Gap Acceptance Research Articles

Implementing Artificial Neural Network-Based Gap Acceptance Models in the Simulation Model of a Traffic Circle in SUMO

The impact of various operational and design alternatives at roundabouts and traffic circles can be evaluated using microscopic simulation tools. Most microscopic simulation software utilizes default underlying models for this purpose, which may not be generalized to specific facilities. Since the effectiveness of traffic operations at traffic circles and roundabouts is highly affected by the gap rejection–acceptance behavior of drivers, it is essential to accurately model drivers’ gap acceptance behavior using location-specific data. The objective of this paper was to evaluate the feasibility of implementing an artificial neural network (ANN)-based gap acceptance model in SUMO, using its application programming interface. A traffic circle in New Jersey was chosen as a case study. Separate ANN models for one stop-controlled and two yield-controlled intersections were trained based on the collected ground truth data. The output of the ANN-based model was then compared with that of the SUMO model, which was calibrated by modifying the default gap acceptance parameters to match the field data. Based on the results of the analyses it was concluded that the advantage of the ANN-based model lies not only in the accuracy of the selected output variables in comparison to the observed field values, but also in the realistic vehicle crossings at the uncontrolled intersections in the simulation model.

Ontologies for Supporting Traffic Behaviour, Critical Gap, and Conflict Models at Unsignalized Intersection Routes

Background: The challenge arises from management of big data in transportation can be identified by ontological approach. Road accidents are regarded as a significant cause of both harm to humans and financial damage in many countries. In Malaysia, there were 567,516 accidents in 2020 alone. This translates into a daily average of 13 people killed due to traffic accidents in the country. Here, our work focuses on gap patterns, critical gap analysis, gap acceptance of right-turning motorists (RTMs) and serious conflict model on Malaysia Rural Roadways at three-leg unsignalized intersections. Methods: In early stage, traffic volume, motorist turning manoeuvre and speed study are implemented to identify the traffic behaviour at selected intersection. Three unsignalized intersection (UI) was involved namely (UI2), (UI 9), and (UI10). In the development of logistic regression models, five different datasets were used in this study: right-turning motorist (RTM) at unsignalized intersection UI2 (259 dataset), right-turning motorist at UI 9 (239 dataset), right turning motorist at UI 10 (314 dataset), right turning motorist combined model (812 dataset) and serious conflict lane change (351 dataset). Determination of critical gap was carried out at each unsignalized intersection 2, 9 and 10. Meanwhile gap-pattern analysis at each intersection used visualization spatial plot. In addition, this work investigated logistic regression method, artificial neuron network and structural equation modelling. Results: Gap pattern three was discovered to be a vulnerable gap pattern. Furthermore, this research reveals that the attributes of the gap three pattern, motorcycles rider, speed limit exceeding 50 kph and RTMs where motorcycles stop near passenger cars in minor roads encourage serious conflict. Moreover, this study proposes novelty tool for identifying hazardous unsignalized intersection by using visualization spatial plot between approach speed and gap.

Analysis of student pedestrian gap acceptance behaviour in suburban areas

The safety of student pedestrians on suburban roads is an issue that is mainly overlooked. Numerous studies have been carried out on pedestrian crossing safety in developing countries. However, a study to identify factors that affect the gap acceptance behaviour of student pedestrians on suburban roads is still lacking. Consequently, it is crucial to understand how factors influence pedestrians’ decisions. This motivated an investigation into student pedestrian crossing behaviour at a crosswalk near a university campus located on suburban roads in Babol city, Iran. A video recording of the case study was conducted to provide a database for the analysis. Following data extraction, a logistic regression method was used to examine gap acceptance behaviour based on influential variables. Results indicated that waiting for time, the mean speed of the flow, headway between vehicles, available gap size and the type and speed of the approaching vehicle were significant. In addition, the gender and age of pedestrians did not significantly affect gap acceptance behaviour. Generally, traffic-related variables were observed as significantly influencing gap selection behaviour.

Evaluation of Dynamic Flashing Yellow Arrow Decision Support System in the Field Using Before and After Real-Time Data

A permissive left turn is one of the most dangerous movements a driver can make because the driver travels across opposing traffic lanes. In recent years, flashing yellow arrow (FYA) signals have been proven to help drivers make safer left turns. The four-section signal display with FYA permissive left-turn indication creates an opportunity to enhance the left-turn phase with a variable mode that can change on demand. A challenge to using this system is knowing the best times of day to apply the FYA indication. The authors developed an exclusive hardware platform: first, to provide a generic device compatible with the different controller types used by different jurisdictions and, second, to automate selection of the FYA left-turn modes based on available gaps in the opposing traffic at intersections acquired in real time from existing sensors in the field. Phase IV provided conclusive field testing and evaluation of the decision support system (DSS), by switching between red and FYA modes in a rational manner consistent with driver expectations and left-turning gap acceptance thresholds. It was also concluded that coordinated signals with long cycle lengths, 3 min and longer, help provide adequate gaps even in heavy traffic patterns since most of the vehicle arrivals are in platoons and at the beginning of the cycle. The analysis showed that the utilization factor for the DSS recommendations for all the intersections ranged between 65% and 75% during peak conditions on weekdays and between 90% and 95% during off-peak conditions and weekends. The total delay in the before study for all the intersections amounted to 737 vehicle-hours and in the after-study was 440 vehicle-hours of delay. An overall reduction of about 40% in delay which confirms the operational benefit of the dynamic FYA DSS. The developed platform is applicable at any four-section head configuration to maximize safety and efficiency of the intersections.

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.

Comparative Evaluation of Roundabout Capacities Methods for Single-lane and Multi-lane Roundabout

A roundabout is a highway engineering concept meant to calm traffic, increase safety, reduce stop-and-go travel, reduce accidents and congestion, and decrease traffic delays. It is circular and facilitates one-way traffic flow around a central point. The first part of this study evaluated the principles and methods used to compare the capacity methods of roundabouts with different traffic conditions and geometric configurations. These methods include gap acceptance, empirical, and simulation software methods. Previous studies mentioned in this research used various methods and other new models developed by several researchers. However, this paper's main aim is to compare different roundabout capacity models for acceptable capacity predictions for single-lane and multi-lane roundabouts. In conclusion, previous studies such as RODEL, SIDRA, Swiss, HCM6, and IRC overestimate capacity, while the GHCM method underestimates it. Each VISSIM and SIDRA predicted higher capacity than HCM2010, Paramics, and Simtrafic capacity methods. Generally, the precise prediction of capacity value depends on the circulating flow, exiting traffic flow, driver behavior, and geometric variations. Also, a comparison between seventeen methods was made using virtual data. For a single-lane roundabout, Girabase and Swiss models estimated higher capacity values when compared with other models, while HCM 2010 estimated lower capacity. The Shamueli’s model provides an estimate of capacity about the same as the lower bound of the HCM 2000 model. Brilon’s model estimated lower capacity values for a multi-lane roundabout compared with other methods. At low circulating flow, the Girabase model estimated a higher capacity value, while with the increasing circulating flow, the FHWA2000 estimated a higher capacity than the Girabase model. Also, there is a bit of a difference between SIDRA 5 and 8.0. There is a small interval between models that are implemented in a single-lane roundabout. Therefore, the models are better at predicting the capacity of a single lane than they are at predicting the capacity of multiple lanes.

Impact of Fundamental Elements of Connected and Autonomous Technology on Right-Turn Gap Acceptance Behavior at an Uncontrolled Intersection

Different original equipment manufacturers (OEMs) provide different connected and autonomous vehicle (CAV) solutions. However, previous studies that estimated the impact of CAV on driving behaviors assumed CAVs are identical, with an integrated form of technologies. In this study, a simulation was conducted to investigate how different fundamental elements of CAV technology (i.e., communication range, number of vehicular interactions, and car-following distance) affect right-turn gap acceptance behavior at unsignalized intersections. The results of the statistical analysis, critical gaps comparison, platooning analysis, and logistic regression revealed that (1) all elements increase the lag acceptance rate compared with all human-driven vehicles; (2) a longer communication range and more vehicular interactions make CAVs turn conservatively from a minor road, causing the critical gap to increase; (3) a shorter car-following distance causes CAVs to generate longer platoons and shorter gaps on a major road, which decreases acceptance probability; and (4) CAVs with a shorter car-following distance on a minor road are more aggressive in accepting gap/lag, causing the critical gap to decrease and acceptance probability to increase. The study conducted an impact analysis of CAV technologies from a novel perspective, and the results can be used as a technical report of CAV’s performance to help build transportation policies.

Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework aiming to facilitate the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.

ORGANIZATION OF TRAFFIC AT UNCONTROLLED INTERSECTIONS

This paper presents a study of drivers' gap acceptance behavior at an uncontrolled intersection. A 4-leg uncontrolled intersection located in a semi-urban area of Ahmedabad, Gujarat was selected to study the gap acceptance behavior of drivers. Video recording technique was adopted for data collection and analysis. Various available methods are used to calculate the critical gap for minor road passing and right turn movements. Gaps are analyzed for three different vehicle types. A comparison is made between the various existing methods of critical gap detection and their critical analysis is presented. The result shows that except for the behavior elimination approach, all other methods cannot provide the real scenario of the critical gap under mixed traffic conditions because all these methods are developed for homogeneous traffic conditions.

Pedestrian Gap Acceptance Behavioral Modelling at Midblock and Uncontrolled Intersections

Pedestrian safety is one of the major concerns and pedestrian gap acceptance (PGA) behaviour depends on various characteristics of pedestrians, vehicles, geometry and environment. This study is intended to develop PGA models at various pedestrian crossing locations (mid-blocks and uncontrolled intersections). Video graphic data was collected from four different pedestrian crossing locations and extracted the required data from videos manually. The multiple linear regression (MLR) technique was used to model the pedestrian gap acceptance behaviour by the vehicular gap size accepted by the pedestrians. MLR models reveal that the frequency of attempts significantly affects the PGA behaviour only in the case of mid-blocks whereas the frequency of disturbances significantly affects only in the case of intersections. The statistical results showed that the pedestrian road crossing behaviour depends on the type of crossing location also. The results can be used to analyse the pedestrian safety levels at various crossing locations.

Development of Empirical Models to Predict Gap Acceptance Parameter Based on the Geometrical and Operational Parameters of Different Roundabouts

This paper develops a mathematical formula that accounts the influence of roundabout’s design and performance parameters to predict the gap acceptance parameter “Critical gap”. Thirteen roundabouts in Hungary having different geometric and operational parameters were selected. The geometrical and operational data of each roundabout’s leg was collected. Raff's method was used to estimate the critical gap for each roundabout leg. Firstly, the collinearity analysis was carried out to identify independent parameters to avoid any possible negative impact on the developed predictive models. Nine out of ten parameters passed the collinearity test. These nine parameters are the main parameters used in the model development. Three models were developed. The first model (M1) is based on Multivariate Adaptive Regression Spline (MARS) algorithm. The second model (M2) is based on Pearson correlation. The last model (M3) was based on Spearman correlation. Linear regression models were constructed using the retained parameters of the M2 and M3 models. Subsequently a comparison of the three developed models is done based on R2 and RMSE values. Based on the results obtained from the comparison, the MARS model (M1) is the best predictive model of the critical gap. According to the results of the MARS model, the most important parameters for predicting critical gap value are circulating traffic flow and the distance between neighboring legs.

Red LED Strip Signalling Pedestrian Presence at Uncontrolled Mid-block Crosswalks

A relevant number of collisions between vehicles and pedestrians occur at night because of the reduced capacity of a driver to perceive and react to the presence of pedestrians at crosswalks. To reduce this risk of collision, new smart road technologies have been introduced. This study aims to evaluate the effectiveness of a red LED strip that warns the driver of the presence of pedestrians at unsignalized mid-block crosswalks at night-time. The technology consists of LED strips installed near the crosswalk on the side of oncoming vehicles. The LED is activated when a sensor detects a pedestrian approaching the crosswalk. In this driving simulation experiment, three different urban mid-block crosswalks were evaluated: (i) without an LED strip (baseline), (ii) with a fixed LED strip, and (iii) with a flashing LED strip. Each scenario was tested for three different levels of pedestrian time gap acceptance (PTGA) (i.e., 4, 6, 8 s) and two road sections (i.e., 1 lane vs. 2 lanes). Thirty-six participants took part in a within-subjects’ design. During the experiment, the minimum time to collision (mTTC), the post-encroachment time (PET), maximum vehicle speed, and reaction distance were monitored to quantitatively compare the different levels of vehicle-pedestrian interaction. Compared to the baseline, the LED strip resulted in a safer driver-pedestrian interaction, with an average increase in mTTC of 1.13 s, PET of 0.66 s, and a longer average reaction distance of 7.9 m. However, a slight (albeit not statistically significant) increase in speed was observed following installation of the LED strip. Furthermore, no significant differences were observed between fixed and flashing LED strips. Overall, these results confirm the LED strips effectiveness in alerting the driver to the presence of a pedestrian, thus increasing the safety of their interaction. Further studies should confirm these findings in a more ecological way, e.g., evaluating the safety impact of this technology under different weather and distracted driving conditions.

Pedestrians’ perceived vulnerability and observed behaviours relating to crossing and passing interactions with autonomous vehicles

Researchers have predicted that “vulnerable road users” (VRUs) such as pedestrians will feel less vulnerable and thus take more risks around autonomous vehicles (AVs) than around human-operated vehicles (HOVs). However, data on the behaviours pedestrians are likely to display during passing as well as crossing interactions with AVs – particularly from naturalistic studies – are currently lacking. Such data could help inform AV system designers and authorities, as well as researchers. So, a novel study was conducted in London, UK. Perceived vulnerability was gauged via a survey on hypothetical pedestrian-vehicle interactions (N = 267). Behaviours were observed during real crossing and passing interactions with AV shuttle pods in a shared space (N = 330). While pedestrians were the main focus, joggers and cyclists were also frequenting the observation site and were included in the analysis of passing interactions. The survey results showed that pedestrians were not perceived to be less vulnerable around AVs. Diminishing initial boldness in the crossing interactions, and high yielding in the passing interactions, supported this, demonstrating that VRUs were not taking undue risks; rather, they appeared to be experiencing some uncertainty and discomfort. Further results showed other VRU behaviours (gap acceptance, inattention, hesitation, changes in speed, explicit communication, a side preference) may be relevant in AV interactions, but not necessarily to the same degree as when around HOVs or not in line with UK road rules. Positive conclusions were drawn for AV programming, and for safety, at least in the short term, but concerns regarding mobility need addressing.

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.

Reckoning Level of Risk and Severity (LORS) Based on the Gap Acceptance at a Three-Legged Uncontrolled Intersection of a Rural Highway

Traffic safety assessment is an essential part of traffic planning and engineering. At uncontrolled intersections, the severity of crashes has increased manifolds. This is due to the fact of not following the priority rule during the right-turning movements. In conventional methods, past accident records were used to analyze traffic safety. However, the drawbacks of the traditional method to estimate the severity require a lot of accident data for analysis to draw any conclusion and suggest safety measures. The accuracy of the record of accidents and details is highly uncertain. Therefore, surrogate safety measures are being used to analyze the safety. In the present paper, the gap acceptance time (GT) is used to examine the risk and the speed and gap values define the level of risk and severity (LORS). Moreover, using the value of the critical gap, the probability of risk is estimated. Further, the level of risk and severity is computed based on the gap and speed data of vehicles at an uncontrolled intersection. Hence, with the speed and gap data, the level of risk and severity is classified as low risk, high risk and low severe, and high severe. The methodology implemented is validated by using the actual accident data.

Pedestrians’ Road Crossing Behavior at Unprotected Mid-Blocks among Adolescents and Adults

Traffic crashes involving pedestrians often occur when the gap acceptance selected is below the minimum critical gap. Social cues and situational awareness play a key role in pedestrians’ choices when crossing roads at unprotected mid-blocks. This paper investigates the effects of conformist behavior on pedestrians’ cautiousness relative to their age group. In particular, we examine differences between adolescents (aged 13–18 years) and adults (aged 18 years or over). Video cameras were set at three urban locations in Auckland City, New Zealand. A total of 1,330 pedestrians were divided into four age groups, with adolescence used as the reference group. Risk ratios and regression analysis were used to measure the association between variables. The key finding from the observed behavior shows that group dynamics can reduce individuals’ attention to the road crossing regardless of age and gender. Although both adolescents and adults display self-regulation with various cautious behaviors, the presence of others affects their selection of the safety margin (time left to spare after crossing). Differences in the safety margins for groups of adolescents and adults largely involved the relationship amongst the group members rather than the number of people. Such findings present evidence that injury prevention strategies need to reflect the instincts and behavioral habits of different pedestrians. Encouraging a safety culture targeted specifically at various age groups that aligns with road design facilities and recognizes these behaviors will assist in the reduction of crashes involving pedestrians.

Simulator-Based Framework to Incorporate Driving Heterogeneity via a Biobehavioral Extension to the Intelligent Driver Model

Mathematical models of car-following, lane changing, and gap acceptance are extensively used to describe behavioral variability when driving. Car-following, in relation to the intelligent driver model (IDM), was the primary component of this research. This research was targeted toward developing a framework to incorporate driver behavioral variability using methodologies adapted from the cognitive and physiological sciences. The main goal was to fuse driving variables such as preferred gap, speed, jerk, and acceleration, together with continuous biobehavioral variables of engagement level, mental workload, situation awareness, and other static driver properties (i.e., age, experience, and driving history), to introduce biobehavioral heterogeneity into the IDM. Incorporating non-conventional tasks during car-following, such as distracted driving, was also a key component of this research. Additionally, assessing the effectiveness of incorporating group-based biobehavioral and driving performance traits rather than individual-level traits into the IDM was also explored. Ninety drivers were recruited to validate the framework using a fixed-base simulator. The scenarios were designed to capture the performance and cognitive parameters when subject to varying task complexities. A biobehavioral extension to the IDM was developed by grouping individual driving performance and behavioral traits. The extended model was successfully validated and found to introduce driving heterogeneity while enhancing the IDM’s car-following modeling accuracy for the sample population. The established methods also serve as a key step toward the inclusion of individual/group-level traits that not only consider driving performance but also harvest cognitive and biological processes that directly affect car-following.

Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions

Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed homogeneous traffic conditions, and applying them directly to heterogeneous traffic conditions may produce biased results. Moreover, driver behavior concerning critical gap acceptance or rejection in traffic also varies from one location to another. The current research focused on the estimation of critical gaps considering different vehicle types (cars, and two- and three-wheelers) under heterogenous traffic conditions at uncontrolled crossings in the city of Peshawar, Pakistan. A four-legged uncontrolled intersection in the study area was used to investigate drivers’ gap acceptance behavior. The gaps were investigated for various vehicle types: two-wheelers, three-wheelers, and cars. For data collection, a video recording method was used, and Avidemux video editing software was used for data investigation. The study investigated the applicability of the maximum likelihood (MLM) method to analyzing a vehicle’s critical gap. MLM estimation results indicate that the essential critical gap values for car drivers are in the range from 7.45 to 4.6 s; for two-wheelers, the critical gap was in the range from 6.78 to 4.7 s; and for three-wheelers, the values were in the range from 6.3 to 4.9 s. At an uncontrolled intersection, the proposed method’s results can assist in distinguishing between different road user groups. This study’s findings are intended to be useful to both researchers and practitioners, particularly in developing countries with similar traffic patterns and vehicle adherence patterns at unsignalized intersections.

Drivers’ gap acceptance during parking maneuvers as a basis for initiating driving actions in automated vehicles

As a social system, traffic requires the coordination between different involved road users, particularly in shared spaces (e.g., parking areas) with fewer statutory regulations and hence more ambiguous encounters. By anticipating the development of driving scenes and adapting own driving actions, drivers maintain individually varying safety margins, such as accepted time gaps, to surrounding traffic participants. However, drivers’ gap acceptance (GA) was shown to be influenced by situational factors and driver characteristics. In order to apply expectable interactions in mixed traffic, involving manual and automated vehicles (AVs), intuitive gaps in traffic flow need to be selected by AVs when initiating parking actions. Derived time gaps of manual drivers could provide a basic orientation for intuitive driving functions in AVs. Thus, the current experimental study assessed manual drivers’ GA in a parking area scenario. Real-world video material was used to investigate the effects of interaction partners, their encountering speeds and drivers’ age and sensation seeking on GA. In the study, 42 participants (including two age groups: 18–30 years vs ≥ 45 years) indicated their GA when initiating a left-turn parking maneuver in front of approaching vehicles. The study examined three interaction partners (motorcycle, passenger car, truck), each approaching at six different speed levels (10–35 km/h). The results revealed that selected time gaps increased as vehicle size increased, with the largest GA for the truck and the smallest gaps for the motorcycle. Moreover, time gaps decreased at higher approaching speeds of the interaction partners. In addition, older participants selected more conservative gaps and participants with higher sensation seeking scores accepted lower time gaps to initiate a parking maneuver. The results of the current study revealed that there is no one single time gap that is always considered as appropriate to initiate parking actions. For intuitive interaction capabilities in AVs and to support smooth encounters in mixed traffic, the speed of the approaching interaction partners should be considered as an essential situational factor when initiating parking maneuvers. To consider individual driving style preferences (e.g., due to driver age) and support user acceptance, selectable driving style profiles should be available in AVs as a form of customization.

Categorization of gaps at mid-block median openings in heterogeneous traffic: adjudging the applicability of support vector machine and occupancy time methods

ABSTRACT Critical gap has been estimated to investigate the gap acceptance characteristics of U-turning vehicles at mid-block median opening (MBMO) because proper modeling of gap acceptance behavior coupled with the integration of an intelligent transport system (ITS) can offer an efficient and safe driving environment at MBMO. In this regard, support vector machine (SVM) and occupancy time (OT), have been used to estimate the spatial and temporal critical gap of U-turning vehicles. The outcome of this study suggests that the SVM method can perform with better veracity than the OT method in accessing the gap acceptance behavior of U-turning vehicles. The result of the study also indicates SVM method can be used for the prediction of gap acceptance and rejection of U-turning vehicles and further can be integrated with a cognitive architecture for safe and efficient traffic flow and management at MBMO in an ITS environment.