Yellow Interval Research Articles

Understanding Drivers' Compliance Behavior: Data-Driven Assessment of Longer Yellow Intervals

Red-light running (RLR) behavior poses significant risks at signalized intersections and has emerged as a leading cause of intersection-related crashes. The Phoenix metropolitan area had 113 RLR-related fatalities and 9,320 injuries from 2014 to 2020. To effectively mitigate RLR violations and uphold the safety of all road users, it is crucial to investigate RLR behavior at local intersections, evaluate the impact of different signal timing parameters—such as the yellow interval—on the frequency of RLR violations, and, finally, identify effective countermeasures. This study investigated the effect of updating the yellow interval on the frequency of red-light violations. Twelve intersections within the City of Phoenix were carefully selected as study sites. Then, smart sensors were installed to collect various data types, such as signal timing parameters, the vehicle count, and RLR violation data. Based on the ITE 2020 guidelines, yellow intervals were adjusted at each intersection. The effects of increased yellow intervals on RLR violations were examined by utilizing a comprehensive experimental before-and-after design. The before-and-after study results indicated that increasing the yellow intervals significantly reduced the average frequency of RLR violations for both through and left-turn movements by 83% and 72%, respectively. The results of this research are instrumental in informing transportation agencies, enabling them to adopt evidence-based approaches to signal timing strategies that enhance intersection safety and effectively reduce RLR violations.

Extending the decision-making process during yellow phase from human drivers to autonomous vehicles: A microsimulation study with safety considerations

One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different car-following, lane-changing, lateral placement and stop/go model parameter values, we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. We also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (≈15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.

Optimizing traffic light signal timing using a yellow interval model: Methodology and empirical evaluation

All over the world, especially in urban areas, the growing population and prosperity of the car industry have resulted in a steady increment in vehicles on the road, leading that already-built car-transportation infrastructure can not be commensurate with the traffic demand. Consequently, this will cause traffic congestion and thus a higher rate of traffic accidence. In such circumstances, the ability of traffic management becomes indispensable and crucial to ameliorate or even solve this problem. Although various advanced approaches can provide effective traffic management, the cost can be prohibitive to execute them pervasively. Many algorithms, especially machine learning related, require a mass of input data and a high hash rate. As a result, massive sensor systems, advanced computers, and elaborate programming will be unavoidable, which are costly. Two models in this paper, respectively, yellow light interval and traffic-light signalization, are based on summarizing and improving on previous models and are easy to understand and apply. The model focuses on timing the traffic light to maximize the number of vehicles passing through a traffic intersection in a given time. Many authors in their paper on traffic-light signalization hardly consider the effect of yellow light intervals. This paper provides a relatively comprehensive model of traffic-light signalization. Although the model in this paper is inferior to those advanced models utilized in effectiveness, this model can be applied simpler than others.

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.

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.

Severity analysis of red-light–running behavior at signalized intersections

Red-light running (RLR) is one of the riskiest behaviors at signalized intersections. This study utilizes intersection- and corridor-level characteristics to identify variables that impact the frequency and severity of RLR violations. The severity of RLR is defined based on the time when the violations happened. That is, less severe violations are the ones that occur within 3 seconds of the signal light turning red (RLR3), while more severe violations happen between 3 and 10 seconds of the signal light turning red (RLR10). Results of the zero-inflated negative binomial model showed that an increase in intersection delay and split failure increases the frequency of both types of RLR severities. An increase in the yellow interval, cycle length, and number of lanes reduces the frequency of RLR3 but increases the frequency of RLR10. Furthermore, based on the factor importance analysis conducted through the random forest, it was shown that split failure is one of the most predictive variables for RLR3 and RLR10. The findings of this research could help transportation agencies in different geographic jurisdictions utilize the calibrated models to understand the impact of varying intersection- and corridor-based characteristics on the frequency and severity of RLR.

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.

The genomic scale of fluctuating selection in a natural plant population.

This study characterizes evolution at ≈1.86 million Single Nucleotide Polymorphisms (SNPs) within a natural population of yellow monkeyflower (Mimulus guttatus). Most SNPs exhibit minimal change over a span of 23 generations (less than 1% per year), consistent with neutral evolution in a large population. However, several thousand SNPs display strong fluctuations in frequency. Multiple lines of evidence indicate that these 'Fluctuating SNPs' are driven by temporally varying selection. Unlinked loci exhibit synchronous changes with the same allele increasing consistently in certain time intervals but declining in others. This synchrony is sufficiently pronounced that we can roughly classify intervals into two categories, "green" and "yellow," corresponding to conflicting selection regimes. Alleles increasing in green intervals are associated with early life investment in vegetative tissue and delayed flowering. The alternative alleles that increase in yellow intervals are associated with rapid progression to flowering. Selection on the Fluctuating SNPs produces a strong ripple effect on variation across the genome. Accounting for estimation error, we estimate the distribution of allele frequency change per generation in this population. While change is minimal for most SNPs, diffuse hitchhiking effects generated by selected loci may be driving neutral SNPs to a much greater extent than classic genetic drift.

The effects of green signal countdown timer and retiming of signal intervals on dilemma zone related crash risk at signalized intersections under heterogeneous traffic conditions

During yellow intervals, dilemma zones (DZ) often trigger red light violations (RLV) and unexpected stoppings near stop lines, causing severe collisions at signalized intersections. While several studies in developed countries have tested various countermeasures to eliminate DZ, this aspect is not well explored for developing countries. This study investigated a green signal countdown timer’s (GSCT) efficacy in reducing DZ at signalized intersections. Based on historical crash data, 10 signalized junctions in Delhi, India, 5 with GSCT and 5 without, were chosen. Although India has a permissive yellow law, all the study sites have a flat 5 s yellow change interval with no provision of all-red (AR) intervals, which might have resulted in severe crashes at these locations. Empirical assessments revealed that GSCT minimizes the length of type-I DZ, which refers to a situation when drivers can neither stop nor proceed to the intersection during the yellow signal. Interestingly, GSCT also minimizes the length of type-II DZ, which is an indecision zone based on drivers’ 10% and 90% stopping probabilities. Summation of yellow and all-red intervals (Y + AR) obtained using Institute of Transportation Engineers (ITE) equations was found to be longer than field-allocated 5 s change interval at GSCT-enabled sites. Consequently, GSCT’s effectiveness across various yellow and all-red intervals was investigated in the PTV-VISSIM microsimulation tool, and crossing and rear-end conflicts were extracted using Surrogate Safety Assessment Model (SSAM). Results suggest that GSCT’s presence, along with estimated yellow and all-red intervals, reduces crossing and rear-end conflicts due to RLVs and inconsistent stoppings, respectively.

Determining Yellow Change and Clearance Intervals for Left-Turning Phases: Evaluation of the Current Guidelines with Connected Vehicle Data

In March 2020, the Institute of Transportation Engineers (ITE) published new guidelines for determining traffic signal change and clearance intervals that included using an extended kinematic equation for left turns. Whereas previous guidelines assumed constant speed for all vehicles approaching an intersection, this new equation accounted for left-turning vehicles needing more yellow time because they decelerate (assuming a maximum safe and comfortable rate) before making turning maneuvers. This paper evaluates these guidelines using real-world vehicle trajectories from the Ann Arbor Connected Vehicle Test Environment (AACVTE). These trajectories confirm that free-flowing left-turning vehicles decelerate, but deceleration usually starts at a moderate rate before reaching the critical distance and continues to the middle of the intersection. Vehicles then accelerate to a departure speed at the clearance point. Since the goal of the yellow change interval is to eliminate the dilemma zone such that a free-flowing vehicle can safely traverse the critical distance, these observations imply that the extended kinematic equation will overestimate the required duration for the following two reasons: (1) the critical distance is shortened by the speed reduction before the braking point; (2) the average traversing speed is higher as vehicles usually decelerate at a moderate rate instead of the maximum rate. The equation will underestimate the clearance interval as the average traversing speed through the intersection is slower than the intersection entry speed. We propose a new left-turn kinematic equation for determining yellow change and clearance intervals, and the results are validated from the observed vehicle trajectories.

Evaluating Driver Behavior in Yellow Interval at Signalized Intersections

Signalized intersection is one of the most popular type of control at intersections in urban areas. Study of the elements of this type of control is vital to reduce accident rate, improve performance, and decrease delay. Yellow period is one of the parameters which has influence in performance of these type ofintersections specially accident rate. At the indication of yellow interval, when drivers approach a signalized intersection, they need either to proceed and cross the intersection or stop safely before the stop line. The objective of this study is to identify significant factors that impact driver’s decision at yellow interval in dilemma zone. Data was collected using questionnaire survey and video recording technique at two signalized intersections in Duhok city for the period of peak and off-peak hours. Assessing the effects of influential factors including vehicle speed, distance to the stop line and vehicle type on driver behavior has been achieved through observations of trends from graphs and statistical analyses using Logistic regression. Results of questionnaire survey indicate that the younger drivers of (18-30) years are more aggressive and end up running a red light. Moreover, the results of trend analysis showed that number of drivers who run the yellow decreases as the distances to stop line increases and dilemma zone at peak hours are located nearer to the stop line as compared to off peak hours. The lower green time to cycle time ratio in four leg intersection increased the probability of crossing the intersection instead to wait for another green period. Results of all three parts show that both the vehicle speed and distance to the stop line are significant factors affecting driver decision.

The Extent of Capacity Loss Caused by Rainfall at Signalised Intersections

Background: Even though their physical characteristics exert a constant influence on capacity and saturation flows, signalized intersections are fixed facilities not affected by rainfall. Whilst traffic conditions with varying effects can be regulated, rainfall conditions cannot be regulated but compensated for by warning drivers to reduce speed. Speed reduction has an impact on signalised intersection capacity, whilst signalised intersection capacity is a function of saturation flow, effective green, and cycle time. In this paper, a capacity loss is the differential percentage between ‘with and without’ rainfall scenario. Aim: The paper investigated the extent of capacity loss caused by rainfall at signalised intersections. Methods: In Durban, South Africa, rainfall data were collected, collated, and correlated with traffic data in a 'with and without' rainfall intensity study. Rainfall intensity was classified according to the rate of precipitation as follows; rainfall intensity(i): light rain (i <2.5mm/h); Moderate rain (2.5mm/h ≤ i < 10mm/h), and heavy rain (10 ≤ i ≤ 50mm/h) as prescribed by the World Meteorological Society. Results: Empirical results show that rainfall intensity has an effect on road capacity at a signalised intersection. Generally, for the vehicles going straight, light rain caused a 4.25% capacity loss; moderate rain 9.18% while heavy rain caused an 11.53% capacity reduction. With right-turning vehicles, light rain caused 7.38% capacity loss; moderate rain caused 14.3%, while heavy rain accounted for 19.15% capacity reduction. Conclusion: The paper concluded that rainfall at signalised intersections would cause an anomalous capacity reduction. Since the database for the study is small, the paper advocates for further studies based on a broader database to include yellow interval time.

Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity

Stopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping time confirms the existence of group specific effects on drivers. However, the AFT model is developed based on the homogeneity assumption. To overcome this drawback, shared frailty survival models are developed for stopping time analysis, which consider the group heterogeneity of drivers. The results show that log-logistic based frailty model with age as a grouping variable has the best goodness of fit and prediction accuracy. Analysis of the models’ parameters indicates that phone status, maximum deceleration, vehicles’ speed, and the distance to stopping line at the onset of the yellow signal have significant impacts on stopping time. Additionally, heterogeneity analysis illustrates that young, middle-aged, and female drivers are more likely to brake harshly and stop past the stop line, which may block the intersection. Furthermore, drivers, who are more familiar with traffic environments, are more possible to make reasonable stopping decisions approaching intersections. The results can be utilized by traffic authorities to implement road safety strategies, which will help reduce traffic incidents caused by improper stopping behavior at intersections.

Improved Traffic Flow Efficiency During Yellow Interval at Signalized Intersections Using a Smart Countdown System

Inadequate and unnecessary stopping at signalized intersections reduces operational efficiency. The study aims at investigating the effect of a smart advanced warning system on driving behavior and the efficiency of signalized intersections. A green LED dynamic light (G-LED) system (combined system with countdown strategy) is developed and evaluated by means of a driving simulator experiment. The efficiency of the proposed system is compared with a default traffic signal condition with green-yellow-red order (i.e. control condition), and a flashing green (F-green) traffic signal (i.e. order: green-flashing green-yellow-red). Sixty-seven participants holding a valid driving license from Qatar participated in the study. All the participants drove a simulation run replicating the real-world environment of the Corniche road in Doha city (Qatar). Data such as vehicle lateral or longitudinal position, travel speed, acceleration or deceleration, and violation tickets were collected for each participant using the STISIM Drive®Software. Results from a binary logistic regression model showed that the probability of crossing during the change interval significantly increased for the G-LED condition compared to the control condition, while it decreased in the F-green condition. Furthermore, the experiment disclosed that the odds of crossing the intersection (without red-light running) for the proposed smart advanced warning system (G-LED) were 29.67 and 2.58 compared to the F-green and the control conditions, respectively. Furthermore, the time-space and speed analyses revealed that most of the drivers decelerated in the option zone and decided to stop at the intersection equipped with flashing green signal.

Determination and Utilization of Dilemma Zone Length and Location for Safety Assessment of Rural High-Speed Signalized Intersections

The objective of the present study is to see how the dilemma zone length and location are related to intersection safety at rural high-speed signalized intersections. For that, dilemma zones at 30 rural signalized intersection approaches where similar traffic operations and land-use conditions are present were identified, and traffic conflicts associated with the dilemma zones were investigated. Drivers’ stop or go behaviors as well as vehicle dynamics (e.g., vehicle speeds and locations) during the yellow and red clearance intervals were collected and analyzed to determine the dilemma zone length and location of each site. Red-light running violations and vehicles’ abrupt stops were also collected to investigate the relationship between such conflicts and dilemma zone length and location. As a result, two dilemma zone conflict models were developed. The analysis results show that the conflict models are accurate enough to predict the safety level of high-speed signalized intersections using the two dilemma zone variables. Results show that the chance of intersection angle conflicts increases if the dilemma zone is located farther from the intersection stop bar. Results also show that there would be a high chance of rear-end conflicts if the dilemma zone length is longer. The models were validated with additional datasets, and acceptable root means square error and mean absolute percentage error values were obtained as a result.

Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study

The change interval, which includes the yellow and all-red times, plays a crucial role in the safety and operation of signalized intersections. During this interval, drivers not only need to decide to stop or go but also have to interact with drivers both in front and behind, trying to avoid conflicting decisions. Red light running and inconsistent stopping behavior may increase the risk for angular and rear-end crashes. This study aims to investigate the effect of different innovative countermeasures on red light running prevention and safe stopping behavior at signalized intersections. Five different conditions were tested inviting sixty-seven volunteers with a valid driving license. The conditions include a default traffic signal setting (control condition), flashing green signal setting (F-green), red LED ground lights integrated with a traffic signal (R-LED), yellow interval countdown variable message sign (C-VMS), and red light running detection camera warning gantry (RW-gantry). Drivers in each condition were exposed to two different situations based on the distance from the stop line. In the first situation, drivers were located in the indecision zone while in the second situation they were located in the likely stopping zone. A series of logistic regression analyses and linear mixed models were conducted to investigate the overall safety effects of the different countermeasures. The probability of red light running (RLR) was significantly reduced for R-LED in both analyses (i.e. in the total sample, and in the sample of crossed vehicles). Moreover, a clearly inconsistent stopping behavior was observed for the flashing green condition. Furthermore, a unit increase in speed (kph) at the onset of yellow interval significantly increases the probability of RLR by 5.3 %.The study showed that R-LED was the most effective solution for improving red light running prevention and encouraging a consistent stopping behavior at the intersection. In conclusion, the R-LED and the RW-gantry treatments are recommended as effective tools to improve safety at signalized intersections.

Cratonaia novaolindensis gen. et sp. nov. (Unionida, Silesunionoidea) from the Aptian of Brazil (Araripe Basin), and its implications for the early evolution of freshwater mussels

The fossil-rich carbonate deposits of the Aptian Crato Formation, Araripe Basin (Brazil) are one of the main Cretaceous Konservat-Lagerstätten of Gondwana, and have come to fame globally. However, information on fossils from deposits other than the famous laminites of the basal part of the unit is scarce. Herein, we describe the first bivalves of the suborder Silesunionidina Skawina and Dzik, 2011 in the order Unionida Gray, 1854 from South America. The specimens were collected from a 0.3–1-m-thick grey to yellow mudstone interval located 0.3 m above the laminated limestones of the lower part of the Crato Formation at Nova Olinda, State of Ceará. They comprise exquisite composite, internal and external moulds, preserving key anatomical characters. Based on the analysis of muscle scars, hinge and ornamentation, these bivalves are here assigned to a new genus and species, Cratonaia novaolindensis gen. et sp. nov. The presence of a series of small pedal elevator scars linearly arranged on the external wall of the umbonal cavity indicates that this is a member of the suborder Silesunionidina. The new form is the by far youngest representative of this group. Closely related bivalves were previously reported from Triassic deposits of Australia, Africa, Europe and potentially India. Detailed stratigraphic, sedimentological and taphonomic observations indicate that the new taxon thrived in a freshwater lake. The occurrence of Silesunionoidea in the Lower Cretaceous of South America indicates that the condition of the musculature in Mesozoic freshwater mussels needs to be established to assign them confidently at family level.

Intergreen Time Calculation Method of Signalized Intersections Based on Safety Reliability Theory: A Monte-Carlo Simulation Approach

In China, around ninety percent of the traffic accidents at signalized intersections occur within the signal change intervals, especially during signal change from green to red. Hence, intergreen time (IGT), that is, yellow change interval plus red clearance interval, is of great significance to the safety at signalized intersections. The conventional calculation method of IGT ignores the randomness of drivers’ behaviors, which we believe is an important factor in calculation of IGT. Therefore, the purpose of this research is to investigate a new approach to calculate the IGT based on safety reliability theory. Firstly, a comprehensive literature review concerning the conventional calculation methods of IGT is conducted. Secondly, a theoretical calculation method of IGT based on safety reliability theory is put forward; different from the conventional methods, this model accounts for the uncertainty of driving behavior parameters. Thirdly, a Monte-Carlo simulation is employed to simulate the interactive process of perception-reaction time (PRT) and vehicular deceleration and solve the proposed model. Finally, according to the Monte-Carlo simulation results, the curve clusters describing the relationship between IGT, safety reliability (50%-90%), and intersection width (15-35m) are drawn. Results show that the IGT of a signalized intersection, obeying the normal distribution, is influenced by multiple factors and most sensitive to the PRT and vehicular deceleration. Our method thus successfully incorporates the probabilistic nature of driving behavior. Taking the safety reliability into consideration can provide a more reasonable method to calculate the IGT of signalized intersections.

Generalized Estimating Equation Model Based Recursive Partitioning: Application to Distracted Driving

Traditional statistical methods have used a coarse aggregation of data across subjects that may not be representative of any single individual. Even though Generalized Estimating Equations procedure extends generalized linear model to allow for analysis of repeated measurements or other correlated observations, the nonlinear relationship between independent variables and dependent variable could significantly hinder the model’s performance. In this study, we propose Generalized Estimating Equation based tree model that combines the advantages of both models by separating the data set recursively into subsets with significantly different parameter estimates. For the best application of the proposed model, distracted driving on intersection is analyzed in this study. Previous studies have focused on evaluating the singular effect of individual geometry and human characteristic variables on driving behaviors. Interactions between variables associated with red-light running (e.g., cell phone usage, cell phone interface, and driver age groups) present different levels of distraction on red-light running. As an indicator of the sensitivity to distractions (referring to the distance being an impairment due to a secondary task), the drivers’ distance to the intersection onset of yellow interval is partitioned into two groups (i.e., close and far distance) that maximally differentiate the distraction behavior. Proposed cell phone impact zones produce more significant impacts of distraction on red-light running, compared against dilemma zone. We identify interactions that are sensitive to red-light running and different as a function of the level of the speed and yellow interval duration. Drivers are more vulnerable to cell phone distractions when their location is near the stop line of the intersection at onset of yellow interval.

Exploring the impact of signal types and adjacent vehicles on drivers’ choices after the onset of yellow

Drivers’ choices at signalized intersections may be made in great uncertainty after the onset of yellow, which creates potential hazards for road safety. These choices are analyzed and modeled based on field observations at three comparable signalized intersections in Changsha, China. The results show that intersections without monitoring devices widen the indecision zone, which can increase the risk of rear-end collisions and the uncertainty of drivers’ decision-making. In addition, drivers are more likely to stop during the yellow interval at intersections equipped with a green signal countdown device (GSCD) than at those with a green signal flashing device (GSFD). Subsequently, according to the results of a binary logistic regression model (BLRM), drivers’ decision making at the onset of the yellow indication is greatly influenced by the vehicle’s spot speed, the distance to the stop line, and signal and monitoring devices. The presence of an adjacent vehicle with a short space headway can particularly motivate the following driver to make a go-decision after the first driver chooses to pass the intersection. However, a stop-decision by a driver in an adjacent lane can also prompt the following driver to stop.