Onset Of Yellow Research Articles

Large Scale Evaluation of Normalized Hard-Braking Events Derived from Connected Vehicle Trajectory Data at Signalized Intersections, Roundabouts, and All-Way Stops

Intersection safety has been traditionally evaluated using three to five years of crash data. Recent literature suggests that connected vehicle (CV)-derived hard braking (HB) events can provide a surrogate for crashes with only a few weeks or months of data collection. This study used CV trajectories to derive HB events. Then, the HB events were normalized as the ratio of HB events to sampled CV trajectories. The normalized HB ratios were evaluated and compared at 435 signalized intersections, roundabouts, and all-way stops in Indiana. The analysis showed that signalized intersections and roundabouts had the highest counts of HB events, and all-way stops had the highest HB ratios. Through movements at signalized intersections showed the lowest HB ratios, whereas left turns at all-way stops had the highest ratios. A density analysis of the geospatial occurrence of HB events concluded that they tend to occur closest to the intersection center at all-way stops, but are more evenly distributed at signalized intersections. Additionally, a speed analysis indicated that HB events at signalized intersection through movements tend to occur at higher speeds, roughly between 26 and 36 MPH, perhaps due to the driver reaction during the onset of yellow. The findings presented in this study provide transportation agencies with insights on the occurrence of normalized HB ratios at three different intersection types. The data provided in this paper provide a framework for agencies to use HB ratios to screen different types of intersections for further evaluation.

A Transcriptomic Analysis of Bottle Gourd-Type Rootstock Roots Identifies Novel Transcription Factors Responsive to Low Root Zone Temperature Stress.

The bottle gourd [Lagenaria siceraria (Molina) Standl.] is often utilized as a rootstock for watermelon grafting. This practice effectively mitigates the challenges associated with continuous cropping obstacles in watermelon cultivation. The lower ground temperature has a direct impact on the rootstocks' root development and nutrient absorption, ultimately leading to slower growth and even the onset of yellowing. However, the mechanisms underlying the bottle gourd's regulation of root growth in response to low root zone temperature (LRT) remain elusive. Understanding the dynamic response of bottle gourd roots to LRT stress is crucial for advancing research regarding its tolerance to low temperatures. In this study, we compared the physiological traits of bottle gourd roots under control and LRT treatments; root sample transcriptomic profiles were monitored after 0 h, 48 h and 72 h of LRT treatment. LRT stress increased the malondialdehyde (MDA) content, relative electrolyte permeability and reactive oxygen species (ROS) levels, especially H2O2 and O2-. Concurrently, LRT treatment enhanced the activities of antioxidant enzymes like superoxide dismutase (SOD) and peroxidase (POD). RNA-Seq analysis revealed the presence of 2507 and 1326 differentially expressed genes (DEGs) after 48 h and 72 h of LRT treatment, respectively. Notably, 174 and 271 transcription factors (TFs) were identified as DEGs compared to the 0 h control. We utilized quantitative real-time polymerase chain reaction (qRT-PCR) to confirm the expression patterns of DEGs belonging to the WRKY, NAC, bHLH, AP2/ERF and MYB families. Collectively, our study provides a robust foundation for the functional characterization of LRT-responsive TFs in bottle gourd roots. Furthermore, these insights may contribute to the enhancement in cold tolerance in bottle gourd-type rootstocks, thereby advancing molecular breeding efforts.

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.

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.

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.

Advanced Gap Seeking Logic for Actuated Signal Control Using Vehicle Trajectory Data: Proof of Concept

As detection systems improve, opportunities are emerging for using vehicle position and speed to drive signal control. This study explored how basic actuation processes might be improved by using vehicle position and speed. The position and speed of arriving vehicles were used to calculate their estimated time of arrival. Two variations on vehicle extension logic were developed that extended a green until there was a headway in the arriving traffic above a size that corresponded to a minimum flow rate, at which it was desired to terminate the phase. In the first method, the headway was measured from the point where the leading vehicle passed the stop bar, whereas the second method measured the headway from where the leading vehicle was unlikely to stop at the onset of yellow. This was compared against a control with a conventional stop bar and upstream detectors. A simulation study was carried out for an eight-phase intersection. The results suggest that at higher levels of demand, trajectory-based actuation could yield substantial reductions in delay. The trajectory-based methods were able to terminate green more efficiently, leading to reductions in delay in some cases, and reductions in emissions and fuel consumption, although there was a tradeoff in the number of split failures. These early results show promise for the development and fine-tuning of the methods.

Analyzing driver's response to the yellow onset at signalized intersections

Intersections are critical points within the highway system at which the risk of crashes increases. This study seeks to better understand drivers’ behavior at an intersection by examining the relationship between their observed driving behavior, psychological attributes and decision to proceed through an intersection. A driving simulator and self-report questionnaire were used to understand driver decision-making at the onset of the yellow phase across several signalized intersections. The simulator measured driving outcomes such as speed, braking, and throttle as drivers cross through four increasingly difficult intersections. The questionnaires measured demographics, psychological traits including mindfulness and impulsiveness along with self-reported driving behaviors. A total of 102 participants completed the questionnaire as well as the driving simulator experiment. Hierarchical clustering served to classify drivers into four groups on the basis of their observed driving in the simulator: the safest drivers, safe drivers, speed demons, and aggressive drivers. These driving styles moderated the relationship between the drivers’ psychological traits and their decision to stop or proceed at each intersection. Results showed that mindfulness was highly related to the safest drivers’ decision to stop at the first intersection, while impulsiveness and anxiety were related to the speed demons’ decision to stop at the third intersection. These findings lay a strong foundation for developing progressive educational campaigns incorporating driver psychology in their methodology. Findings also provide support for research linking driving performance and psychological traits with implications for intersection design.

Modeling dynamic distribution of dilemma zone at signalized intersections for developing world traffic

Drivers approaching a signalized intersection during the onset of yellow are often in a dilemma whether to stop or go. It has been reported in past studies that drivers in India are aggressive and often run a red light, creating dangerous situations. In this study, the distribution of the dilemma zone is analyzed for selected signalized intersections characterized by mixed traffic conditions. A total of 893 vehicle trajectories and the drivers’ responses during the yellow phase were recorded at three high-speed signalized intersections. The drivers’ behavior intending to stop or go during the yellow phase was modeled as a binary decision. The length and location of the dilemma zone were found to be influenced by the vehicle speed during the yellow phase, distance from stop line, vehicle type, and lane occupied, indicating the dynamic nature of the dilemma zone. The findings of the effect of vehicle type revealed that the car has the longest length of the dilemma zone, whereas the truck has the shortest length. The analysis of approach speeds showed that 85th percentile speed at the onset of yellow was higher than the posted speed limit, indicating the aggressive and incompliance nature of the drivers.

Perceptual-based driver behaviour modelling at the yellow onset of signalised intersections

This study aims at modelling unassisted drivers’ speed at the yellow onset to enhance Connected and Autonomous Vehicles applications at signalised intersections and maximise drivers’ comfort. For this purpose, a total of 2442 real-life vehicle trajectories were analysed to extract driver behavioural measures (i.e. speed, acceleration, and distance to intersection) at different times before the yellow onset. These behavioural measures were used to integrate drivers’ perceptual ability into modelling drivers’ speed at the yellow onset. To develop these models, three machine learning techniques; namely, linear regression, Support Vector Machine, and Neural Networks have been adopted. The best model was a neural network model and was selected based on the goodness-of-fit of the test dataset which has an R-squared value of 0.97. The results indicate that the speed at the yellow onset can be estimated based on behavioural measures while accounting for drivers’ perceptual ability. Also, the model can contribute to a V2I application by assisting the driver in a partially autonomous vehicle to avoid trapping in the dilemma zone and stop safely at signalised intersections. Also, the model can be used to recommend a comfortable riding speed, from a rider’s perspective to a fully autonomous vehicle.

Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles

The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.

Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China

Rear-end accidents are the most common accident type at signalized intersections because of the different driving tendencies in the dilemma zone (DZ), where drivers are faced with indecisiveness of making “stop or go” decisions at yellow onset. In various researches, the number of vehicles in the DZ has been used as a safety indicator—the more the vehicles in the DZ, the higher the probability of rear-end accidents. However, the DZ-associated rear-end accident potential varies depending on drivers’ driving tendencies and the situations (position and speed) at the yellow onset. This study’s primary objective is to explore how the driving tendency impacts the DZ distribution and the probability of rear-end accidents. To achieve this, three types of driving tendencies were classified using K-means clustering analysis based on driving variables. Further, the boundary of the DZ is determined by logistic regression model of drivers’ stop/go decision. Then, we proposed the conditional probability model of rear-end accidents and developed a Monte Carlo simulation framework to calculate the model. The results indicate that the rear-end accident probability is dependent on the driving tendency even at the same position with the same speed in the DZ. The aggressive type has the highest risk probability followed by conservative and then the normal types. The quantitative results of the study can provide the basis for rear-end accident assessments.

Driver behaviour at the onset of yellow signal: A comparative study of distraction caused by use of a phone and a music player

The present study attempts to contribute to the literature by identifying and comparing the effects of two important distractions, i.e., phone conversation and music player operations, on drivers’ stop/cross decisions at the yellow onset. For this purpose, 74 participants performed driving experiments on a simulated urban route with six signalised intersections. Driver distraction, demographics, intersection configurations and driving attributes were considered as independent variables. Additionally, some specific situations (combinations of the different attributes) associated with the stop/cross decision were identified with a decision tree method. A generalised linear mixed model with a logit link function was developed for estimating the probability of crossing an intersection by considering all the main variables and the potential interaction obtained from the decision tree. The results highlighted that the driving speed was positively associated with the crossing probability. Whereas, Time to Stop Line (TSL), type of manoeuvre and the presence of the distractions had a negative influence on the probability. The odds of crossing an intersection were 17% lesser for the music player distraction than the phone conversation. Additionally, six interaction variables provided insights into effects of age, gender and driving experience on drivers’ stop/cross decision in some specific situations.

Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions

Intersections are important node points in the road network, ensuring safe and efficient way of maneuvering the traffic. The Ministry of Road Transport and Highways (MORTH) reported in year 2016 that the highest number of road accidents in India happened at intersections accounting for nearly thirty seven percent (37%) of the total crashes that took place. Even though traffic signals are considered to be the most effective way of controlling the traffic, more than 4300 lost their lives at signalized intersections in India. One of the main contributing factor in traffic signal related crashes is the presence of dilemma zone, where a driver becomes indecisive whether to pass or stop at the intersection on the yellow onset. Significant amount of research has been done on the dilemma driver behavior under homogeneous traffic conditions, however little or no research has been found on mixed traffic conditions, where vehicles do vary in physical and dynamic characteristics. The main motive of this study is to investigate the factors influencing the driver behavior in dilemma zone at signalized approaches in India under mixed traffic conditions. Field data was collected at five signalized approaches using video capturing technique to investigate the driver behavior. Frame by frame manual extraction resulted in 1054 driver responses at the yellow onset and binary logistic regression model is developed to represent the observed behavior. Distance from stop line, vehicle’s approach speed and type of intersection were found to be important factors in drivers stop/go decisions. Vehicle type, which is a characteristic of mixed traffic conditions is found to have a significant impact on the driver’s decision at the onset of yellow. The insights from this study findings can be used to enhance the safety and performance of signalized intersections in developing countries.

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.

Impact of pavement type on speed behavior of turning vehicles at crosswalk of a signalized intersection: Brick and red color

ABSTRACTLeft- and right-turning vehicles are allowed to make a turn through the crosswalk allocated for pedestrians to cross the road safely. When drivers share this space with pedestrians, they are expected to travel at a low or near-walking speed. Before approaching the crosswalk, left- and right-turn drivers must adapt their speed so as to not endanger the pedestrians. This article presents a study comparing the effectiveness of brick and red-colored pavement on changing the speed of the driver when they make a turn through the crosswalk at a signalized intersection. Three intersections with different pavement design scenarios (no pavement design, brick pavement, and red-colored pavement) were selected for the field study. The speed at three situations—free flow condition, at yellow onset, and when accepting the gap with a pedestrian—were analyzed to evaluate the speed behavior. This data were analyzed using multiple linear regression. Results show a positive effect of brick pavement on the crosswalk on reducing a driver's speed in left and right turns.

Modeling Human Learning and Cognition Structure: Application to Driver Behavior in Dilemma Zone

AbstractIn transportation studies, modeling human learning and decision-making processes plays a key role in developing realistic safety countermeasures and appropriate crash-mitigation strategies. In this study, a human learning model was created that captures the cognitive structure of human memory. The relationship between long-term and short-term memories was incorporated into a reinforcement learning technique to construct the human learning model. The model was then applied to dilemma zone data collected in a simulator study. Dilemma zone is an area of roadway ahead of the signalized intersection in which drivers have difficulty deciding whether to stop or proceed through at the onset of yellow. Driver choice behavior and learning process in dilemma zones was modeled, taking into account drivers’ experiences at the previous intersections, and was compared to a pure machine learning model. The results of the model revealed lower and faster-merging errors when human learning was considered in training...

Red-light running violation prediction using observational and simulator data

In the United States, 683 people were killed and an estimated 133,000 were injured in crashes due to running red lights in 2012. To help prevent/mitigate crashes caused by running red lights, these violations need to be identified before they occur, so both the road users (i.e., drivers, pedestrians, etc.) in potential danger and the infrastructure can be notified and actions can be taken accordingly. Two different data sets were used to assess the feasibility of developing red-light running (RLR) violation prediction models: (1) observational data and (2) driver simulator data. Both data sets included common factors, such as time to intersection (TTI), distance to intersection (DTI), and velocity at the onset of the yellow indication. However, the observational data set provided additional factors that the simulator data set did not, and vice versa. The observational data included vehicle information (e.g., speed, acceleration, etc.) for several different time frames. For each vehicle approaching an intersection in the observational data set, required data were extracted from several time frames as the vehicle drew closer to the intersection. However, since the observational data were inherently anonymous, driver factors such as age and gender were unavailable in the observational data set. Conversely, the simulator data set contained age and gender. In addition, the simulator data included a secondary (non-driving) task factor and a treatment factor (i.e., incoming/outgoing calls while driving). The simulator data only included vehicle information for certain time frames (e.g., yellow onset); the data did not provide vehicle information for several different time frames while vehicles were approaching an intersection. In this study, the random forest (RF) machine-learning technique was adopted to develop RLR violation prediction models. Factor importance was obtained for different models and different data sets to show how differently the factors influence the performance of each model. A sensitivity analysis showed that the factor importance to identify RLR violations changed when data from different time frames were used to develop the prediction models. TTI, DTI, the required deceleration parameter (RDP), and velocity at the onset of a yellow indication were among the most important factors identified by both models constructed using observational data and simulator data. Furthermore, in addition to the factors obtained from a point in time (i.e., yellow onset), valuable information suitable for RLR violation prediction was obtained from defined monitoring periods. It was found that period lengths of 2–6m contributed to the best model performance.

Driver decision-making in the dilemma zone – Examining the influences of clearance intervals, enforcement cameras and the provision of advance warning through a panel data random parameters probit model

In recent years, there have been a series of innovations in the field of vehicle detection at intersection approaches. Modern radar-based smart sensors make it possible to track individual vehicles in close proximity to an intersection. These advancements in technology potentially enable the provision of vehicle- and site-specific decision dilemma zone protection at the onset of the yellow indication at signalized intersections. To exploit this opportunity, it is critical to develop an in-depth understanding of those factors influencing a driver's decision to stop or go at the onset of yellow. This study investigates how signal timing strategies such as yellow interval durations, all-red clearance intervals, advance warning flashers, and automated camera enforcement affect driver decision-making. Data from 87 intersection approaches across five regions of the United States are used to develop a series of decision (i.e., probability of stopping) curves using vehicle trajectory and signal phasing data. A panel data random parameters probit model is used to account for heterogeneity across locations, as well as correlation in driver decision-making, due to unobserved factors that are unique to each signalized intersection. The results demonstrate drivers are more likely to stop at locations where enforcement cameras or flashers are present. Stopping was also more prevalent at intersections with lower speed limits, longer crossing distances, and where pedestrian crosswalks were present.

Comparative analysis of driver’s brake perception-reaction time at signalized intersections with and without countdown timer using parametric duration models

Countdown timers display the time left on the current signal, which makes drivers be more ready to react to the phase change. However, previous related studies have rarely explored the effects of countdown timer on driver’s brake perception-reaction time (BPRT) to yellow light. The goal of this study was therefore to characterize and model driver’s BPRT to yellow signal at signalized intersections with and without countdown timer. BPRT data for “first-to-stop” vehicles after yellow onset within the transitional zone were collected through on-site observation at six signalized intersections in Harbin, China. Statistical analysis showed that the observed 15th, 50th, and 85th percentile BPRTs without countdown timer were 0.52, 0.84, and 1.26s, respectively. The observed 15th, 50th, and 85th percentile BPRTs with countdown timer were 0.32, 1.20, and 2.52s, respectively. Log-logistic distribution appeared to best fit the BPRT without countdown timer, while Weibull distribution seemed to best fit the BPRT with countdown timer. After that, a Log-logistic accelerated failure time (AFT) duration model was developed to model driver’s BPRT without countdown timer, whereas a Weibull AFT duration model was established to model driver’s BPRT with countdown timer. Three significant factors affecting the BPRT identified in both AFT models included yellow-onset distance from the stop line, yellow-onset approach speed, and deceleration rate. No matter whether the presence of countdown timer or not, BPRT increased as yellow-onset distance to the stop line or deceleration rate increased, but decreased as yellow-onset speed increased. The impairment of driver’s BPRT due to countdown timer appeared to increase with yellow-onset distance to the stop line or deceleration rate, but decrease with yellow-onset speed. An increase in driver’s BPRT because of countdown timer may induce risky driving behaviors (i.e., stop abruptly, or even violate traffic signal), revealing a weakness of countdown timer in traffic safety aspect.

Assessment of driver stopping prediction models before and after the onset of yellow using two driving simulator datasets

Accurate modeling of driver decisions in dilemma zones (DZ), where drivers are not sure whether to stop or go at the onset of yellow, can be used to increase safety at signalized intersections. This study utilized data obtained from two different driving simulator studies (VT-SCORES and NADS datasets) to investigate the possibility of developing accurate driver-decision prediction/classification models in DZ. Canonical discriminant analysis was used to construct the prediction models, and two timeframes were considered. The first timeframe used data collected during green immediately before the onset of yellow, and the second timeframe used data collected during the first three seconds after the onset of yellow. Signal protection algorithms could use the results of the prediction model during the first timeframe to decide the best time for ending the green signal, and could use the results of the prediction model during the first three seconds of yellow to extend the clearance interval. It was found that the discriminant model using data collected during the first three seconds of yellow was the most accurate, at 99% accuracy. It was also found that data collection should focus on variables that are related to speed, acceleration, time, and distance to intersection, as opposed to secondary variables, such as pavement conditions, since secondary variables did not significantly change the accuracy of the prediction models. The results reveal a promising possibility for incorporating the developed models in traffic-signal controllers to improve DZ-protection strategies.