Impact of “Signal Ahead” pavement marking on driver behavior at signalized intersections

When drivers are approaching a signalized intersection at the onset of yellow signal, they may hesitate to decide whether to stop or cross the intersection due to the dilemma zone. As defined in the Traffic Engineering Handbook [Institute of Transportation Engineers (ITE). (1999). Traffic engineering handbook, Washington, DC: Institute of Transportation Engineers], the dilemma zone for an intersection is a specific road segment, prior to the intersection in which a vehicle approaching the intersection during the yellow phase can neither safely clear the intersection, nor stop comfortably at the stop line. In this study, a pavement marking countermeasure is proposed to reduce the dilemma zone and improve traffic safety at signalized intersections. A pavement marking with word message ‘Signal Ahead’ that is placed on the pavement upstream of a signalized intersection may have a potential to assist drivers in stop/go decisions and improve driver performance during the signal change. This paper presents a simulator-based study that was designed to test the impact of the pavement marking countermeasure on driving behaviors, including driver’s stop/go decision, red-light running violation, brake response time, and deceleration rate of the stopping vehicle. The experiment results indicated that the marking had positive effects on drivers’ behaviors at signalized intersections. It was found that the marking can reduce the probabilities of both conservative-stop and risky-go decisions, contribute to a lower red-light running rate, and result in a lower deceleration rate for stopping drivers at higher speed limit intersections. These findings suggest that the marking countermeasure may have a potential to reduce the probabilities of both rear-end and angle crashes.     

Fuzzy sets to describe driver behavior in the dilemma zone of high-speed signalized intersections

The Type II dilemma zone describes a segment of road on the approach to a signalized intersection where, if occupied by a motorist presented with the circular yellow indication, is likely to result in a motorist having difficulty deciding to stop at the stop line or proceed through the intersection. This phenomenon results in increased frequency of three failure conditions: rear-end collision at the stop line (excessive deceleration rates), the more severe right-angle crashes in the intersections, and left-turn head-on collisions (both resulting from incorrect estimates of clearance time). A more effective boundary definition for Type II dilemma zones could contribute to the safe design of signalized intersections. The prevailing approaches to dilemma zone delineation include the consideration of the vehicle’s travel time to the stop line or the driver’s likelihood of stopping at a particular distance from the stop line. The imprecision of the driver’s perception of speed and distance suggest that fuzzy logic may contribute to the identification of the Type II dilemma zone boundaries. A fuzzy logic (FL) model was constructed and validated from driver’s empirically observed behavior at high-speed signalized intersections. The research resulted in an increased understanding of the phenomenon which, when applied to the timing of signals and the placement of vehicle detection, can improve the overall safety of signalized intersections.     

Safer driver responses at intersections with green signal countdown timers

Traffic signal countdown timers (TSCTs) are innovative, practical, and cost effective technologies with the potential to improve safety at signalized intersections. The purpose of these devices is to assist motorists in decision-making at signalized intersections by providing them with real-time signal duration information. This study examines US driver responses in the presence of a green signal countdown timer (GSCT) and the implications those responses have on intersection safety. A driving simulator study was conducted to record driver responses to virtual GSCTs. Fifty-five participants (32 male and 23 female) responded to 1100 simulated traffic signals, half of which had GSCTs. A predictive model was developed and validated to estimate the change in driver’s probability to stop at different distances from the stop line in the presence of a GSCT. The presence of a GSCT increased average driver stopping probability in the dilemma zone by 13.10%, while decreasing average driver deceleration rates by 1.50 ft/s². These results suggest that GSCTs may contribute to improved intersection safety in the US.     

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.     

Analysing driver’s decision in dilemma zone at signalized intersections under disordered traffic conditions

Delay in the decision-making process of stop or go during the amber phase of the signal cycle often leads to abrupt hard deceleration or red light violations at signalized intersections. The indecisiveness or the dilemma in decision making often results in compromised safety of the road users. The present study attempts to analyze the driver’s behaviour in order to make the decision of stop or go and developed a binary logistic regression model while considering different traffic behaviour parameters exhibited and observed after the onset of the amber phase. Empirical vehicular trajectory data from three signalized intersections covering 121 signal cycles and 1347 vehicles are used in the study. The study presents two dilemma zone identification models based on distance from the stop line, focusing on easy-to-use and static driver assistance and dynamic-realtime driver assistance systems. Both the models are observed to show good fit and prediction accuracy. The models are validated internally and externally for their adaptability in the field. The effect of different traffic parameters on the dilemma zone is explored, and a possible real-time application of the dynamic model as a driver assistance system in decision-making is explored.     

Effects of forward collision warning technology in different pre-crash scenarios

The forward collision warning (FCW) system is expected to reduce rear-end crashes; however, its effects on driving behavior and safety have not been thoroughly investigated, specifically the effect variations between different pre-crash scenarios. To identify these variations, this study conducted a driving simulator experiment and compared the FCW’s effects between three pre-crash scenarios: the freeway scenario, the arterial scenario and the intersection dilemma zone scenario. Thirty-nine participants were involved in the experiment. The results showed that the adaptation of driver behavior in impending rear-end collision events resulted from both the FCW and the scenario. The intersection dilemma zone scenario has indications of slowing down, which encouraged drivers to take a more aggressive response strategy under the FCW; the arterial scenario might be regarded as an “easy-to-handle” situation in which a significant portion of drivers adopted moderate level of response strategy under the FCW; both the intersection dilemma zone scenario and freeway scenario have burdened driving tasks, and this might deteriorate a driver’s ability to adapt to the FCW. In addition, different types of drivers experienced varied benefits from the FCW in each scenario. The FCW would be particularly recommended for non-experienced drivers in the freeway scenario and for female drivers in the arterial scenario; moreover, in the scenario of the intersection dilemma zone, the FCW would be particularly recommended for drivers who have a crash/citation before. The results also support specific FCW designs which are able to highlight the collision risk. This study demonstrated that it would be better to indicate the effects of the FCW under the restriction of specific scenario features and develop the FCW based on that.     

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.     

Impact of time pressure on acceleration behavior and crossing decision at the onset of yellow signal

This study investigates acceleration behavior and crossing decision of the drivers under increasing time pressure driving conditions. A typical urban route was designed in a fixed-base driving simulator consisting of four signalized intersections with varying time to stop line (4 s and 6 s) and maneuver type (right-turn and go-through). 97 participants’ data were obtained under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. The acceleration behavior was examined at the onset of yellow signal in four ways: continuous deceleration, acceleration-deceleration, deceleration-acceleration, and continuous acceleration. A random forest model was used to build an acceleration behavior prediction model for identifying the significant explanatory variables based on variable importance ranking. Further, a Mixed Effects Multinomial Logit (MEML) model was developed using the explanatory variables obtained from a random forest model. Additionally, a generalized linear mixed model was incorporated for estimating the likelihood of crossing an intersection by considering all the explanatory variables. A MEML model result revealed that the odds of adopting acceleration-deceleration, deceleration-acceleration, and continuous acceleration instead of continuous deceleration increased by 63 %, 123 %, and 77 %, respectively under HTP driving conditions. Moreover, the likelihood of crossing a signalized intersection increased by 2.73 times and 4.26 times when the drivers were under LTP and HTP driving conditions, respectively as compared to NTP driving condition. Apart from this, time to stop line (reference: 6 s) and age showed negative association with crossing probability. Overall, the findings from this study revealed that drivers altered their acceleration behavior for executing risky driving decisions under increasing time pressure driving conditions.     

The effect of yellow light onset time on older and younger drivers’ perception response time (PRT) and intersection behavior

To understand why older drivers are over-represented in intersection crashes, this study sought to describe the intersection performance of older and younger drivers when traffic lights changed from green to yellow at the last second. Using a moderate-fidelity driving simulator, time to stop line (TSL) at yellow onset was manipulated as drivers approached intersections at 70 km/h (42 mph). Seventy-seven participants, approximately balanced for gender and age group, volunteered from the age categories of 18 to 24, 25 to 35, 55 to 64, and 65+. Driver decisions to stop or go were predicted using a logistic regression model with time to stop line as the single significant predictor. There were no age differences in perception response time (PRT). Older drivers approached intersections at a lower velocity and stopped more accurately than younger drivers. For those drivers who chose to go through the yellow light, speed profiles across the intersection and intersection clearance indicate that older drivers are more likely to be in the intersection when the light changed to red. A PRT of 1.0 s for yellow signal phasing was sufficient for all age groups.     

Examining the efficacy of improved traffic signs and markings at flashing-light-controlled grade crossings based on driving simulation and eye tracking systems

The majority of the collisions at grade crossings occurred at flashing-light-controlled grade crossings. Understanding drivers’ behaviors and visual performances in the process of approaching the crossings is the foundation of improving crossing safety. This study aims at utilizing driving simulation and eye tracking systems to investigate the efficacy of improved traffic signs and pavement markings (PSM) at flashing-light-controlled grade crossings. The improved signs and markings were modeled in a driving simulation system and tested with a series of flashing light trigger time (FLTT) ranging from 2 s to 6 s with 1 s interval increment. Foggy conditions and drivers’ genders and vocations were considered in experiment design. Thirty-six fully-licensed drivers between 30 and 48 years participated in the experiment. Several eye-movement and behavioral measures were adopted as reflections of the subjects’ performances, including the first fixation time on signs and signals and distance to stop line, total fixation duration, compliance rate, stop position, average speed at the stop line, maximum deceleration rate and brake response time. Results showed that compared with traditional grade crossings signs and pavement markings, drivers could perceive signs timelier and fixate on the flashing-light signal earlier in PSM, especially in the scenarios of earlier FLTTs. The improvement in fixation performance and sign design contributed to a higher stop compliance rate. Importantly, it was found that drivers would hesitate to decide whether to stop or cross facing with flashing red lights, which is similar to the dilemma zone of roadway intersections. Drivers were more likely to fall into the dilemma zones when FLTT was <4 s. When FLTT was 2 s, it was particularly difficult to stop in front of the stop line. Moreover, under a foggy condition, drivers had a difficulty in searching signs and had a longer brake response time compared with a clear condition. For the characteristics of drivers, male drivers had longer fixation duration on signs than females. Professional drivers had a higher maximum deceleration rate compared with non-professional drivers. Above findings implied that improved traffic signs and markings would have a potential to improve traffic safety and deserve a field implementation in the future.     

Driver's avoidance characteristics to hazardous situations: A driving simulator study

When analyzing the causes of an accident, it is critical to determine whether the driver could have prevented the accident. In previous studies on the reaction times of drivers, the definition and values of reaction times vary, so applying reaction time is difficult. In such analysis, the driver’s reaction time from perception is required to determine whether the driver could have prevented the accident, but past studies are difficult to utilize in accident analysis as reaction time measurements were taken after the occurrence of hazardous situations. In this study, 93 subjects from age groups ranging from 20 s to 40 s participated in an experiment inside a full-scale driving simulator, to determine reaction time values that can be practically applied to accident analysis. A total of 4 hazardous accident situations were reproduced, including driving over the centerline, pedestrian jaywalking, a vehicle cutting in, and intersection traffic signal violation. The Time-To-Collision (TTC) was 2.5 s and the driving speed was set to the common city road speed limits of 60 and 80 km/h. An eye tracker was used to determine the driver’s Saccade Latency (SL) during hazardous situations. Brake Reaction Time from Perception (BRT_P), Steer Reaction Time from Perception (SRT_P), and Driver Reaction Time from Perception (DRT_P) were derived, and the measurements were statistically analyzed to investigate differences by age group, gender, speed, and type of hazardous situation. Most participants were found to avoid collisions by braking first rather than steering for the presented hazardous situations, except for the cutting in situation. Also, to determine a reaction time that would cover most drivers, the 85th percentile of DRT_P was calculated. The 85th percentile of DRT_P was in the range of 0.550 – 0.800 s. Specifically for each hazardous situation, it was 0.650 s for driving over the centerline, 0.800 s for the pedestrian jaywalking, 0.660 s for cutting in, and 0.550 s for the intersection traffic signal violation. For all 4 hazardous situations combined, the 85th percentile of DRT_P was 0.646 s. The findings can be utilized to determine the driver’s likelihood of avoiding accidents when faced with similar hazardous situations.     

A study of the compliance level of connected vehicle warning information in a fog warning system based on a driving simulation

The accidents in the freeway are frequent and more serious on foggy days. Connected vehicle (CV) technology as a new technology can inform drivers of fog conditions in advance so the drivers can adjust their driving behaviors through Human Machine Interface (HMI) and Dynamic Message Sign (DMS) in the fog warning system. The level of driver's compliance with a fog warning system is the key to assessing the effectiveness of the fog warning system. To evaluate the compliance level of fog warning system, the study established a CV system testing platform based on driving simulator, and analyzed the changes in driving behavior and influencing factors for drivers under three different visibility conditions (No fog, light fog and heavy fog) based on the platform, finally, comprehensive evaluation of the optimal fog warning system under different foggy conditions based on the compliance level were made. Research indicators are divided into three aspects: 1) the response degree, including the mean speed, the minimum speed in the fog zone, the difference of the speeds when entering and leaving the fog zone, the proportion of speed following, and the speed reduction proportion prompt; 2) the response start time, which is the time to start slowing down to the speed limit; and 3) the response time duration, namely, the time in which the speed limit is followed. The results show that the response degree of the driver to the fog warning system is high, the fog warning system can effectively reduce the driving speed of the drivers and improve the speed following proportion of the drivers, the deceleration ratio of the driver at each warning point is relatively high, and the influencing factors of various indicators are complex but are mostly related to visibility and technical level. Comprehensive evaluation results show that the warning mode of the combined HMI and DMS has the highest level of compliance under light fog conditions, and when fog concentration increases, compliance level of fog warning system with HMI only is higher than others. The study establishes a reference platform for CV system and provides methods and index system for the compliance level of CV research.     

Altering contingencies to facilitate compliance with traffic light systems

The effects of altering light pattern sequences on driver compliance at a busy, urban intersection were explored. The baseline light timing sequences resulted in only 46.8% of drivers stopping at the yellow or red lights. Using an A-B-C design, we altered light pattern sequences that increased the probability of drivers stopping at the signals to 88.8% and 98.8%. These findings indicate that traffic light contingencies have potent effects in influencing driver behaviors at busy intersections. Following completion of the study, the traffic engineer approved the permanence of the light timing pattern that increased traffic rule compliance. Accident data collected before and after the light timing changes indicated a reduction in automobile accidents.     

The effect of a collision warning system on the driving performance of young drivers at intersections

This study investigated the manner in which the driving performance of young people was affected by a collision warning system when they encountered a driver running a red light at an intersection. Furthermore, the causal relationship among driving performance, traffic factors and intersection accidents was examined using Path Analysis. Participants drove a driving simulator with an intersection collision warning system (ICWS) in a simulated urban area. The driving performance measures recorded were reaction time, speed, lateral position deviation and crash events. Experimental results indicated that drivers who drove a vehicle with an ICWS audio signal at an intersection had a shorter reaction time, a lower speed and a reduced accident rate than those observed while driving a vehicle without ICWS audio signal. Furthermore, Path Analysis showed that the ICWS had an indirect effect on accident rate reduction through improved driving performance. The location of intersection accident had both direct and indirect effects on the accident rate. The number of driving days per week had a direct effect on accident rate reduction.     

Developing the Impulsive Driver Behavior Scale

The aim of the present study is to develop a driving context specific impulsivity scale. First, a qualitative study was conducted by interviewing 20 individual drivers to develop the scale items based on the definitions of the basic impulsivity dimensions in the literature. Then, a quantitative study with a total of 506 individual drivers to examine the psychometric qualities of the newly developed Impulsive Driver Behavior Scale. In addition, the variance in driver behaviors, namely violations, errors, lapses and positive driver behaviors measured by the Driver Behavior Questionnaire (DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990) explained by the general impulsivity scales in the literature and the newly developed scale were compared. In all the comparisons, Impulsive Driver Behavior Scale explained higher amount of variance than the general impulsivity scales. Finally, it was found that the newly developed driving context specific impulsivity scale had incremental validity over the general impulsivity scales in predicting driver behaviors.     

Diverging diamond interchanges: A driving simulator study

This study assessed driver performance while navigating a Diverging Diamond Interchange (DDI) compared to a standard intersection in a driving simulator. A total of 201 Western Australian (WA) drivers aged 18–80 years completed the simulator drive and questionnaire. Measures of driving simulator performance assessed included time spent out of lane, number of lane excursions, compliance to the speed limit, crashes and near misses. Other driving measures, which were recorded by the researcher, included driver errors/violations such as red-light violations, wrong way violations and navigation errors. Qualitative information was also obtained in a post exit interview with each participant regarding the difficulties they experienced when driving through the DDI. A repeated-measure analysis of variance (r-ANOVA) was undertaken to examine differences in intersection type (DDI versus standard intersection) and driving performance measures from the driving simulator. The only significant result was compliance to the speed limit (F (1, 656) = 160.11, p < 0.001) on the driving simulator. A higher proportion of red-light violations were observed by the researcher as participants navigated through the DDI, compared to the standard intersection. Qualitative comments from participants also highlighted the need for better signage and road markings. Recommendations when DDIs are implemented include community education on speed limit compliance, avoidance of red-light violations and design improvements regarding signage and road markings.     

Driving demands,stress reactivity and driving behavior: An interactional approach

The aim of the present study was to investigate the role of driving demands, neuroticism, and their interaction when predicting driving behavior. More precisely, we strived to examine how driving behaviors (i.e., speeding, winding, tailgating and jerky driving) unfold across low and high driving demands and whether they are contingent on a personality factor that has previously been linked to stress reactivity. In a driving simulator, 50 participants with a valid driver’s license (56.6% female, age: M = 30.13, SD = 10.16) were exposed to driving scenarios of different levels of information processing and vehicle handling demands. Additionally, they filled-out a self-report questionnaire that measured their neuroticism. We found that driving behavior became safer in scenarios that were highly demanding in terms of information processing, while this pattern did not emerge with vehicle handling demands. Moreover, tentative support was found for the notion that individuals high in neuroticism are less able to adapt their behavior to higher information processing demands. The present study offers new insights on driving demands in a simulated driving context and points to the potential importance of exploring interactions between personality and situational factors when understanding driving behavior. Additionally, the results of the present study may be used to adapt driver’s education programs.     

Exploring associations between self-reported executive functions,impulsive personality traits,driving self-efficacy,and functional abilities in driver behaviour after brain injury

ObjectiveThe assessment of self-awareness and self-efficacy as they relate to driving after stroke and TBI is lacking in the literature where the focus has tended to be on neuropsychological testing of underlying component of cognition in predicting driving outcome. Therefore, this study aims to investigate the associations between self-rating of higher-level functions and post-injury driving behaviour.MethodsThe present one-year follow-up study included twenty-four adults with stroke and ten adults with traumatic brain injury (TBI) deemed suitable for driving after a comprehensive driving evaluation according to Norwegian regulations. In addition, but not part of the decision making, baseline measurements included self-rating of executive functions (Behaviour Rating of Executive Function (BRIEF-A)), impulsive personality traits (UPPS Impulsive Behaviour Scale), driving self-efficacy (Adelaide Driving Self-Efficacy Scale (ADSES)), and functional abilities (Awareness Questionnaire (AQ)). Follow-up measurements twelve months after baseline were collected, the ADSES, AQ, and Swedish Driver Behaviour Questionnaire (Swedish DBQ).ResultsPerceived driving self-efficacy and functional abilities did not change from baseline to follow-up. Baseline perceived executive functions and impulsive personality traits were significantly associated with driving self-efficacy at follow-up. Lower self-efficacy and functional abilities were associated with lower driving mileage and increased use of compensatory driving strategies, whereas lower self-efficacy beliefs were associated with driver mistakes and inattention. Driver violations and inattention were associated with minor accidents.ConclusionThe present study demonstrates that higher-level functions such as executive functions, impulsive personality traits, driving self-efficacy and functional abilities, influence post-injury accident involvement mediated through proximal driving factors such as driver inattention. Further evidence is warranted to explore self-rating measures compared to performance-based methods as predictors of risky driver behaviour, crashes, and near misses.     

A field investigation of red-light-running in Shanghai,China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.