Alternative intersection (AI) designs, such as the Median U-Turn (MUT), Reduced Conflict Intersection (RCI), Continuous Flow Intersection (CFI), and Quadrant Roadway Intersection (QRI), introduce innovative geometric and control features compared to a conventional intersection (CI), which offer the potential for substantial safety and operational improvements. Nevertheless, most AI designs present unconventional ways of maneuvering traffic through an intersection, such as restriction of movements, crossover of traffic to the opposite side of the road, separating left turning movements, etc. As corridor construction or improvement projects continue to utilize AI designs, understanding their impacts on driver behavior, especially when implemented successively along a corridor, is essential for effective deployment. This research developed a comprehensive driving simulator experiment to evaluate driver performance when navigating AI corridors, focusing on four key metrics: number of failure movements (FMs), approach speed (AS), hard-braking events (HBEs), and approach lane changes (ALCs). Three background corridor treatments were investigated: a CI corridor, an RCI corridor, and a corridor with varied AI designs. A total of 12 intersection pairs were created to represent typical and practical combinations of AIs, with each pair consisting of a test intersection and a preceding intersection. Based on data collected from 48 participants, this research found that gender, age, and background corridor treatment did not significantly influence driver behavior. In contrast, trial number, preceding intersection configuration, test intersection movement, and intersection pair were all found to have significant effects. Among the test movements, the MUT side street left-turn presented the highest risk of FMs. Approach speed was lowest at the MUT side street left-turn and highest at the RCI side street through movement, with speeds generally increasing over successive trials. HBEs occurred most frequently at QRI, MUT, and RCI configurations. ALCs were more common when the test intersection was preceded by a CI, with the highest ALCs observed during CI main street left-turns, followed by MUT and CFI configurations. Post-experiment interviews highlighted the importance of clear and reasonably placed traffic signs and pavement markings to inform drivers of unconventional traffic patterns at AIs.

Understanding driver behaviors in varied traffic scenarios is critical to the design of safe and efficient roadways and traffic control device. This research presents an analysis of driver cognitive workload, situation awareness (SA) and performance for three different scenarios, including a standard intersection and contraflow grade-separated intersections (C-GSI) and quadrant GSI (Q-GSI) with lane assignment sign manipulations. The study used a simulator-based driving experiment with application of the NASA Task Load Index and Situation Awareness Global Assessment Technique to assess the influence of the scenarios on driver behavioral responses. The findings reveal challenges for drivers navigating the C-GSI, characterized by diminished SA and elevated workload. These states were associated with behaviors such as delayed lane changes, missed opportunities for appropriate lane changes, heightened acceleration behavior within deceleration segments, and frequent speeding. In contrast, while drivers in the Q-GSI scenario faced elevated workloads, their SA remained steady, largely due to lane-specific signs facilitating early lane changes. Although the Q-GSI led to increased speed variability and slight increases in deceleration, the use of supplementary speed signage revealed a promising alternative to the S-intersection. Correlation analysis highlighted a significant relationship between mental workload and acceleration responses, indicating that increased acceleration was associated with higher mental workload. In addition, a significant negative correlation between driver perceived performance and absolute lane deviations indicated that drivers with higher self-assessed performance were more accurate in lane-keeping. The study underscores the need for GSIs and signage designs that support driver SA, manage cognitive workload to improve driver performance and increase road safety.

A quadrant roadway intersection (QRI) reduces congestion relative to a four-phase intersection. (Note: this study relates to traffic systems where vehicles drive on the right-hand side of the road.) It does this by removing left-turn traffic from the main intersection, resulting in a two-phase signal. Nevertheless, there is a lack of clear understanding of the tradeoffs between savings in control delay versus extra travel time experienced by the rerouted movements. This research compared the operational performance of five QRI designs with the counterpart conventional intersection (CI) under various traffic demand scenarios via TransModeler microsimulation modeling. Three measures-of-effectiveness (MOEs) were employed: time-in-system (TIS), control delay, and intersection capacity utilization. Simulation results show that all QRI designs outperform CI design for all three MOEs under all demand scenarios. QRIs with direct left-turn design have a smaller average TIS than those with loop left-turn design, indicating that savings in control delays did not offset the extra travel times. Under a relatively low demand condition, a single QRI design can generally balance the tradeoffs between control delay and extra travel time. Under a high demand scenario, a dual or full QRI with direct left-turns is preferred, since it reroutes or partially reroutes left- and right-turn traffic to secondary intersections, thus the main intersection has a lower capacity utilization and can accommodate more through-traffic demands than CI, single QRI, and dual or full QRIs with loop left-turns.

An offset T-intersection splits a conventional four leg intersection into two three-leg T-intersections to reduce the number of conflicts. While the safety benefits of offset T-intersections have been widely documented, the effects on operations are not well understood. To fix that, this paper employed microsimulation modeling to investigate the differences in operational performance between offset T-intersections and four-leg standard intersections under various traffic demands, intersection spacings, and signal timing schemes for three development types: superstore, hybrid gas station, and residential area. Queue length and delay were employed as measurements of effectiveness. Based on microsimulation modeling, we found that under most of the tested scenarios, offset T-intersections were superior to four-leg intersections in terms of reducing delay for the main street traffic. In addition, we found that the left–right (L-R) offset T-intersection configuration outperformed the right-left (R-L) offset configuration in terms of preventing main-street left turn queue spillback. Based on the simulation results, the paper provided practice-ready guidelines on selecting an optimum intersection configuration for each specific development type given the volume demands and known geometric constraints for a given site.

Traditional crash frequency models cannot estimate crash frequency for individual traffic movements at an intersection, which precludes the safety evaluation of individual movements and identification of hazardous ones. This paper proposes a movement-based (MB) model that estimates crash frequency for individual movements as well as for the entire intersection. A base model using the safety performance function form in the Highway Safety Manual was also developed for comparison against the MB model. This study used crashes collected for five to eight years at 41 signalized intersections in North Carolina for the model estimation and validation (21 intersections for the estimation and 20 intersections for the validation). The models were validated using cumulative residual plots, test set validation, and in a case study. The test set validation showed that the MB model yielded slight improvements in estimations compared to the base model (1.17%-5.83% reductions in mean absolute error and 3.32%-6.64% reductions in root-mean-square error). The case study showed the MB model correctly identified hazardous traffic movements that had demonstrable safety problems based on observed and estimated crash frequencies. The MB model will enable engineers to identify hazardous movements and approaches to implement safety improvement countermeasures at the deserving locations and movements.

This study investigated the safety effects of the conversion from a protected-only left turn to protected-permissive left turn with flashing yellow arrow (FYA−PPLT) with time-of-day operation. The observational before–after study with the comparison group method was used to develop crash modification factors (CMFs) for the total crashes and two types of target crashes that involve left-turning vehicles on treated approaches (left-turn−same roadway and rear-end crashes). For all potential crashes, crash reports, crash diagrams, and narratives were manually inspected to identify the actual target crashes. The CMFs were separately developed for a full 24-h day and specific time-of-day based on FYA-PPLT operation, and for two severity categories (all severities and fatal & injury). The results showed that the total crashes for 24 h had a slight increase, whereas the target crashes had substantially larger changes (increase in left-turn−same roadway crashes and decrease in rear-end crashes). When looking at the specific time-of-day use of FYA-PPLT, the increase in left-turn−same roadway crashes was significantly higher than the full 24-h period. This implies that engineers could extend protected-only use more on the fringes of the peak periods to mitigate the increase in left-turn-related crashes. The analysis results also showed a trade-off between left-turn–same roadway and rear-end crashes after the signal conversion. The findings reveal how important it is for engineers to properly understand the safety effects on the different categories of target crashes and time periods when considering the signal conversion from a protected-only left turn to FYA-PPLT.

With the advances in vehicle technologies, more information is communicated in real-time to the driver via an in-vehicle interface. In-vehicle messaging may deliver safety-related information such as warnings as well as non-safety-related information such as an upcoming lodging place. While much research has focused on the design of messaging safety-related information, little is known about the best practice in in-vehicle messaging of non-safety-related information. This study investigated the effects of information source and load on driver signage logo identification, glance behavior, and vehicle control among younger, middle-aged and older drivers. The logos were presented on: (1) an on-road sign panel, (2) an in-vehicle display, or (3) a combination of both, with half of the drives showing logo only, and the other half of the drives showing logo plus additional text. The general findings support the use of in-vehicle displays, especially when it is presented simultaneously with on-road signs. In-vehicle displays did not lead to a higher workload or more visual distraction, and simultaneous presentations resulted in slightly better speed control. The findings also showed minimal negative impacts on logo identification from increased information load. Older drivers performed less well in signage identification and vehicle control, and they made longer glances to logo information suggesting design considerations should be made to accommodate specific driver characteristics.

As automated vehicles become more prevalent on roadways, it is necessary to study driver behaviors in interacting with such systems. With higher levels of vehicle automation, drivers may become less engaged with the roadway environment. As a result, how to effectively bring non-safety related information (e.g., guide and service sign content) to a driver’s attention is an open research question. In this review, we summarize current literature on three domains of research, including: (1) the design and effectiveness of traditional road signage, (2) human factors considerations in vehicle automation design, and (3) current design guidelines for in-vehicle information presentation. Based on the review, including empirical studies, we identify knowledge relevant to communicating road signage information in automated vehicles. We propose a framework highlighting various factors that could determine the effectiveness of in-vehicle messaging. The framework is intended to motivate future research on development of in-vehicle interfaces for highly automated driving.

Many state departments of transportation have designed and implemented grade-separated interchanges (GSI) to increase overall capacity at traffic intersections. As compared with traditional intersections, GSI designs are less familiar to drivers and, even with guidance signs, they are more likely to lead to erroneous driving actions. Based on the current literature, there is a need for predictive models of driver behavior based on GSI configurations and sign settings (i.e., use and placement of guidance signs). We evaluated multiple machine learning models for predicting whether an interchange design/configuration and sign settings are associated with erroneous driver actions, given different cognitive (workload and situation awareness) states. Results revealed a decision tree (DT) model to produce the highest prediction performance. Furthermore, based on importance scores for each factor in the DT, we found that interchange configuration was dominant in predicting erroneous driving outcomes, while cognitive workload and sign settings also played important roles in classification.

In recent years, there has been a push towards use of grade-separated interchange (GSI) design to increase the overall capacity of intersections. The primary recommendation has been to resolve physical intersection constraints, including signalized left turns (in the U.S.). However, few, if any, investigations have made comparisons of driver situation awareness (SA) and cognitive workload in navigating novel grade-separated configurations and how to effectively implement associated signage to promote driver and traffic safety at different types of interchanges. To address this research gap, this study designed and conducted a driving simulation experiment to compare driver SA and cognitive workload in negotiating standard GSIs vs. novel GSI conditions, including contra-flow and quadrant configurations. All GSIs accommodated cross-traffic flows (north, south, east, and west) with four-lane roadways running in each direction through urban environments. The experiment also manipulated driver exposure to lane assignment (LA) signs (present and absent) and decision point (DP) signs with either overhead or right-side mount configurations. Forty-eight (48) licensed drivers participated in the experiment with each driver experiencing each GSI configuration in two trials for a total of six experiment trials for each participant (total of 288 trials). Participants in the experiment were divided into two groups according to age, including young (18-24 yrs.) and middle-aged (25-64 yrs.). The participants were also assigned to unique combinations of LA and DP signs (LA present + DP overhead; LA present + DP right-side mounted; LA absent + DP overhead; LA absent + DP right-side mounted), which remained consistent across GSI configurations for each driver. A high-fidelity and full-motion driving simulator was used in this study. During each trial, a driver was required to maintain posted speed limits and to achieve a pre-identified destination (“Garden St. North), as posted on the LA and DP signs. At specific stopping points in each test trial, driver SA was assessed using the Situation Awareness Global Assessment Technique (SAGAT). The simulation scenario was frozen and drivers were posed with multiple queries addressing perception, comprehension, and projection of roadway conditions, vehicle and traffic states, and routes. Qualtrics survey software was used to present questions in an electronic format (using driver mobile devices) with all being randomly selected from a large pool of questions on the driving environment. Driver responses to queries were graded based on recordings of ground-truth simulator settings. That SAGAT output as a percentage of correct responses to all queries delivered at a simulation freeze with range [0,1]. Driver cognitive workload was assessed using the NASA Task Load index. The purpose of using this index was to determine the cognitive load imposed on drivers by the signage conditions in negotiating the various types of GSIs. At the beginning of the experiment, participants ranked the importance of six workload demand components, including mental, physical, temporal, performance, effort, and frustration for the driving task. At the end of each test trial, participants rated their perceived mental workload, according to the various demand components on a 100-point scale. The NASA TLX was calculated as the rank-weighted sum of the demand ratings scaled from 0 to 100 points. The results revealed driver SA and workload to significantly differ among GSIs. The standard and contra-flow GSIs were not different in driver SA but both were superior to the quadrant configuration. There were no significant differences in SA detected for the use (LA) and placement (DP) of signs. Regarding cognitive workload, results corresponded with SA findings, indicating the standard and contra-flow GSIs produced lower cognitive demands for drivers than the quadrant configuration. However, there were no significant differences in cognitive workload detected between the use and placement of signs. No interactions were detected among the GSI configurations and use and placement of signs for both SA and cognitive workload. In addition, correlation analyses were also applied to the SA and workload responses. Results indicated that SA and workload were complimentary in the context of the present experiment and they represent unique methods for assessing human behavior/performance in driving research.On the basis of these results, it was concluded that novel GSI designs influence driver SA and workload responses compared with standard interchanges; however, the presence of LA signs and positioning of DP signs does not appear to positively influence these responses. There is a need for additional empirical driving research to determine what aspects of GSI geometry and other traffic control devices may serve to promote comparable levels of driver SA and workload for new designs as compared to standard interchanges.

Alternative Intersection designs, such as Restricted Crossing U-Turn (RCUT) intersections, have the potential of bringing in significant operational and safety improvements compared to conventional intersections. Nevertheless, as corridor construction or improvement projects increase utilization of RCUT designs, there is a concern that drivers might be confused on how to safely navigate a corridor when adjacent intersections handle movements in different ways. With this concern, this research employed focus groups to gain a better understanding of drivers’ attitude with the RCUT intersection design, and how they would navigate it when approaching an individual RCUT intersection and a sequence of RCUT intersections. Two groups of participants were recruited to compare drivers who had some experience with RCUT intersections to those unfamiliar with them. Videos of the potential scenarios they might encounter while driving on a RCUT corridor were presented via Adobe Connect and Zoom, and driver behavior-related questions were collected. Moreover, through facilitator-led discussions, this research investigated what roadway signs or other clues were useful for participants determining where they should be, and their concerns and opinions on navigating a corridor with RCUT intersections. Results show that both familiar and unfamiliar groups wanted more descriptive signs and pavement markings and wanted them to be displayed earlier so that they can more timely recognize the alternative intersection configuration, and accordingly make adequate lane-selection maneuvers.

This document provides information and guidance on the Diverging Diamond Interchange (DDI). To the extent possible, the guide addresses a variety of conditions found in the United States, to achieve designs suitable for a wide array of potential users. This guide provides general information, planning techniques, evaluation procedures for assessing safety and operational performance, design guidelines, and principles to be considered for selecting and designing Diverging Diamond Interchanges.

This study evaluated the performance of pedestrian-bicycle crossing alternatives at Continuous Flow Intersections (CFI). Further, a comparison was also performed of CFI crossing types against a standard intersection designed to provide an equivalent volume-to-capacity ratio. Three CFI crossing alternatives were tested, namely Traditional, Offset, and Midblock crossings. In total, 12 alternative scenarios were generated by incorporating two bicycle path types and two right-turn control types. These scenarios were analyzed through microsimulation on the basis of stopped delay and number of stops. Simulation results revealed that the Offset crossing alternative incurred the least stopped delay for all user classes, including motorized traffic. The Traditional crossing generated the least number of stops for most route types. The Midblock crossing can be considered as a supplement to either the Offset or Traditional crossing depending on the specific origin-destination patterns at the intersection. The exclusive bicycle path performed better than the shared-use path in most cases. When compared with an equivalent standard intersection, aggregated results showed significant improvement for all CFI crossing types with respect to stopped delay, but the standard intersection had an equal or fewer number of stops for most routes investigated. Regarding the effect on vehicular movement, the lowest volume-to-capacity ratio of the main intersection was incurred by the Offset crossing. Future research includes incorporating pedestrian-bicycle safety, comfort, and the relative effects of these crossing alternatives on additional vehicular performance measures.

This study investigates the presentation of service logo information under partially automated driving. Drivers completed simulated drives with partial automation during which they had to detect target logo signs and react to hazards by taking over vehicle control when needed. Driver performance was measured in terms of sign detection rate, crash rate, and hazard response time. A number of factors, including sign information source, sign information load, and driver age group, were investigated. In general, our findings support the delivery of service logo information via in-vehicle display under partially automated driving, especially when the in-vehicle display occurred simultaneously with the on-road signage. Under this presentation condition, drivers were most accurate in detecting target logo signs, and showed little impairment from processing sign information as a secondary task when negotiating a hazard. Implications of the findings and future directions were discussed.

With advances in vehicle technologies, more information can be communicated in real-time to drivers via invehicle interfaces. In-vehicle messaging can be used for safetyrelated information, such as warnings, as well as non-safetyrelated information, such as upcoming gas stations. While much research has focused on the design of messaging safety-related information, little is known about the best practice for in-vehicle messaging non-safety-related information. The current study aimed to examine how drivers process service logos - as an example of non-safety-related information - and respond to road hazards when logos are presented on: (1) an on-road sign panel, (2) an in-vehicle display, or (3) a combination of both. It was found that drivers generally identified logos with high accuracy and low workload across the presentation conditions. Driver reactions to road hazards were slower when logos were present for processing, although the number of collisions did not increase. Although the majority of drivers self-reported a preference for the on-road presentation, the simultaneous presentation of logos on-road and in-vehicle showed a benefit on driver hazard negotiation (fewer collisions). Older drivers were less accurate in identifying logos but also had fewer collisions, likely due to them being more cautious and allocating more attention to the driving task. Findings of this study provide support for use of in-vehicle presentation of non-safety-related information in addition to existing on-road signage.

In-vehicle technologies for communicating information to drivers have realized increasing use in recent years. While most attention has been paid to in-vehicle displays for presenting safety-related information, such as warnings, few studies have explored communication of non-safety-related information with in-vehicle displays. This simulated driving study examined driver performance in vehicle control and sign identification, when processing service logo information from on-road signs or an in-vehicle display. Findings suggest that in-vehicle displays, on-road signage, or both allowed drivers to identify service logos with a high accuracy and a relatively low level of workload. The use of in-vehicle displays either alone or simultaneously with on-road signage produced lower speed deviations therefore better vehicle control. Age differences were observed in vehicle control as well, suggesting the need for personalization of sign messages according to individual characteristics. This study is an initial step to examine the use of in-vehicle displays for messaging service logos as an example of non-safety-related information. The study is ongoing, and findings could provide a basis for in-vehicle display and on-road sign design for non-safety-related information.

A national-level safety evaluation of Diverging Diamond Interchanges (DDIs) in the United States was completed. This study aimed to update previous evaluations and to expand the treatment group size of previous studies to provide a more robust and reliable safety assessment of DDI deployments. For this particular treatment, it was determined that, of the observational before-and-after evaluation methodologies, the comparison group approach yields the best evaluation results. The naïve method can be influenced by outside factors that cannot be accounted for (weather, crash reporting tendencies, etc.). The empirical Bayes method is unnecessary as DDIs are installed for operational benefits, meaning that risk of selection bias and regression-to-the-mean is minimal. This study recommends a total crashes crash modification factor (CMF) of 0.633 based on the comparison group analysis of 26 DDIs in 11 states. The comparison group method was also applied to a variety of crash variables for this study. Angle, rear-end, and sideswipe crashes were found to have CMFs of 0.441, 0.549, and 1.139, respectively. Fatal-and-injury crashes provided a CMF of 0.461. Daytime and nighttime crashes provided CMFs of 0.648 and 0.638, respectively.