reduction in frontal-impact crashes, and a 15% reduction in “ran-stop-sign” crashes.

Control-Type Selection at Isolated Intersections Based on Control Delay under Various Demand Levels

The Highway Capacity Manual (HCM 2000) displayed the figure Exhibit 10-15 for the purpose of forecasting the likely intersection control types for future facilities. Because this figure is from a source external to HCM, to verify it, this paper employs HCM methodologies for the estimation and comparison of control delay, the choice measure of effectiveness at intersections controlled by signal, all-way stop signs, and two-way stop signs. After detailed analyses of more than 5,000 cases using Highway Capacity Software, results of control delay with various control types under a wide range of demand conditions were charted for comparison with Exhibit 10-15. It is found that Exhibit 10-15 is inconsistent with the results from HCM methodologies and, perhaps, should be replaced with the figures developed in this paper. On the basis of the criterion of minimizing delay alone, it is found that if demand is unbalanced between major and minor streets and if the traffic is low on minor streets, two-way-stop control should be used; if demand is somewhat balanced and minor streets see low to medium traffic, all-way-stop control is preferred; otherwise, signal control should be favored. The paper also demonstrates that the percentage of left-turning traffic has a significant effect on decisions involving intersection control types.

Safety Effectiveness of “Stop Ahead” Pavement Markings

There is a need for rigorous evaluation of low-cost safety strategies that states may implement as part of their strategic highway safety plans. Highway safety funds are limited, so it is important to identify cost-effective strategies. FHWA organized a pooled-fund study of 26 states to evaluate low-cost safety strategies; “Stop Ahead” pavement markings were one strategy selected for investigation. Geometric, traffic, and crash data were obtained for 175 intersections with “Stop Ahead” pavement markings in Arkansas, Maryland, and Minnesota. Similar data were obtained for reference sites within each state. An empirical Bayes approach was incorporated in a before–after study design to evaluate the safety effectiveness of the “Stop Ahead” pavement markings. An aggregate analysis indicated a significant reduction in total crashes for Arkansas, Maryland, and the two states combined. There was also a significant reduction in right-angle and rear-end crashes for Arkansas and a general reduction in injury crashes. A disaggregate analysis indicated that crash reductions are significantly greater at three-legged intersections than at four-legged intersections. The strategy was also more effective at intersections with all-way stop control. Given the low cost of this strategy, even with conservatively high cost estimates, only a modest reduction in crashes is needed to justify its use. On the basis of the estimated safety effectiveness, the necessary crash reduction is easily achievable.