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.     

Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.     

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

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

Interpersonal perceptions of personality traits in elite coach-athlete dyads

IntroductionThe extent to which coaches and athletes can effectively work together is an essential consideration in the pursuit of athletic success. This is particularly important at the elite level due to the high pressures on tangible outcomes, such as reaching the podium of a major competition. This study sought to explore and explain how both coaches and athletes identify personality traits in themselves and their partners to manage and maintain a positive relationship.MethodsUsing a mixed methodological design underpinned by critical realism, four elite coach-athlete dyads (four male coaches, one male athlete, three female athletes) were purposefully recruited from a single sport. Each participant completed the 44-item Big Five Inventory (John & Srivastava, 1999) on their own and their partner’s perceived personality traits. The data generated were used to inform the discussions in follow-up, individual semi-structured interviews with all participants.ResultsThe interview data were analysed using thematic analysis, which generated three higher themes and seven lower order themes. The three higher order themes were perceived compatibility, relationship persona and collective personality.ConclusionThe present investigation has identified what coaches and athletes perceive to be the key personality characteristics to manage and maintain a successful working relationship.     

Speed reductions and judgments of travel time loss: Biases and debiasing

Priority decisions concerning maintenance or reconstruction of roads are made with the aim of road improvements with as little traffic disturbance and time loss as possible. However, it cannot be avoided that speed will be reduced and travel time increased during the time of construction. The present study shows how intuitive judgments of travel time losses are biased in a way similar to the times saving bias (Svenson, 2008), but not perfectly corresponding to that bias. This means that when speed is decreased from a slow speed <50 km/h, the time loss is underestimated and when speed is decreased from a high speed >80 km/h it is overestimated. Also, drivers, politicians and policy makers who do not make exact calculations are likely victims of the time loss bias. The time loss bias was weakened but not eliminated by a debiasing instruction including mathematical computations of travel times. When driving speed restrictions are implemented, in particular on fast motorways, it is necessary to consider and counteract the time loss bias and inform the public. This can be done, for example, in communications about travel time facts, by information in driver training and by mounting temporary road signs informing about the average travel time prolongation due to a road work.     

Beyond “accident-proneness”: Using Five-Factor Model prototypes to predict driving behavior

Research on the association between personality traits and driving behavior as well as accident involvement has produced mixed results. In order to obtain empirical evidence for a consistent relation between personality and driving behavior, a prototype approach based on the Five-Factor Model (FFM) was applied. In two samples of drivers, cluster analyses of FFM scales revealed three prototypes that replicate previous ones. The prototypes, labeled Resilient, Overcontrolled, and Undercontrolled, were found to differ reliably in accident involvement and driving behavior, indicating differential accident liability. Undercontrollers are the most problematic drivers followed by Resilients, whereas Overcontrollers most consistently obey traffic rules and drive accordingly.     

Does graduation give rise to increased poignancy? Moderation roles of university identity and emotion regulation

Cross-sectional (Study 1) and longitudinal (Study 2) designs were used to examine the relationship between graduation and poignancy (i.e. feeling both happy and sad at the same time). Participants included college students who completed a short emotion checklist, and measures of university identity and emotion regulation strategies. Students who were about to graduate, experienced a higher level of poignancy than those who were further away from graduation. Yet, university identity and emotion regulation moderated this relationship. Students with a higher level of university identity and those with a lower level of emotional suppression experienced a higher level of poignancy when approaching graduation. These findings not only provide support for the postulate of socioemotional selectivity theory that anticipated endings increase poignancy, but also qualify it in terms of the moderators.     

Boredom: Practical consequences and a theory

Boredom is defined as a unique psychophysiological state possessing interrelated and inseparable emotional, motivational, perceptual and cognitive concomitants. Practical consequences of boredom are reviewed, including diminished performance efficiency, general life satisfaction and health. Finally, the outline of a theoretical model is presented.     

Does traffic congestion increase driver aggression?

In his recent article about aggressive driving, David Shinar proposed that the classical frustration-aggression hypothesis (Dollard, J., Doob, L., Miller, N., Mowrer, O. & Sears, R. (1939). Frustration and aggression. New Haven, CN: Yale University Press) provides a useful tool for understanding driver aggression (Shinar, D. (1998). Aggressive driving: the contribution of the drivers and situation. Transportation Research Part F, 1, 137–160). According to Shinar's (1998) application of the frustration–aggression hypothesis, driver aggression is caused by frustration because of traffic congestion and delays. In the present study, the relationships between exposure to congestion (rush-hour driving) and aggressive violations (DBQ) were investigated in Great Britain, Finland and the Netherlands. Partial correlations showed that the frequency of rush-hour driving did not correlate statistically significantly with driver aggression. Correlations between driving during rush-hour and aggression did not differ in magnitude from those between driving on country roads and aggressive violations. In addition, correlations between exposure to congestion and aggressive violations in countries with large number of vehicles per road kilometre (UK, Netherlands) were not higher than those in a sparsely populated country (Finland). These results from three countries suggest that congestion does not increase driver aggression as directly as suggested by Shinar (1998).     

Evaluating the impact of adaptive signal control technology on driver stress and behavior using real-world experimental data

While the operational and crash reduction benefits of adaptive signal control technology (ASCT) have long been investigated, the impact of this technology on driver behavior and stress is still uncertain. This study evaluated the impact of ASCT on driver behavior and stress in a real-world environment. Participants travelled through two arterial corridors, one equipped with ASCT and the other having traditional time-of-day coordinated signals. Driver stress was measured using a heart rate detector and a perceived stress scale while driver behavior was examined using vehicular trajectory data. Overall, driving behavior improved on the ASCT as compared to the non-ASCT corridor, as indicated by higher speeds and a fewer number of stops on the ASCT corridor relative to the non-ASCT corridor. Repeated measures ANOVA showed a statistically significant reduction in driver heart rate by −10 beats per minute over the ASCT corridor. A similar trend was observed for drivers’ perceived stress, analyzed by Wilcoxon sign ranked test. Driving behavior also showed significant improvement with ASCT presence, and speed was found to be negatively correlated with stress. Furthermore, the participants’ speed was controlled by the two systems i.e. ASCT and non-ASCT as expected. This study provides a significant proof of concept that ASCT can create positive improvements in driver stress and behavior that can be further investigated in the future.