Gap Acceptance Behavior Research Articles

Estimation and modeling of pedestrian dilemma zone at uncontrolled intersections under mixed traffic conditions

Objectives Pedestrian gap acceptance (PGA) theory is the basic concept for pedestrian dilemma zone (PDZ) analysis and modeling and their gap acceptance behavior depends on dilemma behavior also. Uncontrolled intersections are one of the major locations where pedestrians have more dilemma and there is a possibility of an interaction between the pedestrian and vehicle due to the incorrect decision taken by the pedestrian when the vehicle lies within the limits of PDZ. Elimination and modeling of spatial boundaries of pedestrian dilemma stage improve the PGA. Methods The present study is intended to quantify and model the pedestrian dilemma zone (PDZ) boundaries at uncontrolled X-intersections under mixed traffic conditions. Video data were collected from four four-legged uncontrolled intersections in India. Pedestrian and vehicle information was extracted using DataFromSky software and manually from video. The Cumulative Gap Distribution (GCD) and Support Vector Machine (SVM) methods were used to estimate the boundaries of PDZ and developed a binary logistic regression model to estimate the PDZ boundary limits. Results The lower boundary limits of PDZ using GCD and SVM methods are 9.0 m and 6.0, respectively, and the upper boundary limits of PDZ using GCD and SVM methods are 16.5 and 18.5 m, respectively. The GCD method overestimated the lower limit and underestimated the upper limit compared with the SVM method. The binary logistic regression model results confirmed that pedestrian age, gender and crossing speed have a negative correlation and location of pedestrian crossing, vehicle type, and approaching speed have a positive correlation with the boundary limits of PDZ. Conclusions From the present study, it is concluded that SVM better estimated the PDZ boundary limits with the largest margin compared to the GCD method. It is concluded that the boundary limits shift away from the intersection in the case of female and old-age pedestrians compared to male and young-age pedestrians, respectively. The size benefit in the case of 2Ws is the reason for shifting the PDZ boundary limits close to the crosswalk. The lower approaching speeds of the vehicles at uncontrolled intersections are the reason for pedestrians accept the gap at shorter distances.

Unveiling gap acceptance behaviour during lane change with EDIV data: A deep dive into driving behaviour on expressway using a three level mixed effect linear regression approach

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Modelling Merging Behaviour of Drivers in Heterogeneous Traffic at Roundabouts

Roundabouts are among the widely used traffic control infrastructures, which function by defining priorities for various entry approaches and circulating traffic. In many developing countries, the priority rules are not implemented through pavement markings or road signs and drivers are also unaware of these rules. Thus, available gaps become the only criteria for merging and movement of vehicles through the roundabout. It has been observed that the gap acceptance behaviour of drivers also depends on the type of vehicle in heterogeneous traffic streams. The main objective of this study is to investigate the difference in gap acceptance behaviour of motorcycle riders and car drivers at roundabouts. The gaps accepted by car drivers and motorcycle riders under different traffic conditions are measured from the recorded video of the traffic stream at a selected roundabout using an unmanned aerial vehicle. The critical gap is estimated using Raff’s method and its relationship with circulatory volume and approach speed is analysed. The results show that the critical gap for motorcycles (2.46 seconds) is 23.3% smaller than the critical gap for cars (3.21 seconds). Motorcycles can move through smaller gaps in comparison with cars due to their size and ease of manoeuvring. Results also show that motorcycles tend to merge with greater approach speed as the available gap within the circulating vehicles reduces, whereas the cars are observed following an opposite trend.

Optimizing Intersection Design: Insights From Older Drivers’ Physiological Responses and Gap Acceptance Behavior at Signalized Left Turns

Optimizing Intersection Design: Insights From Older Drivers’ Physiological Responses and Gap Acceptance Behavior at Signalized Left Turns

Analysis of discretionary lane-changing behaviours of autonomous vehicles based on real-world data

This study aims to quantify the impact of discretionary lane-changing (DLC) on following vehicles (FVs) in the target lane using real-world dataset. The Waymo Open Dataset is used to identify the differences between autonomous vehicles (AVs) DLC and human-driven vehicles (HDVs) DLC maneuvers and compare their impacts on the driving volatility. Then, a block maxima (BM) model is applied to estimate crash risks. Finally, multivariate adaptive regression splines (MARS) is adopted to model gap acceptance behaviors of AV and HDV. Compared to HDV DLC, AV DLC leads to lower speed and yaw rate volatility and smaller acceleration rates of FVs. Further, the BM model reveals that the crash risk in AV DLC events is half of that in HDV DLC events. Additionally, MARS show that AV and HDV accept different lead gap. These findings highlight the benefits of mixing AVs in traffic and guide the improvement of AV controllers.

Evaluating the gap choice decisions of pedestrians in conflict situations in mass religious gatherings and controlled experimental setup – A pilot study

Previous studies on modelling the microscopic behaviour of pedestrians have focused on conflict resolution among pedestrians in pedestrian-pedestrian interactions. Many of these models propose alternate mechanisms to avoid conflicts by introducing repulsive forces between pedestrians or a set of predefined rules stating the precedence of movements to sidestep obstacles and other pedestrians. However, the possibility of formulating the decision-making mechanism pedestrians use to overcome conflicts as a gap-seeking behaviour has not been explored. In this study, resolving conflicts between opposing pedestrians is modelled as gap choice decisions made by individuals. Pedestrians looking for gaps or spaces in a crowd to facilitate their movement form the basis for such an analysis. The study compares pedestrians' gap acceptance behaviour across two scenarios: pedestrian movement in a field setup (Kumbh Mela) and a controlled experiment. Multiple gap choice decisions of individuals are modelled to understand the effect of individual-level heterogeneity on gap choices. Apart from the gap duration, spacing, position of gap, linear density, age, and presence of luggage significantly influenced the gap choices. Model validation is done using appropriate methods for both field and experimental data. The bootstrap method of internal validation and holdout validation is used to assess the performance of the estimated model on field data and experimental data, respectively. It is seen that the models have reasonable predictive and discriminative abilities. The analysis results also indicate that pedestrians tend to force gaps to facilitate movement in their natural state. Consequently, controlled experiments might have limitations in reproducing or motivating the participants to behave like a crowd.

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.

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.

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.

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.

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

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

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.

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.

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.

How do the recognizability and driving styles of automated vehicles affect human drivers’ gap acceptance at T- Intersections?

Future traffic will be composed of both human-driven vehicles (HDVs) and automated vehicles (AVs). To accurately predict the performance of mixed traffic, an important aspect is describing HDV behavior when interacting with AVs. A few exploratory studies show that HDVs change their behavior when interacting with AVs, being influenced by factors such as recognizability and driving style of AVs. Unsignalized priority intersections can significantly affect traffic flow efficiency and safety of the road network. To understand HDV behavior in mixed traffic at unsignalized priority T-intersections, a driving simulator experiment was set up in which 95 drivers took part in it. The route in the driving simulator included three T-intersections where the drivers had to give priority to traffic on the major road. The participants drove different scenarios which varied in whether the AVs were recognizable or not, and in their driving style (Aggressive or Defensive). The results showed that in mixed traffic having recognizable aggressive AVs, drivers accepted significantly larger gaps (and had larger critical gaps) when merging in front of AVs as compared to mixed traffic having either recognizable defensive AVs or recognizable mixed AVs (composed of both aggressive and defensive). This was not the case when merging in front of an HDV in the same scenarios. Drivers had significantly smaller critical gaps when driving in traffic having non-recognizable aggressive AVs compared to non-recognizable defensive AVs. The findings suggest that human drivers change their gap acceptance behavior in mixed traffic depending on the combined effect of recognizability and driving style of AVs, including accepting shorter gaps in front of non-recognizable aggressive AVs and changing their original driving behavior. This could have implications for traffic efficiency and safety at such priority intersections. Decision makers must carefully consider such behavioral adaptations before implementing any policy changes related to AVs and the infrastructure.

Evaluation of Factors Influencing Roundabout Performance in Atlanta

This paper evaluates the potential impacts of various geometric and operational parameters on gap-acceptance behavior for approaches to roundabouts in the metropolitan area of Atlanta, Georgia. Twelve roundabouts were selected to provide a range of different conditions such as number of legs, number of circulating lanes, conflicting volumes, presence of pedestrian crossings, and so forth. Observational data were collected at these roundabouts using a remotely operated drone equipped with a stabilized high-resolution (4K) video camera operating at a sufficient altitude (just less than 400 ft/120 m) to encompass the complete facility within a single frame. The resulting videos were processed using commercial machine vision analysis supplemented by additional computer-assisted analysis to determine vehicle trajectories, spacings, and potential conflicts on a frame-by-frame (0.033 s) basis. These data, in turn, were used to establish gap-acceptance behavior of each roundabout approach and, through the use of logistic regression on the data, to establish observed critical headways (defined as the logistic inflection point [ t50]). The variability of observed headways was compared against known parameters of both the roundabout and of the specific approach to develop a descriptive/predictive model as to how critical headways and gap-acceptance behavior were affected by variations in these parameters. Several factors were observed to have significant impacts on critical headways, including geometric (size category of the roundabout, number of legs, and visual angle to the upstream approach), environmental (presence of a state route on the upstream approach, presence of additional conflicting lanes, presence of a pedestrian crosswalk on the approach), and operational (approach speeds) factors.

Traffic volume and waiting time influence on gap acceptance of selected change direction U-turn opening

The vehicles moving on change direction U-turn opening have to spend some time while the acceptable headway provided on conflicting main traffic flow. The present paper investigated the effect of waiting time and traffic volume increasing on gap acceptance behavior, critical gap limit, and U-turn capacity. The data were collected at selected change direction U-turn in Najaf city (center city of the Najaf governorate located approximately 160 km south of Baghdad, the capital of Iraq) within Najaf city highways network. The study concentrated on the passenger cars driver movement at change direction U-turn openings. The analysis approach depended on evaluating these parameters and their degree of influence on U-turn capacity is statistical analysis. statistical analysis established that, when waiting time fall in a range between 21- 30 sec, driver enforced to accept gap size less than that fall in the range of 11 to 20 sec at a confidence interval of 95%. On the other hand, there is a slightly different in mean gap acceptance between an interval of (1-10) and (11-20) sec) at the same confidence interval. Results showed the studied U- turn change direction median critical gap equal to 3.75 sec. and follow-up time was 1.1 seconds. According to Siegloch’s formula the maximum capacity of 3273pcu/ hr. At level of confidence of 95%, the mean value of the highest wait time group interval is lower than the critical gap. Therefore, the studied change direction U-turn might be hazard location and it is important to control and manage at the median opening.

Analysing pedestrian–vehicle conflict behaviours at urban pedestrian crossings

Pedestrian safety has become a serious problem with the rapid growth of motorised vehicles in transportation systems in developing countries. Pedestrians often respond differently to changes in surrounding and traffic conditions. A study was undertaken to investigate pedestrians’ gap acceptance and the parameters affecting their risk-taking behaviours based on time to collision (TTC) and post-encroachment time (PET) indexes. Three signallised intersections and two mid-block crossings were selected in Qazvin, Iran. A total of 752 pedestrians were examined by video recording and field observation, and pedestrians’ gap acceptance behaviour was estimated by using a binary logit model. The results showed that the average TTC and PET were 4.27 and 1.44 s, respectively. In addition, the presence of children alongside the older pedestrians led to a less risk-taking crossing. Additionally, pedestrian risk-taking was reduced by increasing both time indexes. Rainy weather conditions also reduced pedestrians’ risk-taking behaviour. Elasticity analysis indicated that parameters such as pedestrians’ conflict with vehicles at the first or second half of the crossings, walking with a child, speed of the approaching vehicle, the crossing type and running while crossing were the most important factors in pedestrian risk-taking.

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.

Modelling the pedestrian dilemma zone at uncontrolled midblock sections

Introduction: Pedestrians at high-speed midblock crossings with the intention to cross the road usually face safety risks due to difficulty in judging the available gaps. The risk to pedestrians is high in developing nations like India since priorities are not respected by road users. Moreover, the non-yielding vehicular traffic puts pedestrians at further risk. While crossing the road, pedestrians are clear about rejecting small gaps and accepting the large gaps, however, they experience a dilemma between the small and large gaps. Method: This study attempts to model the dilemma zone for pedestrians intending to cross the high-speed roads (posted speed limit of 60 km/h). The field data were collected using high-definition video cameras at two uncontrolled midblock crossings, each in the cities of Mumbai and Kolhapur, located in the southwestern part of India. The variations in the spatial gap acceptance behavior were analyzed for 1,107 pedestrian observations using binary logit models. Results: The findings revealed that the length and the distribution of the dilemma zone were significantly affected by the speed of the approaching vehicle and the distance from it. Moreover, the influence of vehicle type (truck, car, or two-wheeler), pedestrian type (walking alone or in a group), crossing speed, and waiting time also influenced pedestrians’ gap acceptance behavior. Interestingly, pedestrians’ gender did not play a significant role in their road crossing decisions. Conclusions: Overall, the study identified the dilemma zone boundaries that will help pedestrians to judge the safe gaps while crossing, and in turn, reduce the probability of pedestrian-vehicle crashes. Practical Application: The proposed dilemma zone intends to protect the pedestrians by assisting in making precise crossing decisions at high-speed midblock crossings.