Gap Acceptance Decisions Research Articles

How Sure is the Driver? Modelling Drivers’ Confidence in Left-Turn Gap Acceptance Decisions

When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N = 17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift–diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task.

Modeling drivers’ dilemma at unsignalized T-intersections under mixed traffic conditions: A case study from India

Objective At unsignalized T-intersections, right turning drivers from major or minor roads have to accept or reject the available gap and cross the intersection. Poor judgment may arise risk of collision with the major road conflicting vehicles. Subject drivers intending to cross the intersection encounter wide range of gaps. Drivers accept large gaps and reject small gaps. However, drivers experience dilemma over a wide range of gaps. This study aims to examine dilemma experienced by the right turning drivers by modeling gap acceptance and rejection decisions for estimating dilemma zone boundaries on major road. Methods Videographic method was considered for collecting traffic movement data at unsignalized T-intersections. Traffic video data was collected at three specific unsignalized T-intersections with varying degree of channelization. After this, accepted and rejected gaps with different traffic characteristics of offending vehicles (subject drivers) and conflicting vehicles such as speed, distance, vehicle type, etc. for right turning movements were extracted from the video data to analyze gap acceptance and rejection decisions of the drivers and estimate dilemma zone boundaries. Results Gap decisions (acceptance or rejection) were modeled using Generalized Linear Model (GLM) as a function of conflicting vehicle type, speed, and distance from the intersection along with degree of channelization for major and minor roads. The results showed that gap rejection probability increased with increment in conflicting vehicle’s speed and decrement in conflicting vehicle’s distance from the intersection. Dilemma zone boundaries were obtained using developed GLM models by estimating the conflicting vehicle distance from the intersection for 90% and 10% gap rejection probabilities. Dilemma zone boundaries were observed to shift farther from the intersection with increase in vehicle speed and vehicle size. The analysis revealed that subject drivers experienced more dilemma while accepting a gap from major road as compared to minor road. Conclusions This study showed that channelization plays a major role in mitigating dilemma of the subject drivers. Overall, the study identified dilemma zone boundaries for unsignalized T-intersections which may assist the right turning subject drivers to cross the intersection safely.

Should I stay or should I go? The cerebral bases of street-crossing decision.

An observer willing to cross a street must first estimate if the approaching cars offer enough time to safely complete the task. The brain areas supporting this perception, known as Time-To-Contact (TTC) perception, have been mainly studied through noninvasive correlational approaches. We carried out an experiment in which patients were tested during an awake brain surgery electrostimulation mapping to examine the causal implication of various brain areas in the street-crossing decision process. Forty patients were tested in a gap acceptance task before their surgery to establish a baseline performance. The task was individually adapted upon this baseline level and carried out during their surgery. We acquired and normalized to MNI space the coordinates of the functional areas that influenced task performance. A total of 103 stimulation sites were tested, allowing to establish a large map of the areas involved in the street-crossing decision. Multiple sites were found to impact the gap acceptance decision. A direct implication was however found mostly for sites within the right parietal lobe, while indirect implication was found for sites within the language, motor, or attentional networks. The right parietal lobe can be considered as causally influencing the gap acceptance decision. Other positive sites were all accompanied with dysfunction in other cognitive functions, and therefore should probably not be considered as the site of TTC estimation.

Response times in drivers' gap acceptance decisions during overtaking

Overtaking on two-lane roads can lead to increased collision risks due to drivers' errors in evaluating whether or not to accept the gap to the vehicle in the opposite lane. Understanding these gap acceptance decisions can help mitigate the risks associated with overtaking. Previous research on overtaking has focused on the factors influencing gap acceptance decisions. However, the cognitive processes underlying gap acceptance decisions remain poorly understood. Previous studies have shown that response time (i.e. the time it takes the driver to evaluate the gap and make a decision) can provide valuable insights into the cognitive processes during gap acceptance decisions, in particular in pedestrian crossing and left turn decisions. However, the more complex nature of the overtaking maneuver renders it difficult to measure response times in overtaking. As a result, response times in overtaking have not been investigated, thereby limiting our understanding of overtaking behavior. To address this gap, in this paper we propose a method to measure response time in drivers' overtaking decisions and demonstrate this method in a driving simulator experiment (N=25). Specifically, we analyzed the effect of distance to the oncoming vehicle and speed of the ego vehicle on response time in accepted and rejected gaps. We found that response times for rejected gaps were on average longer than for accepted gaps. The response times increased with the distance gap and decreased with the initial velocity of the ego vehicle. We conclude that using the proposed method for measuring response time can give insight in the way drivers make gap acceptance decisions during overtaking. These results provide basis for cognitive process models that can help further understand overtaking decisions.

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers.

We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.

Simulation of Vehicles’ Gap Acceptance Decision at Unsignalized Intersections Using SUMO

In this paper, an artificial neural network (ANN)-based gap acceptance behavior model was proposed. The feasibility of implementing this model in a microscopic simulation tool was tested using the application programming interface of Simulation of Urban Mobility (SUMO) simulation package. A stop-controlled intersection in New Jersey was selected as a case study. The simulation model of this intersection was calibrated using ground truth data extracted during the afternoon peak hours. The ANN-based SUMO model was compared to SUMO model with default gap acceptance parameters and the SUMO model with calibrated gap acceptance parameters. The comparison was based on wait time and accepted gap values at the minor approach of the intersection. The results showed that the ANN-based model produced superior results based on the selected outputs. The analysis results also indicated that the ANN-based model leads to significantly more realistic driving behavior of vehicles on the major approach of the intersection.

Analyzing the impact of time pressure on drivers’ safety by assessing gap-acceptance behavior at un-signalized intersections

Time pressure is a driving condition experienced by drivers in day-to-day life. However, limited studies are conducted on assessing the effects of time pressure on driving performance, particularly in Low-and-Middle Income Countries. Further, the majority of road safety research in Low-and-Middle Income Countries is based on the assumption of consistency and homogeneity of driving condition without taking into account the risk tolerance of the drivers which highly governs the driving performance and decision making while driving. Thus, the current study was conducted to overcome this particular aspect by examining the gap-acceptance decision of the drivers and its implication on safety at un-signalized intersections under time pressure driving conditions. Ninety-four participants’ data were collected on the driving simulator located at Indian Institute of Technology Bombay in three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The accepted gaps were analyzed using a decision tree which revealed that merge distance of oncoming vehicles, speed reduction, gender, regular exercise, and driving profession along with time pressure significantly influenced the gap-acceptance decisions. Compared to NTP driving condition, the accident probability was observed to increase by 127% and 181% in LTP and HTP driving conditions, respectively. The overall findings from the current study aids in understanding the effect of time pressure on drivers’ decision making and safety as well as highlight the role of human factors that needs to be specifically incorporated in the road safety research conducted in Low-and-Middle Income Countries to avoid safety–critical situations.

To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossings

IntroductionPedestrians are vulnerable road users exposed at risk during their interaction with vehicles in uncontrolled urban areas. In this paper, a critical overview of the literature and meta-analyses were conducted on the topic of pedestrian gap acceptance choice during midblock street crossings. MethodΑ large number of studies examining personal and contextual factors affecting midblock crossing choices were identified. In an effort to condense research outputs, a quantitative approach was adopted alongside qualitative assessments. Meta-analyses were conducted to summarize the impacts of various predictor variables on pedestrian gap acceptance probabilities from binary logistic models in midblock locations. After application of a rigorous set of criteria, 14 publications were considered appropriate to conduct meta-analyses on beta coefficients of four factors: vehicle speed (VS), gap size (GS), waiting time (WT) and frequency of attempts (FA). ResultsStatistically significant results were obtained for VS, GS and FA. Specifically, it was determined that for one unit increase in incoming VS, there is a 10% decrease (OR=0.9) in the odds of pedestrians crossing the road (accepting the incoming time gap) for pedestrians. For one unit increase in temporal GS, the odds of crossing the road become 7.22 times larger for pedestrians. For each crossing attempt, as measured by FA, the odds of crossing the road become 16.6 times larger for pedestrians. WT was determined to have a non-significant impact on pedestrian crossing odds. ConclusionsTo the best of the authors' knowledge, this is the first time that a meta-analysis of the critical factors influencing gap acceptance of pedestrians in midblock street crossings is carried out. Study findings are useful for practitioners and policymakers to formulate appropriate management plans to reduce interactions of pedestrians and vehicles at uncontrolled urban midblock locations. Future research is needed to further understand the determinants of gap choice decision making.

Multi-state semi-Markov modeling of recurrent events: Estimating driver waiting time at semi-controlled crosswalks

At “semi-controlled” crosswalks, signs and markings are present, but delay to pedestrians and motorists is largely the result of the “negotiation” between the two parties to determine who yields. This paper proposes a novel approach using multi-state semi-Markov models to investigate motorists’ delay and their interactions with pedestrians. Motorist waiting behavior can be divided into a series of gap acceptance decisions as part of a Markov Chain. Each gap acceptance decision is modeled as a specific transition between two states in the Markov Chain.To demonstrate the reliability of the proposed models, multi-state semi-Markov models are estimated for the waiting behavior of more than 1,000 drivers in the presence of pedestrians at semi-controlled crosswalks. The multi-state semi-Markov models are capable of dealing with specific challenges related to (i) the need to account for recurrent events and (ii) a generalized framework for vehicle delay estimation and simulation at semi-controlled crosswalks. The extent to which motorists behave more aggressively and impatiently as their delay increases is demonstrated. Differences in behavior for operators of buses and trucks were also identified. The semi-Markov method is also able to deal effectively with the “pulsing” arrival patterns of pedestrians at crosswalks as university classes begin and end nearby and handle temporal heterogeneity. Finally, to address aggressive driver behavior, several safety implications are discussed.

Drivers’ gap acceptance behaviours at intersections: A driving simulator study to understand the impact of mobile phone visual-manual interactions

Mobile phone use is often considered to be the main source of distraction on the road. Gap acceptance at intersections is a frequent and complex driving task that requires high visual attention from drivers. This study aims to investigate the effect of mobile phone use on the gap acceptance manoeuvre at intersections. Different mobile phone use positions, intersection type, gap size and driver characteristics were considered in the study. A total of 41 licenced drivers drove in an advanced driving simulator in three phone use conditions: baseline (no phone use), using the phone under the steering wheel (covert) and using the phone above the steering wheel (overt). Drivers drove the simulator three times and experienced two intersection types (straight-forward vs. left-turn) and two gap sizes (4 s vs. 7 s) during each drive. A parametric accelerated failure time (AFT) duration model was developed to evaluate the intersection crossing completion time of drivers. The results showed no significant difference of gap acceptance behaviours between the two phone use positions. The distraction task did not affect drivers’ gap acceptance decision, but it increased the crossing completion time by over 10 % compared to baseline. Besides, drivers behaved conservatively at intersections while using a mobile phone, such as adopting a larger deceleration, waiting a longer time, and mainting a larger distance to the front vehicle, etc. However, these compensational behaviours were not helpful in improving the intersection traffic situation regarding both safety and efficiency. Intersection type and gap size were both significant factors of gap acceptance decision and crossing completion time. Additionally, younger drivers were more likely to accept a gap than older drivers, and female drivers spent longer time to cross the intersection than males.

Gap acceptance probability model for pedestrians at unsignalized mid-block crosswalks based on logistic regression

Gap acceptance represents a pedestrian’s assessment of how safe it may be to use an available gap in traffic flow at a particular point in time. Though walking is a major component of urban mobility, the high rate of fatal interaction with motor vehicle traffic raises safety issues around how pedestrians decide to accept the available gap. This paper explored these interactions by modeling gap acceptance behavior at the midblock crosswalks. Unlike other pedestrian gap acceptance studies that focus on individual psychological and sociological factors that are difficult to control or manage, this study focused on six environmental factors that we considered important and as having the potential to affect the pedestrians’ gap acceptance decision at the crosswalks, i.e. gap size, crossing distance, number of waiting pedestrians, waiting time, vehicle traffic volume and position of pedestrian (whether on street kerb or median). Video data was collected on pedestrian gap acceptance from 13 midblock crosswalk locations in Shanghai, China. A Logit model with 96% accuracy was developed to describe and predict the pedestrian gap acceptance behaviors. The results show that gap size and crossing distance have the highest effect on the pedestrian gap acceptance decision. Pedestrians waiting at the kerbside could confidently accept gaps (with a 95% probability) when the gap is longer than 2.2s, 5.9s, and 9.6s under the condition that the crossing distance is 4 m (one lane), 7.5 m (two lanes), and 11 m (three lanes), respectively while pedestrians waiting at the median could confidently accept gaps when the gap is longer than 1.6s, 5.3s, and 8.5s respectively under the same conditions. The recommendations on improving the crossing safety are proposed accordingly.

Horizontal and Vertical Curvature and its Effects on Driver Passing Choice

Passing maneuvers on rural two-lane highways require drivers to enter the opposing traffic lane to overtake an impeding vehicle. A successful maneuver requires the driver to correctly judge the time it will take to complete the pass, and the distance to and speed of the oncoming vehicle. Previous studies have shown that the type and speed of impeding vehicle, traffic volume, roadway cross-section, and driver characteristics influence gap-acceptance behavior, but have not considered vertical curvature or specified directionality in the horizontal curvature. This paper describes a driving simulation experiment in which these geometric, situational, and driver characteristic variables were collected for 643 passing attempts. A logistic regression model was developed to infer the effects of horizontal and vertical curvature on driver passing decisions. This study uniquely quantified that drivers were: more likely to pass if the road curved to the left than if it was straight and less likely to pass if the road curved to the right; more likely to pass if there was a sag curve than if the road was flat and less likely to pass if there was a crest curve; and less likely to pass when traveling uphill than if the road was level. As the influence of roadway geometry on gap-acceptance decisions is not currently implemented in overtaking models, the results of this study have practical implications for microsimulation of rural two-lane highways, highway design, and highway safety, and the inclusion of roadway geometry variables may improve future modeling of roadway capacity and passing locations along two-lane highways.

Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors

Driving behaviour is an inherently complex process affected by various factors ranging from network topography, traffic conditions and vehicle features to driver characteristics like age, experience, aggressiveness and emotional state. Among these, the effects of emotional state and stress have received considerable attention in the context of crash analysis and safety research where driving behaviour has been found to be affected by drivers’ mental state/stress, cognitive workload and distraction. However, these studies are mostly based on questionnaire surveys and self-reports which can be prone to response bias and reporting/measurement errors. The analyses are also often descriptive in nature. In a parallel stream of research, advances in sensor technologies have made it possible to observe drivers’ stress through human physiological responses, e.g. heart rate, electro-dermal activity etc. However, these studies have primarily focused on detecting stress rather than quantifying or modelling its effects on driving decisions. The present paper combines these two approaches in a single framework and investigates the gap-acceptance behaviour of drivers during an intersection crossing, using data collected using a driving simulator. The participants are deliberately subjected to stress induced by time pressure, and their stress levels are measured using two physiological indicators, namely Electrodermal Activity (skin conductance) and heart rate. In addition to statistical analyses, discrete choice models are developed to link the accept-reject choices of a driver with the driver demographics, traffic conditions and stress levels. The results of the models indicate that increased stress levels significantly increase the probabilities of accepting a gap. The improvement in model fit and safety implications derived from model estimates are also discussed. The insights from the results can be used for designing appropriate intervention strategies to improve safety.

Group Gap Acceptance: A New Method to Analyze Driver Behavior and Estimate the Critical Gap at Multilane Roundabouts

The operations of multilane roundabouts, especially three-lane roundabouts, are unique and more complicated than any other type of roundabouts. This study aims to analyze driver behavior and estimate the critical gap at three-lane roundabouts. Video data were collected at two roundabouts. The analysis identified a pattern of group gap acceptance, where vehicles entering the roundabout from different lanes moved in groups during the same gap. In this case, the decision of vehicles entering from outside lanes greatly depended on the gap acceptance decision of vehicles in the inside lane. Analysis showed that the vast majority of the vehicles accept the gap in groups and the critical gap was estimated accordingly. The study provides a new explanation for the operation at multilane roundabouts. The use of this simple method is recommended when estimating critical gaps for multilane roundabouts.

Evaluation of dynamic passing gap acceptance on two-lane highways using field data

Gap availability is an important element of safe passing on two-lane highways. Time gaps are used to determine passing behaviour based on human factors. In this paper, the decision whether to accept or reject an available passing gap is modelled using logistic regression technique that included driver characteristics (age and experience) and the gap size. Field studies were conducted to collect experimental data regarding passing driver behaviour. The data were collected using dual camera Car DVRs and a GPS data logger device that records the instantaneous speed and position of the three vehicles involved in the passing maneuver: passing vehicle, impeding vehicle, and opposing vehicle. Regression models that include driver age and gender (required as input to the gap acceptance model) were established for initial passing time, starting gap, ending gap, and time to collision. The gap acceptance model was implemented in Simulink and the results revealed that driver characteristics significantly affect gap acceptance decisions.

Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests

Abstract: Driver's gap acceptance behaviour highly influences the performance and safety of unsignalized intersections. At unsignalized intersections, crossing drivers have to accept or reject the available gap. The gap acceptance decision is influenced by different dynamics such as traffic, geometric, environmental and human factors. The decision to cross the major road largely depends on the speed, distance and the heaviness of the potentially conflicting vehicle on the major road. The driver's gap acceptance data was collected at 4-legged unsignalized intersections. The information such as the type of crossing vehicle, conflicting vehicle, speed and the spatial gap was extracted from the video data. This paper deals with the application of three widely used non-parametric data mining techniques, namely, Support Vector Machines (SVMs), Random Forests (RF) and Decision Trees (DT) to predict the gap acceptance behaviour of the driver. While SVMs are insensitive to class imbalance, decision tree generated by CART algorithm provides critical insights into decision making process employed by the driver. Random forests and decision tree implicitly establish the relative importance of different factors affecting the driver's decision. Further, skill scores used to validate the models revealed that SVM and DT models performed almost similarly whereas, RF model outperformed SVM and DT.

Passing Behavior on Two-Lane Roads in Real and Simulated Environments

Passing maneuvers allow faster drivers to continue driving at their desired speeds without being delayed behind impeding vehicles. On two-lane rural roads, such maneuvers require the passing driver to occupy the opposing lane; this condition has tremendous implications for the safety and operation of two-lane roads. Several studies have investigated the passing behavior of drivers, and some studies have used driving simulators to analyze drivers’ behavior during following and passing maneuvers. However, the validity of simulators has not been ensured, because their results have rarely been compared with real data. The objective of this study was to compare drivers’ passing behavior as observed in the field with passing behavior in a driving simulator. For this purpose, data on passing performance and passing gap acceptance decisions were required. The study carried out a comparative analysis of the most significant variables related to passing behavior. The results showed similarities between passing time and passing distance of completed maneuvers (during the occupation of the opposing lane). However, drivers passed faster in the driving simulator and maintained greater clearances. Gap acceptance decisions were found to be similar, as the distributions of accepted and rejected gaps were similar, although critical gaps were found to be lower in the driving simulator. This finding might be explained by the absence of objective risks. The applicability of driving simulation seems reasonable, although some improvements are still possible, to account for sight distance limitations, replicate age and gender distributions, and reproduce the opposing traffic flow.

Pedestrian temporal and spatial gap acceptance at mid-block street crossing in developing world

IntroductionMost of the midblock pedestrian crossings on urban roads in India are uncontrolled; wherein the high degree of discretion in pedestrians' behavior while crossing the traffic stream, has made the situation complex to analyze. Vehicles do not yield to pedestrians, even though the traffic laws give priority to pedestrians over motorized vehicles at unsignalized pedestrian crossings. Therefore, a pedestrian has to decide if an available gap is safe or not for crossing. MethodThis paper aims to investigate pedestrian temporal and spatial gap acceptance for midblock street crossings. Field data were collected using video camera at two midblock pedestrian crossings. The data extraction in laboratory resulted in 1107 pedestrian gaps. Available gaps, pedestrians' decision, traffic volume, etc. were extracted from the videos. While crossing a road with multiple lanes, rolling gap acceptance behavior was observed. Using binary logit analysis, six utility models were developed, three each for temporal and spatial gaps. Results and conclusionsThe 50th percentile temporal and spatial gaps ranged from 4.1 to 4.8s and 67 to 79m respectively, whereas the 85th percentile temporal and spatial gaps ranged from 5 to 5.8s and 82 to 95m respectively. These gap values were smaller than that reported in the studies in developed countries. The speed of conflicting vehicle was found to be significant in spatial gap but not in temporal gap acceptance. The gap acceptance decision was also found to be affected by the type of conflicting vehicles. Practical applicationsThe insights from this study can be used for the safety and performance evaluation of uncontrolled midblock street crossings in developing countries.

Rear-end and Lane-changing Collisions Avoidance Theoretical Models of the Multi-lane Freeway in Disaster Weather

Rear-end collisions and lane-changing collisions are two most common types of automobile collision in multi-lane freeway traffic conditions, and they are dangerous in disaster weather. Car-following behavior, which describes the behavior of a vehicle while following the vehicle in front of it, has a significant impact on traffic safety. One of the most possible behavior leading to rear-end collisions is studied in this paper. In addition, car-following is one of the essential components of microscopic traffic models. In this paper, we estimate the emergency braking distance of passenger vehicles with respect to the following interval that should be kept as a “gap” between two consecutive vehicles moving in the same lane. Lane- changing behavior which describes a vehicle changes its lane into the other lane according to the accurate gap-acceptance decisions theory. Lane-changing related to collisions prone to occur in the process of lane-changing maneuvers. Lane- changing theoretical models and operational risk are studied in this paper to reveal the essence of traffic flow characteristics of the multi-lane freeway at the microscopic level. In summary, the paper presents a flexible framework for modeling car- following behavior and lane-changing behavior, which relaxes some constraints and assumptions of the most commonly used car following models and lane-changing models.

Empirical Study of Gap-Acceptance Behavior of Right-Turn-on-Red Drivers on Dual Right-Turn Lanes

Operational performance of right-turn-on-red (RTOR) at signalized intersections is a function of drivers’ gap-acceptance behavior. The objective of this study is to characterize gap-acceptance behavioral patterns of individual RTOR drivers turning from dual right-turn lanes. On the basis of direct field observation, binary logit models were developed, calibrated, and validated, and the attributes that have significant effects on gap-acceptance decisions were identified. The proposed model showed an improved capability of predicting gap-acceptance decisions as opposed to conventional, deterministic methods that may overrepresent aggressive drivers, whereas the deterministic method represents a reasonable trade-off between accuracy and ease of use for dual right-turn lanes. For RTOR drivers turning from a curb right-turn lane, the critical headway decreases on average as they are waiting for an acceptable gap. RTOR drivers turning from an inside right-turn lane did not demonstrate statistically significant evidence of increased impatience while waiting.