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.     

Decisions and actions of distracted drivers at the onset of yellow lights

Driving on an approach to a signalized intersection while distracted is relatively risky, as potential vehicular conflicts and resulting angle collisions tend to be relatively more severe compared to other locations. Given the prevalence and importance of this particular scenario, the objective of this study was to examine the decisions and actions of distracted drivers during the onset of yellow lights. Driving simulator data were obtained from a sample of 69 drivers under baseline and handheld cell phone conditions at the University of Iowa – National Advanced Driving Simulator. Explanatory variables included age, gender, cell phone use, distance to stop-line, and speed. Although there is extensive research on drivers’ responses to yellow traffic signals, the examinations have been conducted from a traditional regression-based approach, which do not necessary provide the underlying relations and patterns among the sampled data. In this paper, we exploit the benefits of both classical statistical inference and data mining techniques to identify the a priori relationships among main effects, non-linearities, and interaction effects. Results suggest that the probability of yellow light running increases with the increase in driving speed at the onset of yellow. Both young (18–25 years) and middle-aged (30–45 years) drivers reveal reduced propensity for yellow light running whilst distracted across the entire speed range, exhibiting possible risk compensation during this critical driving situation. The propensity for yellow light running for both distracted male and female older (50–60 years) drivers is significantly higher. Driver experience captured by age interacts with distraction, resulting in their combined effect having slower physiological response and being distracted particularly risky.     

A hazard-based duration model for analyzing crossing behavior of cyclists and electric bike riders at signalized intersections

This paper presents a hazard-based duration approach to investigate riders’ waiting times, violation hazards, associated risk factors, and their differences between cyclists and electric bike riders at signalized intersections. A total of 2322 two-wheeled riders approaching the intersections during red light periods were observed in Beijing, China. The data were classified into censored and uncensored data to distinguish between safe crossing and red-light running behavior. The results indicated that the red-light crossing behavior of most riders was dependent on waiting time. They were inclined to terminate waiting behavior and run against the traffic light with the increase of waiting duration. Over half of the observed riders cannot endure 49 s or longer. 25% of the riders can endure 97 s or longer. Rider type, gender, waiting position, conformity tendency and crossing traffic volume were identified to have significant effects on riders’ waiting times and violation hazards. Electric bike riders were found to be more sensitive to the external risk factors such as other riders’ crossing behavior and crossing traffic volume than cyclists. Moreover, unobserved heterogeneity was examined in the proposed models. The finding of this paper can explain when and why cyclists and electric bike riders run against the red light at intersections. The results of this paper are useful for traffic design and management agencies to implement strategies to enhance the safety of riders.     

How do drivers behave during indecision zone maneuvers?

Yellow signal indications at intersections are well-known to be a contributor to traffic crashes. This study examined drivers’ behavior during yellow signal indication (i.e., indecision zone) maneuvers. Data from a driving simulator study was used, which included drivers’ performance data when they encountered a yellow signal indication at intersections under different secondary-task conditions. This study calculated drivers’ likelihood to go through a yellow signal indication and examined factors that are related to drivers’ decision making on intersection traversing. The results showed that drivers’ decision on stopping or not at a yellow signal indication was associated with different variables including age, distraction, pedal conditions, and time to stop line. Distracted drivers’ insensitive behavior was also captured from the significant interaction effect between time to stop line and distraction conditions, which implied that intersection related crash risk may increase when drivers were distracted.     

Simulator evaluation of drivers' speed, deceleration and lateral position at rural intersections in relation to different perceptual cues

Aim of the study was to investigate, by means of a driving simulator experiment, drivers’ behaviour in terms of speed, deceleration, and lateral position on major approaches of rural intersections in relation to different perceptual cues.In the experiment, ten different design conditions with and without speed-reducing treatments along the approach to the intersection were tested. Twenty-three drivers drove a test route two times and data from the second drive were used for comparison. The order of the ten design conditions was counterbalanced for all the drivers to minimize the presentation order effect. Three different data analysis techniques were used: (a) cluster analysis of speed and lateral position data, (b) statistical tests of speed and lateral position data, and (c) categorical analysis of deceleration behaviour patterns.The most effective treatments were the dragon teeth markings (based on the principle of optical road narrowing), the colored intersection area (based on the principle of intersection highlighting), and the raised median island (based on the principle of physical road narrowing). These measures, in comparison to the base intersection, produced: (1) a significant speed reduction starting from 250 m before the intersection in the range between 13 and 23 km/h, (2) a significant change in the deceleration behaviour with a reduction in the proportion of drivers which did not decelerate, and (3) a shift away from the intersection of the deceleration beginning. Given the significant effects on drivers’ behaviour, the dragon teeth markings, the colored intersection area, and the raised median island are strongly recommended for real world implementation.     

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.     

Interaction between socio-demographic characteristics: Traffic rule violations and traffic crash history for young drivers

Young drivers’ high traffic violation involvement rate and significant contribution to traffic crashes compared to older drivers creates the need for detailed analyses of factors affecting young drivers’ behaviors. This study is based on survey data collected from 2,057 18–29 year old young adults. Data were collected via face-to-face questionnaire surveys in four different cities in Turkey. The main objective of this study is to identify the relationship between socio-demographic characteristics, traffic rule violations, and traffic crashes among young drivers. Four main traffic rule violations are examined: red light violations, seat belt violations, speeding, and driving under the influence of alcohol, which are decisive in determining driving behavior and traffic crashes. The survey investigates the socio-demographic characteristics, traffic rule violation behavior and traffic crash histories of young adults. Four hypothetical scenarios were prepared for each traffic rule violation and data from the scenarios were modeled using the ordered probit model. Significant variables affecting each traffic rule violation are stated. Finally, significant variables that interact with crash involvements were investigated with binary logit models. According to the data analysis, 23.9% of drivers stated that they were involved in at least one traffic crash within the last three years. This crash rate increases to 38.3% for those who received at least one traffic citation/violation in last three years and peaks to 47.4% for those who were fined for seat belt violations in last three years.     

Novel application of red-light runner proneness theory within traffic microsimulation to an actual signal junction

Building on previous research a conceptual framework, based on potential conflicts analysis, has provided a quantitative evaluation of ‘proneness’ to red-light running behaviour at urban signalised intersections of different geometric, flow and driver characteristics. The results provided evidence that commonly used violation rates could cause inappropriate evaluation of the extent of the red-light running phenomenon. Initially, an in-depth investigation of the functional form of the mathematical relationship between the potential and actual red-light runners was carried out. The application of the conceptual framework was tested on a signalised intersection in order to quantify the proneness to red-light running. For the particular junction studied proneness for daytime was found to be 0.17 north and 0.16 south for opposing main road approaches and 0.42 east and 0.59 west for the secondary approaches. Further investigations were carried out using a traffic microsimulation model, to explore those geometric features and traffic volumes (arrival patterns at the stop-line) that significantly affect red-light running. In this way the prediction capability of the proposed potential conflict model was improved. A degree of consistency in the measured and simulated red-light running was observed and the conceptual framework was tested through a sensitivity analysis applied to different stop-line positions and traffic volume variations. The microsimulation, although at its early stages of development, has shown promise in its ability to model unintentional red light running behaviour and following further work through application to other junctions, potentially provides a tool for evaluation of alternative junction designs on proneness. In brief, this paper proposes and applies a novel approach to model red-light running using a microsimulation and demonstrates consistency with the observed and theoretical results.     

Evaluating time-reminder strategies before amber: Common signal,green flashing and green countdown

The safety level of signalized intersection depends greatly on drivers’ decision-making behaviors, which are significantly influenced by the time-reminder strategy before amber of the signal device. However, previous related studies are mainly based on the statistical results from the field data rather than explore the influence mechanism of the signal device on the signalized intersection's safety level. Therefore, this study aims to find out how these three typical signal devices with various time-reminder strategies, i.e., common signal device (CSD), green signal flashing device (GSFD), and green signal countdown device (GSCD), affect drivers’ decision-making processes during the period from the end of the green phase to the onset of the red phase (i.e., G2R) and then evaluate their safety performance from the aspect of RLR violations. Firstly, an overall decision-making framework during G2R is presented to describe the driver–signal interaction and encloses four decision-making processes, which can be analyzed and modeled based on the field data collected from six signalized intersections in Changsha, China. Empirical analyses show that the time point of decision-making before amber under GSCD is the earliest and that under CSD is the latest, which can also be modeled and reproduced by back propagation neural network (BPNN). After that, five binary logistic regression models are developed to determine the safety effect during other various processes and results show that red-light-running (RLR) violations are not only dependent on the range of dilemma zones (DZ) but also substantially on stop and go decisions of those vehicles in DZ, both of which are the potential cause and direct factors to RLR violations and found to be significantly affected by the time-reminder strategy of the green signal device. Finally, although GSCD stimulates the drivers in DZ to choose to cross the intersection during amber, which produces a higher RLR risk compared with CSD and GSFD, the intersection with GSCD is verified to own the lowest RLR violations due to its greatly positive effect in cutting down the range of DZ.     

Driver behaviour, dilemma zone and safety effects at urban signalized intersections in Greece

Driving behaviour at signalized intersections is one of the main factors contributing to the safety level of such entities. This behaviour, with respect to yellow signal obedience or violation, is examined at a signalized intersection in Thessaloniki, Greece. The data, collected for the study purposes, include vehicles' speeds and distance from stop line when exposed to yellow light, gender and age group of drivers as well as reaction of Platoon leaders and first followers. Drivers were grouped into three categories, namely conservative, normal and aggressive, according to their behaviour at the intersection, the existence of a dilemma or option zone and their initial approaching speed. A binary choice model was also developed relating the probability of stopping at the STOP line or crossing it as a function of approach speed, distance from intersection, gender, age group and the existence or not of a dilemma zone. The findings of this research indicate that a large percentage of drivers facing the yellow signal are caught in a dilemma zone due to high approaching speeds and exercise an aggressive behaviour. The distribution of drivers into the three categories changes with differing assumptions pertaining to the factors affecting the calculation of safe stopping distance and critical crossing distance as well as speed thresholds for determining a priori aggressive behaviour. The research concludes that aggressive drivers represent a high percentage of all drivers and measures towards improving driving behaviour and/or reducing vehicles' speeds are required.