Stop-controlled Intersections Research Articles

Comparative Sensitivity Analysis on Intersection Crash Prediction Models by Control Type: Highway Safety Manual Approach

A comprehensive, in-depth investigation into the comparative safety of different intersection control types is presented, specifically signalized and minor road stop-controlled intersections across facility types. Employing a rigorous sensitivity analysis, the study scrutinizes intersection crash prediction models (CPMs) rooted in the Highway Safety Manual (HSM) methodology. Through meticulous data analyses and thorough comparisons of predictive models across a range of major and minor road traffic volumes, the paper critically examines the portrayal of signalized and stop-controlled intersections within the default HSM Part C CPMs. Additionally, the study delves into the effects of certain crash modification factors on CPMs, further exploring the impacts of calibration factors and locally developed models on crash prediction results. CPMs for signalized intersections do not uniformly lead to fewer crashes compared with stop-controlled intersections, especially under certain traffic volumes and when considering fatal and injury crashes. Therefore, the choice of intersection control should be based on more nuanced criteria than traffic volume alone. Additionally, the research emphasizes the role of intersection geometry and operations, for example, the benefits of adding turn lanes and varying left-turn phasing, in influencing safety outcomes. This study significantly contributes to the understanding of intersection safety and HSM modeling approaches, providing a valuable resource for transportation professionals dedicated to advancing intersection safety. Examining strengths and limitations of existing models provides valuable input for researchers developing future intersection CPMs, and offers essential insights for transportation planners and engineers, informing decision-making for optimizing intersection control strategies and enhancing overall intersection safety and traffic management.

Calibration and development of safety performance functions for two-way stop-control intersections on rural two-lane highways in Louisiana

The first edition of the Highway Safety Manual (HSM) contains a simplistic version of the crash prediction model for two-way stop-controlled intersections (TWSC) on rural two-lane highways. This model considers AADT on major and minor roads, with the base conditions defined as no intersection skewness, no turning lanes, and no lighting. A crash modification factor (CMF) will be applied if an intersection has conditions different from the base condition. However, the HSM model does not take account of curvature. It is well known that curved TWSC intersections are less safe than non-curved ones, particularly on rural two-lane roadways. This paper presents the development of crash prediction models incorporating intersection geometrics for TWSC intersections on rural two-lane highways in Louisiana. Then, it compares the results from the developed model with the calibrated HSM model. The negative binomial model was used with 5126 TWSC intersections verified one by one, including both three- and four-leg intersections from all parishes (counties). The estimation results indicate that AADT, curve radius, and intersection skewness angle significantly impact expected crash frequency for both three- and four-leg intersections. This research utilizes cumulative residual plots, mean absolute error, and root mean square error for comparative analysis of HSM models, HSM models with calibration, and Louisiana-specific models. The results show that Louisiana-specific SPFs outperformed the calibrated SPFs with superior reliability. Calibration factors of 0.58 for three-leg intersections and 0.46 for four-leg intersections are estimated, suggesting that the original HSM model overpredicts crashes in Louisiana.

Exploration of the Heterogeneity among Elderly Drivers by Analyzing Traffic Crash Data: A Case Study in Pennsylvania, USA

<div>With population aging and life expectancy increasing, elderly drivers have been increasing quickly in the United States and the heterogeneity among them with age is also increasingly non-ignorable. Based on traffic crash data of Pennsylvania from 2011 to 2019, this study was designed to identify this heterogeneity by quantifying the relationship between age and crash characteristics using linear regression. It is found that for elderly driver-involved crashes, the proportion leading to casualties significantly increases with age. Meanwhile, the proportions at night, on rainy days, on snowy days, and involving driving under the influence (DUI) decrease linearly with age, implying that elderly drivers tend to avoid traveling in risky scenarios.</div> <div>Regarding collision types, elderly driver-involved crashes are mainly composed of angle, rear-end, and hit-fixed-object collisions, proportions of which increase linearly, decrease linearly, and keep consistent with age, respectively. The increase in angle collisions is primarily attributed to more crashes at stop-controlled intersections. The findings suggest that it may be inappropriate to take elderly drivers as homogeneous or simply categorize them into several age groups. Instead, regarding elderly drivers, age should be taken as continuous in future studies to display their linearly changing trends. This is one of the pioneering studies exploring the heterogeneity across elderly drivers with age with solid data analysis. The findings are expected to provide new insights for agencies to develop customized countermeasures regarding elderly traffic safety in the aging society.</div>

Risk-Taking Driving Behavior in Interacting With an Autonomous Vehicle at Two-Way Stop-Controlled Intersections

During the initial stages of autonomous vehicles (AVs), they will coexist and interact with human-driven vehicles (HVs). There is a research gap in exploring AVs’ impact on human drivers’ risk-taking driving behaviors during such interactions. A questionnaire survey was conducted among 676 drivers to gain insight into this impact. Respondents were asked to complete the questionnaires after understanding the scenarios of interacting between HVs and AVs at two-way stop-controlled intersections. The results demonstrate that human drivers have higher risky driving intentions and perceive lesser social pressure and safety risks when interacting with AVs compared to HVs interactions. Attitudes are the most important factor in explaining risk-taking driving intentions. Furthermore, intentions are directly influenced by perceived behavioral control when interacting with AVs. These findings indicate that human drivers may engage in risky behaviors when interacting with AVs.

Strategy and safety at stop intersections in older adults with mild cognitive impairment and visual decline

This study assessed the impact of age-related cognitive and visual declines on stop-controlled intersection stopping and scanning behaviors across varying roadway, traffic, and environmental challenges.Real-world driver data, collected from drivers’ personal vehicles using in-vehicle sensor systems, was analyzed in 68 older adults (65–90 years old) with and without mild cognitive impairment (MCI) and with a range of age-related visual declines. Driver behavior, environmental characteristics, and traffic characteristic were examined across 2,596 approaches at 173 stop-controlled intersections. A mixed-effects logistic regression modeled stopping behavior as a binary response (full stop or rolling/no-stop).Overall, drivers who scanned more on intersection approaches (OR = 0.77) or had more visual decline (OR = 2.28) were more likely to make full stops at a stop-controlled approach. Drivers with a contrast sensitivity logMAR score > 0.8 showed the greatest probability of making a full stop compared across all drivers. Drivers without MCI were ∼ 5 times more likely to come to a full stop when they scanned more (23 % versus 5 % when they scanned less) compared to drivers with MCI, who were only twice as likely to stop (14 % versus 6 % when they scanned less). Drivers were more likely to fully stop on two-lane roadways (1.5 %), during night (2.0 %), and at intersections with opposing vehicles (10.4 %).Findings illuminate how driver strategies interact with underlying impairment. While drivers with visual decline adopt strategies that may improve safety, when drivers with MCI adopt strategies it did not result in the same degree of improvement in stopping which may result in greater risk.

Bimodal Trust: High and Low Trust in Vehicle Automation Influence Response to Automation Errors

Extended exposure to reliable automation may lead to overreliance as evidenced by poor responses to auto-mation errors. Individual differences in trust may also influence responses. We investigated how these factors affect response to automation errors in a driving simulator study comprised of stop-controlled and uncon-trolled intersections. Drivers experienced reliable vehicle automation during six drives where they indicated if they felt the automation was going too slow or too fast by pressing the accelerator or brake pedal. Engage-ment via pedal presses did not affect the automation but offered an objective measure of trust in automation. In the final drive, an error occurred where the vehicle failed to stop at a stop-controlled intersection. Drivers’ response to the error was inferred from brake presses. Mixture models showed bimodal response times and revealed that drivers with high trust were less likely to respond to automation errors than drivers with low trust.

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety-Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both. This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1,140-min of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, weaving segments, expressway merge/diverge segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multivideo stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based trajectory datasets, CitySim had significantly more safety-critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations’ three-dimensional base maps and signal timings.

An exploration of the effects of various street lighting conditions on driver behaviors and safety at stop-controlled intersections: A driving simulator experiment

Lack of visibility is one of the challenges that impact drivers’ ability to perform driving tasks at night. Although prior studies indicated that lighting increases nighttime safety, lighting at stop-controlled intersections is not mandatory according to many U.S. states’ guidelines. This paper aims to investigate the impact of different lighting conditions on safety-related driving behavior during nighttime at stop-controlled intersections. The data were collected using a driving simulator experiment where participants drove through several intersections during daytime, nighttime without lighting, nighttime with partial lighting, and nighttime with full lighting. Collected measures included speed, time-to-collision (TTC), and post-encroachment time (PET). TTC/PET were used as safety surrogate measures to evaluate the impact of lighting on drivers’ safety. The results indicated that TTC/PET were higher during daytime and nighttime with partial/full lighting. Furthermore, the findings revealed that lower speeds were recorded at nighttime with partial/full lighting. Kruskal–Wallis test revealed that TTC/PET medians for nighttime without lighting were statistically significantly different from TTC/PET medians of the remaining lighting conditions. Moreover, negative binomial modeling indicated that lack of lighting and speed had inverse correlation with TTC. In summary, the results revealed that street lighting at stop-controlled intersections provided 37% increase in TTC, 15% increase in PET, and 15% reduction in speed.

Joint Confidence Region Approach to Ranking Hotspot Locations Considering Uncertainty in Expected Risk Estimates

Network screening or crash hotspot identification is an essential task of all road safety improvement programs. The most common approach to network screening is to use statistical models to predict the expected risk at the locations of interest and then rank them accordingly. The predicted risk used for ranking is mostly in the form of point estimates, without any consideration of the inherent uncertainty with the estimates, which could lead to identifying a wrong list of crash hotspots. This study aims to fill this research gap by employing a frequentist approach to finding a joint confidence region of risk for ranking locations and identification of hotspots. A case study on three-legged minor approach stop-controlled intersections in Kitchener, Ontario, is conducted to illustrate the proposed approach. Crash risk is modeled using a combination of a hierarchical full Bayesian negative binomial model and a multinomial logit model, which are then used to estimate the 95% confidence interval of the expected risk. For each location, the confidence region of rankings is obtained on the basis of the expected risk estimates. The results show that considering uncertainty in the crash hotspot identification process can lead to varied ranking positions for each location. In fact, considering uncertainty, the true value of the estimated crash risk is unknown. By quantifying uncertainty, it can be concluded that the true value of the estimated risk follows a distribution with different probabilities. As a result, consideration of uncertainty in the road safety analysis may help to identify hotspots more accurately.

Operational Performance of Roundabouts at High-Speed and Rural Locations

A recent research project investigated the operational benefits of modern roundabouts for high-speed locations that serve heavy vehicles, while developing examples of best practices for design. The research team compiled proven results from designs in other states and collected and analyzed data from within and outside of Texas to develop updated design guidance, which can be used to implement roundabout designs that accommodate oversize/overweight (OSOW) vehicles in rural areas. This paper summarizes activities and findings from that project. Project activities indicate that the drone-based data collection effort, along with a computer algorithm-based method for data reduction, was an effective and efficient alternative to traditional data collection and reduction methods for this project. Findings from field data and simulation confirmed practices in other states that roundabouts can be designed to accommodate not only heavy vehicles but also OSOW vehicles in rural locations with high-speed approaches. The roundabout with the largest diameter studied (180 ft) had the best performance, but the smallest roundabout (120-ft diameter) was also able to accommodate larger vehicles at lower volumes, and roundabouts of all sizes generally outperformed two-way stop-controlled intersections in simulation results.

Performance and safety effectiveness evaluation of mini-roundabouts in Michigan

This study evaluates the performance and safety effectiveness of mini-roundabouts. Crash, operational, and geometric data for 14 mini-roundabouts located in the state of Michigan were collected for both before and after installation time periods between 2007 and 2016. Three years of complete before and after installation crash data were analyzed utilizing the Before-and-After study with Comparison Group methodology. A total 28 untreated sites with similar characteristics to the 14 mini-roundabouts sites were used to account for changes in crashes unrelated to the treatment (mini-roundabout installation), evaluate the impacts of the treatment on the intersection, and develop the crash modification factors (CMFs). The results for overall safety effectiveness indicate a decrease in total crashes at signalized intersections when converted into mini-roundabouts, and an increase in total crashes at two-way stop-controlled (TWSC) and all-way stop-controlled (AWSC) intersections when converted into mini-roundabouts. The recommended CMFs for converting a TWSC, AWSC, and signalized intersection into a mini-roundabout are 1.039, 1.051, and 0.755 for total crashes, respectively.

Factors Affecting Driver Yielding at Uncontrolled Crosswalks

This study evaluated driver yielding behavior at basic- versus high-visibility crosswalk (HVC) markings on undivided major street approach legs of two-way stop-controlled intersections. The research team measured and compared driver yielding rates using staged pedestrian crossings at sites with HVCs and at sites with basic markings. Study sites were undivided two-lane roadways with relatively low speeds (25 to 35 mph speed limits) and volumes (up to 616 two-way vehicles per hour during crossings)—half with basic crosswalk markings, half with HVCs. In addition to site characteristic data (crossing distance, presence of bike lane, presence of on-street parking, location context), the study included the collection of vehicle volumes and approach speeds, which enabled statistical modeling of yielding in relation to site characteristics and driver speeds. In total, the study collected 1,188 individual observations at 32 sites in four states. The results showed that, under certain conditions, HVCs were associated with increased driver yielding relative to crossings with basic markings. The study further showed that an approaching driver’s speed and prevailing site speeds had a strong and consistent negative effect on driver yielding. Site context and other treatments present at the site had an impact, but the relationship between driver speed and yielding was robust and statistically significant across all tests. Drivers exceeding 40 mph were unlikely to yield to a pedestrian waiting at a marked crosswalk, suggesting that an agency should consider supplemental treatments to increase driver yielding at such locations.

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.

Modeling the performance of restricted crossing U-turn intersections including the effects of connected and autonomous vehicles: a case study in California

Despite numerous studies demonstrating the effectiveness of restricted crossing U-turn (RCUT) intersection design, its implementation remains close to zero in some large states (e.g., California). The study included four locations on high-speed rural expressways (highways with partial access control) in California. The operational evaluation relies on microscopic simulation models of existing two-way stop-controlled (TWSC) intersections and alternate RCUT designs used to estimate network-wide performance measures. Two series of simulation scenarios were tested: (1) scenarios with no connected and autonomous vehicles (CAVs) and (2) scenarios with different SAE International Level 4 CAV market penetration rates (MPRs) to help policymakers and practitioners in future planning strategies. The microsimulation models were also used to generate trajectory data to create a surrogate measure-based approach. Based on the results, the RCUT designs reduced or eliminated the more severe crossing conflicts. Similar vehicle travel times were identified in various CAV MPRs except for one of the locations with challenging geometric features, where travel time increased at higher CAV MPRs.

Three Player Interactions in Urban Settings: Design Challenges for Autonomous Vehicles

An observational analysis of crossing episodes between two intersecting vehicles, in which a third road user clearly affected its evolution, was conducted in an attempt to identify (i) recurring patterns of informal coordination among road users and (ii) traffic situational invariances that may inform AV prediction algorithms. The term BLOCK-EXPLOITING is introduced to describe a driver’s exploitation of situational opportunities to gain priority often contrary to regulatory provisions, but favouring overall traffic efficiency. Video-data from an urban stop-controlled intersection were analysed through the lens of joint systems theory using a phenomenological framework developed in this study. Four generic types of BLOCK-EXPLOITING were identified (i.e. covering, ghost-covering, piggybacking, sneaking). Covering and ghost-covering led to minimal or no delays while piggybacking and sneaking, although abusive to other drivers, still only resulted in 1.99 to 3.33 sec delay. It is advocated that BLOCK-EXPLOITING can be socially acceptable. Proposed design challenges for AVs in mixed traffic include the ability to (i) distinguish BLOCK-EXPLOITING from errant driving, (ii) recognise to whom a ‘space-offering’ is addressed, and (iii) assess the appropriateness or abusiveness of a BLOCK-EXPLOITING action. Finally, this study brings to fore very short-time span joint-activity coordination requirements among diverse agents unknown to each other.

Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic at Two-Way Stop-Controlled Intersections

Connected and automated vehicles (CAVs) hold great promise in enhancing transportation operations and roadway capacity. However, there is still limited guidance in the field of highway capacity analysis to help agencies account for the potential benefits. This paper investigates the impact of CAVs on the capacity of two-way-stop-controlled (TWSC) intersections and quantifies capacity adjustments under different CAV scenarios. An isolated TWSC intersection is considered in this research to analyze the CAV impacts on various intersection movement capacities under different CAV market penetration rates (MPRs) and conflict flow rates (CFRs). To model CAVs, cooperative adaptive cruise control (CACC) is considered because it directly impacts the vehicle’s longitudinal car-following behavior and therefore the capacity. Additionally, enhanced gap-acceptance behavior based on vehicle-to-vehicle (V2V) communication is included because it can benefit minor road movements via advanced notifications. This paper collects data from customized, well-calibrated TWSC simulation models constructed in commercially available software. The results show that with the increase in MPRs, CAVs can substantially enhance the capacity of the stop or yield controlled movements, and the improvement is more significant under low CFR scenarios. CAVs can also reduce the follow-up headway and thus improve the potential capacity based on the existing HCM TWSC capacity estimation method. Finally, based on the data collected from the simulation, a capacity adjustment factor (CAF) table is proposed for HCM implementation.

Safety Effectiveness and the Role of Geometric, Traffic, and Crash History–Related Factors in Converting a Stop-Controlled Intersection to a Miniroundabout

Safety Effectiveness and the Role of Geometric, Traffic, and Crash History–Related Factors in Converting a Stop-Controlled Intersection to a Miniroundabout

Impact of Simulation on Road User Acceptance of Restricted Crossing U-Turns

Restricted Crossing U-turns (RCUTs) show reduction in severe and fatal crashes compared to traditional stop-controlled intersections in rural and suburban areas, but poor community acceptance of these relatively novel road designs can lead to costly delays in installation or cessation of the project. One way to increase road user acceptance of RCUTs and other novel road designs is through the use of persuasive technologies such as virtual environments and simulation. Three studies employing different forms of simulated virtual environments with the same RCUT design were conducted, with participants providing pre- and post-exposure self-report attitudes toward the RCUTs. The results indicated that acceptance towards RCUTs generally improved for two of the three studies that provided a virtual experience driving through an RCUT, but attitudes did not improve for the virtual environment utilizing a highly immersive full-cab simulator. The results have implications for the use of persuasive technologies for novel roadway designs.

Capacity Estimation at All-Way Stop-Controlled Intersections Considering Pedestrian Crossing Effects

All-way stop-controlled (AWSC) intersections are a typical type of unsignalized intersection that has been commonly used when both intersecting roads have a relatively low vehicular traffic demand. At an AWSC intersection, vehicles proceed through the intersection under a “first come, first served” rule. Since AWSC intersections are often deployed in residential or business areas where pedestrian crossings tend to be inevitable. When a driver who has the right-of-way yields to the crossing pedestrians, the right-of-way at the intersection might be reallocated, which could result in capacity losses to the current approach. On the other hand, the conflicting vehicular traffic movement(s) to the current approach may use this gap and proceed through the intersection ahead of this vehicle. Nevertheless, the effects of pedestrian crossings at AWSCs have not been considered by the existing capacity modeling methods. This research develops a method to evaluate the capacity changes caused by pedestrian crossings at AWSC intersections. Field data collected at two representative sites were collected for model calibration and validation. A sensitivity analysis was conducted to reveal the combined effects of different pedestrian crossing movements and under various pedestrian crossing demands. Results indicated that pedestrian crossing movements could change the capacity at AWSC intersections, but the effects vary according to pedestrian activities and vehicle directions.

Evaluation of Enforcement and Messaging Campaign Focused on Reducing Cell Phone-Related Distracted Driving

Cellular telephone use has increased significantly in the United States as 97% of residents now own some type of cell phone. The ubiquity of cell phones has introduced concerns with respect to traffic safety as cell phone-related distractions have been shown to affect driving ability and increase crash risk. Various countermeasures have been implemented to address this issue, including public outreach campaigns and targeted enforcement activities. However, the efficacy of such strategies has been the subject of limited research. This paper examines cell phone use rates in consideration of enforcement activities in conjunction with targeted safety messages on roadside dynamic message signs. Two phases of enforcement were conducted in two urbanized areas of Michigan. Data were collected before, during, and after the enforcement period. A two-way random effects logistic regression model was estimated, and the results showed that cell phone use rates were lower during and, particularly, after the enforcement activities were conducted. Use rates were also found to vary based on age, gender, and race, allowing for the identification of target groups for public awareness and outreach campaigns. Use rates were also lower at freeway exit ramps as compared to signalized and stop-controlled surface street intersections. Lastly, cell phone-specific safety messages were associated with lower use rates compared with other message types, suggesting a potential synergistic effect.