Dilemma Zone 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.

What would affect drivers’ stop-and-go decisions at yellow dilemma zones? A driving simulator study in Hong Kong

Yellow dilemma, at which a driver can neither stop nor go safely after the onset of yellow signals, is one of the major crash contributory factors at the signal junctions. Studies have visited the yellow dilemma problem using observation surveys. Factors including road environment, traffic conditions, and driver characteristics that affect the driver behaviours are revealed. However, it is rare that the joint effects of situational and attitudinal factors on the driver behaviours at the yellow dilemma zone are considered. In this study, drivers’ propensity to stop after the onset of yellow signals is examined using the driving simulator approach. For instances, the association between driver propensity, socio-demographics, safety perception, traffic signals, and traffic and weather conditions are measured using a binary logit model. Additionally, variations in the effect of influencing factors on driver behaviours are accommodated by adding the interaction terms for driver characteristics, traffic flow characteristics, traffic signals, and weather conditions. Results indicate that weather conditions, traffic volume, position of yellow dilemma in the sequence, driver age and safety perception significantly affect the drivers’ propensity to stop after the onset of yellow signals. Furthermore, there are remarkable interactions for the effects of driver gender and location of yellow dilemma.

Empirical analysis of dilemma zone using high-resolution event data

The dilemma zone (DZ) has been physically characterized based on two divergent definitions: Type I and Type II. However, treating DZ differently based on these definitions may lead to inaccurate results of DZ boundary and subsequent safety analyses. Moreover, an integrated empirical assessment of Type I and Type II definitions for consistency in boundary quantification is not yet well-addressed. To this end, we empirically analyzed the two DZ definitions by comparing their boundary dynamics with approach velocity and time of day. First, we proposed a rule-based matching methodology with 92% accuracy to match actuation events between the advance and stop-bar detectors. This methodology was then applied to process two months of high-resolution event data from an intersection approach, yielding 28,700 vehicle arrivals on yellow. Results showed that 13.2% of approaching vehicles fall into an indecision zone or make Type I-contrary stop/run decisions at the yellow onset. The Type I and Type II DZ boundaries were temporally segregated and did not significantly overlap. Our novel findings indicate a lack of consistency in quantifying DZ and emphasize a need for data-driven quantification of the DZ boundary and its dynamics.

Exploring the effects of traffic light countdown displays on the driving behaviours of drivers with different personality dispositions: A VR-simulated study

The dilemma zone, where drivers must decide whether to stop or proceed through an intersection as a traffic signal turns yellow, poses a substantial risk for traffic accidents. One factor affecting the dilemma zone is the inconsistency of green light countdown displays across intersections. This VR simulation study aims to investigate how different countdown display settings impact driver behaviour in the dilemma zone. Using a simulated traffic intersection developed using Unity3D, 31 participants completed driving tasks under 5 different green light countdown displays. Vehicle speeds were collected objectively in the experiments. Questionnaires were employed to assess drivers' hesitation levels and personality dispositions. The results from the statistical analysis reveal that drivers with diverse personality dispositions exhibited consistent hesitation levels across countdown displays. While longer countdown displays tended to reduce drivers' hesitation, the countdowns set between 5-10 seconds resulted in lower vehicle speeds through the intersection compared to other shorter or longer settings. This indicates that the optimal green light countdown display for mitigating the dilemma zone is 5-10 seconds. Findings of the study provide useful insights into the relevant research field. Transportation departments may apply these findings to improve driver safety, refine policies, and standardise countdown timers across intersections.

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.

Characterizing the Dilemma Zone at Intersections by Introducing the Break Point

Characterizing the Dilemma Zone at Intersections by Introducing the Break Point

Impact of countdown timer on drivers’ anticipation at the onset of yellow at signalized intersections

ABSTRACT Countdown timers at traffic signals display the time remaining for a phase to terminate, influencing driver behaviour. Studies on their effects at the onset of yellow have generally focused on drivers’ stop/go decisions and dilemma zone, without explicitly addressing how they help drivers anticipate the signal change and make ‘informed decisions’. Therefore, this study first analysed the impact of countdown timer on drivers’ anticipation by comparing brake initiation, deceleration, and speed in the absence and presence of countdown timer. Next, driver behaviour was studied using ‘Accelerated Failure Time’ models. A driving simulator experiment was designed for data collection, with demographic and additional driving behaviour data gathered through a questionnaire. When countdown timer was present, drivers anticipated the onset of yellow, resulting in significant variations in driver behaviour. These variations, in turn, influenced the duration of braking and the time taken to cross the stop line from the onset of yellow.

Modelling yielding dilemma zone for drivers at non-signalised crosswalks

In developing countries like China, non-signalised crosswalks represent a considerable risk to pedestrian safety. This study introduces a “yielding dilemma zone” model to tackle the decision-making challenges encountered by drivers at these crosswalks. Field data collected via drones at three non-signalised crosswalks in Xi'an (China) facilitated the analysis of 1687 instances of driver yielding behaviour. The study employed Support Vector Machine (SVM), Artificial Neural Network (ANN), and Binary Logistic Regression (BLR) models to establish the boundaries of the dilemma zone. Additionally, it utilized surrogate safety metrics, such as the deceleration rate required to stop (DRS) and time to crossing (TC), to compare risks between dilemma and non-dilemma zones. Each modelling approach provided insightful findings. The SVM and ANN models identified the speed-distance dilemma zones, while the BLR model determined the multifactorial dilemma zones by considering characteristics such as pedestrian, vehicle, and traffic condition. The findings underscore the significance of the “yielding dilemma zone” for understanding driver-pedestrian interactions at non-signalised crosswalks. The innovative multi-model approach uncovered that pedestrian speed, vehicle type, vehicle approach speed, adjacent vehicle number, driving directions, and pedestrian crossing stage significantly influence this interaction. indicated a higher probability of collisions within dilemma zones compared to non-dilemma zones across all models. The study suggests that the SVM model could be instrumental in creating passive physical warning signs, while the ANN and BLR models could significantly contribute to real-time pedestrian and driver assistance systems. The “yielding dilemma zone” model, with its comprehensive approach, presents a promising avenue for enhancing pedestrian safety, intelligent transportation systems, and traffic management at non-signalised crosswalks.

Evaluating the effectiveness of engineering countermeasures at yellow dilemma zone: a driving simulator-based study

ABSTRACT The indecisiveness of drivers at the yellow onset creates a dilemma zone (DZ) upstream of the signalized crossings. The yellow light dilemma negatively influences intersection safety, often leading to rear-end and right-angle collisions. This study explores the impact of upstream road marking and active in-vehicle warnings designed based on DZ boundaries. The prototype concepts of in-vehicle warning systems and upstream road marking were designed and implemented in the driving simulator environment. The driver responses from the experimental setup were captured to evaluate the effectiveness of the designed DZ countermeasures. Study results indicate that implemented DZ countermeasures (i.e. DZ road marking, in-vehicle display warning, and in-vehicle audio warning) significantly decreased the drivers’ crossing probability, resulting in a shorter dilemma zone. However, the proposed countermeasures did not show any improvement in the drivers’ red-light running (RLR) behavior. The driving simulator study showed promising results in reducing aggressive crossing responses at the yellow onset.

Comparison of dilemma zone length between car and motorcycle using visual observation data

There is a zone known as the dilemma zone located on each arm of a signalized intersection. In the dilemma zone, there is a decision to go through the intersection at the risk or a decision to stop suddenly. The longer the dilemma zone, the higher the risk that the driver will pass through the intersection. Therefore, the purpose of this study is to present a comparison of visual observation data between car and motorcycle regarding the length of dilemma zone for 3 seconds yellow interval at the 3-arm signalized intersection. The length of dilemma zone for car is longer than motorcycle in peak-hour (by about 100%) and this can be found that the acceleration/ deceleration time for cars are slower than motorcycles. Other result shows that the length of dilemma zone for cars has significant different in off-peak hours and peak hours.

Safety-aware vehicle-following driving optimization of intelligent and connected vehicle at signalized road intersection

The driving safety at signalized road intersections is a critical issue for intelligent and connected vehicles (ICVs). Vehicle-following driving scenarios are common conditions at road intersections, as the traffic congestion often occurs at these locations. This paper proposes a safety-aware vehicle-following driving optimization strategy (SAVFDOS) for ICVs at signalized road intersections. The framework of the SAVFDOS includes three layers, i.e., the situation assessment layer, the decision-making layer and the speed planning layer. The situation assessment layer evaluates the likelihood of the vehicle passing through the signalized road intersection and the safety of the vehicle-following driving. The decision-making layer determines the ICV's actions, such as acceleration, deceleration, cruising and stop. The speed planning layer outputs the planned speed based on the situation assessment layer and the decision-making layer. With the SAVFDOS, the rear-end collision and the dilemma zone (DZ) problem can be simultaneously avoided. The validations are conducted by comparing the proposed method with an advanced benchmarked method. Compared with the benchmarked method in the simulation scenarios, the average time proportion of the following vehicle in DZ by the proposed method can be decreased by 33%. In addition, the real-world validations demonstrate that the average time proportion of the following vehicle in DZ by the proposed method is lower than 25%. The validation results prove that the proposed method can eliminate the potential risks of ICVs in vehicle-following driving scene at signalized road intersection.

Mitigation of signalized intersection collision risks with trajectory based dynamic dilemma zone protection

Dilemma zone is one of the major factors causing red-light violations, right-angled and rear-end crashes at signalized intersections. In this paper, a dilemma zone protection system is introduced, which employs a dynamic vehicular trajectory optimization approach to guide vehicles approaching a signalized intersection. Unlike conventional methods that aim to eliminate dilemma zones, this system adjusts the speed profiles of individual vehicles to shift the distribution of dilemma zones and prevent vehicles from becoming trapped. Extensive simulated experiments were conducted to test and validate the proposed system for both individual vehicles and platoons. Results demonstrate that the system offers superior protection for individual vehicles, with full coverage across various settings of initial speeds and distances to the stop line. In the traffic environment with realistic platooning settings, the proposed system significantly reduces the number of vehicles in the dilemma zone, resulting in improved operational and safety benefits such as reduced risks of hazardous maneuvers and savings in vehicular delay.

An integrated intelligent intersection control system for preventing rear-end and angled crashes: System design and deployment results

In view of the dynamic all-red extension (DARE) system’s effectiveness in preventing angled crashes (Park et al., 2018), this study has further enhanced its function to contend with rear-end collisions with dynamic green extension (DGE). With such a function, the enhanced Integrated Intelligent Intersection control system (III-CS) is capable of dynamically terminating the green at the interval of the lowest rear-end collision risk, so as to prevent undesirable “max-out” under actuated signal control which often traps some vehicles in the dilemma zone during high-volume traffic conditions. To ensure its effectiveness in practice, the proposed III-CS has been designed with the following new features: (i) executing the DGE within a customized time window of the green phase to ensure the signal’s effective coordination with its neighboring intersections; (ii) adopting the comparison-based heuristic for the DGE’s real-time risk prediction so as to circumvent the computing and communications delays. The results of two after-deployment assessments show that the system’s DARE has perfectly detected all red-light runners; 66.7 percent of the decisions by the DGE module were observed to achieve the control objective during the first field assessment. The DGE’s performance in making optimal decisions has improved over time and reached the level of 81.3% in the second field evaluation. Other measures of effectiveness, such as the number of vehicles trapped in the dilemma zone and the average deceleration rate of the driving populations approaching the target intersection, have also evolved to the anticipated trend after the deployment.

Drivers’ Dilemma at High-Speed Unsignalized Intersections

At unsignalized intersections, drivers typically reject smaller gaps and accept larger gaps in traffic. However, drivers experience a dilemma or confusion over a wide range of gaps, since incorrect decisions by them can lead to crashes. The present study quantifies the drivers’ dilemma at high-speed unsignalized intersections. Traffic data on gap size (temporal and spatial), driver’s decision (acceptance or rejection), the waiting time of the offending vehicle, the offending and conflicting vehicle types, and the speed of the conflicting vehicle are extracted from recorded video. The decisions by drivers to accept or reject gaps are modeled as a function of the gap size, the waiting time of the offending vehicle, and the speed of the conflicting vehicle using binary logit regression. The results reveal that the probability of rejection decreases as the gap size and waiting time of the subject vehicle increases. On the contrary, as the speed of the conflicting vehicle increases, the probability of rejection increases. The length and location of the dilemma zone were investigated using vehicle type, right-turning movement, and the speed of the conflicting vehicle, and a significant effect was noted. Moreover, the length and location of the dilemma zone are analytically quantified based on drivers’ compliance with a driver assistance system (DAS). The length of the dilemma decreases as the proportion of drivers’ compliance with the DAS increases. Further, the location of the dilemma zone shifts upstream of the intersection with an increase in drivers’ compliance with the DAS.

Signal Phasing and Timing Prediction Using Connected Vehicle Data

Signal phasing and timing can be adaptive and actuated in practice. This makes it challenging to understand what the cycle length and phase duration of the next few cycles will be. Many innovative applications can be designed based on the knowledge of future signal timing states such as dilemma zone warning, efficient route planning, and so forth. This work proposes a long short-term memory model capable of predicting both cycle length and phase duration prediction up to six cycles in the future. GPS information of several vehicles are merged with signal timing information of eight intersections. Several key features such as waiting time, approach speed and acceleration, departing speed and acceleration, are calculated based on the geolocation of individual journeys. The results show that cycle length prediction can reach mean absolute error (MAE) of about 7 s while phase prediction MAE is about 9 s.

Influence of cognitive processes on driver decision-making in dilemma zone

In the past, research, which addressed driver behaviour in dilemma zone while approaching signalised intersections, did not focus on cognitive processes underlying stop/go decisions and collision risk after yellow-onset. Therefore, a study in a dynamic driving simulator with 20 participants was conducted to examine cognitive processes as basis of decision-making at signalised intersections. The study followed a 2×2 design with repeated measures. First, cognitive processes were triggered by perceptual cues, like the onset of yellow and a car in a leading position. Second, a cognitive distracting secondary task, the drivers had to solve interfered with cognitive processes. The results show, that a car in a leading position increases the probability not to stop after yellow-onset. Furthermore, the cognitive distracting secondary task leads to longer perception-response times (PRT) after yellow-onset, but only if there was no car in a leading position. Additionally, pupillary responses of the drivers during cognitive loading driving conditions are supporting this pattern of results. Finally, the concept of coupled motion is suggested to explain the underlying stop/go pattern of drivers after yellow-onset.

Dynamic dilemma zone protection system for high-speed signalized intersections: A comprehensive safety -operational assessment

Dynamic dilemma zone protection (DZP) system with continuous vehicle tracking via radar sensors has been implemented at multiple locations in Alabama to promote the safety and operational efficiency of high-speed signalized intersections. The present study discusses the main features of the DZP system, and its comprehensive assessment in three important aspects: changes in (1) vehicle arrival distributions in different signal intervals (green, yellow, and red), (2) dilemma zone length and location, and (3) dilemma zone conflicts before and after the DZP system implementation. For the system assessment, an extensive amount of data collection and analysis efforts were performed with radar sensor-based vehicle attribute data and high-resolution signal controller data collected from seven intersection approaches where a DZP system has been recently implemented. The analysis results showed that vehicle arrivals on green intervals were significantly increased after the DZP system implementation, while vehicle arrivals on the yellow and red intervals were both reduced significantly despite no change in total vehicle arrivals before and after the system implementation. The chance of dilemma zone conflicts (i.e., red-light running and abrupt stops) was also significantly reduced after the DZP system implementation. Results also showed that the dilemma zone length became shorter, and its location was closer to the intersection stop bar after its implementation. Thus, the present study claims that the DZP system promotes intersection operational efficiency by effectively utilizing available green time as well as improving intersection safety by reducing the number of vehicles trapped within the dilemma zone.

Evaluation of Dilemma Zone Protection on High-Speed Arterials with Signal Coordination

Evaluation of Dilemma Zone Protection on High-Speed Arterials with Signal Coordination

Assessing the Impact of Safety Countermeasures on Dilemma Zones at Signalized Intersections of Urban Roads: a Driving Simulator Study

When drivers approach a signalized intersection at the start of the yellow signal, they might be reluctant to decide whether to stop or go through the intersection due to the dilemma zone (DZ), with the consequent risk of improper behaviors that can lead to rear-end collisions and right-angle crashes. The main goal of this research is to analyze the driver's behavior at the onset of the yellow signal and to identify the most effective countermeasures for the resolution of the dilemma zone in order to improve the safety and efficiency of urban signalized intersections. A driving simulator study was carried out, and three different countermeasures were specifically designed and tested: Green Signal Countdown Timers (GSCT) (C1); a new scheme of vertical and horizontal warning signs (C2); and an in-vehicle advanced driving assistance system that uses Augmented Reality (AR) and connected vehicle technologies (C3). Forty-six volunteers took part in the experiments, driving the same scenario four times (three with countermeasures and one as a baseline condition). The results demonstrated that with countermeasure C1, the length of the DZ increased with respect to the baseline condition; moreover, the stop/go drivers’ decisions were found to be more inconsistent, increasing the potential risk of rear-end crashes. Conversely, countermeasures C2 and C3 resulted in a reduction of DZ length (-30.5% and 21.6%, respectively); in addition, C2 was found to be the one that recorded the greatest consistency of drivers’ decision-making behaviors, while C3 provided the drivers with timely and personalized early-stop warnings and recorded fewer wrong behaviors. Based on the results of this study, C2 and C3 countermeasures might be a good way to improve safety and operations at urban signalized intersections and cut down on the number of drivers who aren't sure what to do when the yellow light comes on.

Comparison of dilemma zone length for car drivers between PTV Vissim and visual observation

There is a zone known as the dilemma zone located on each arm of a signalized intersection, where in this zone, the vehicle at the start of the yellow light is hesitant to stop safely, before the stop line or continues past the intersection with the start of the red light. In the dilemma zone, there is a decision to go through the intersection at the risk of colliding with a car coming from the other arm, or a decision to stop suddenly which might result in a rear-end collision. The longer the dilemma zone, the higher the risk that the driver will pass through the intersection. Therefore, the purpose of this study is to compare the results between visual observations and PTV Vissim results regarding the yellow light dilemma zone for 3 seconds at the 4-arm intersection for car drivers. using the Gaziz method. The dilemma zone for passenger cars from visual observation compared to PTV Vissim simulation has a result of 55% longer with a distance of 6,15m to 28,65 m, where PTV Vissim shows a distance of 2,79 m to 11,25 m. This distance is based on the denser traffic situation during peak hour.