Evaluating the traffic safety performance of left-turn waiting areas at signalized intersections

Left-turn waiting area (LWA) is an innovative traffic design that is popularly applied to improve the traffic capacity of signalized intersections in China. The traffic safety impacts of the LWA, however, have not been fully discussed in previous studies. Thus, the study aims to evaluate the safety performance of the LWA by means of the traffic conflict technique. A field investigation was conducted to collect the post-encroachment time (PET) of conflicts and relevant variables at the signalized intersections in Jinhua, China. The Chi-square and two sample t-tests were adopted to examine the difference in conflict distribution between the intersections with and without LWA. The random parameter ordered logit model was employed to identify the factors contributing to the risks of vehicular collisions. Results indicate that (1) intersections with LWA are generally associated with more merging conflicts; (2) there are no significant discrepancies in the PET values between intersections with and without LWA; and (3) factors such as the number of left-turn lanes, number of receiving lanes, conflict type, vehicle type, driving direction, stopping outside LWA and overtaking behavior are identified to significantly impact the traffic conflicts. The findings serve to develop the countermeasures to ensure the safe operation of LWA.

Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning

Driving behavior is considered as a unique driving habit of each driver and has a significant impact on road safety. This study proposed a novel data-driven Machine Learning framework that can classify driving behavior at signalized intersections considering two different signal conditions. To the best of our knowledge, this is the first study that investigates driving behavior at signalized intersections with two different conditions that are mostly used in practice, i.e., the control setting with the signal order of green-yellow-red and a flashing green setting with the signal order of green-flashing green-yellow-red. A driving simulator dataset collected from participants at Qatar University's Qatar Transportation and Traffic Safety Center, driving through multiple signalized intersections, was used. The proposed framework extracts volatility measures from vehicle kinematic parameters including longitudinal speed and acceleration. K-means clustering algorithm with elbow method was used as an unsupervised machine learning to cluster driving behavior into three classes (i.e., conservative, normal, and aggressive) and investigate the impact of signal conditions. The framework confirmed that in general driving behavior at a signalized intersection reflects drivers' habits and personality rather than the signal condition, still, it manifests the intersection nature that usually requires drivers to be more vigilant and cautious. Nonetheless, the results suggested that flashing green condition could make drivers more conservative, which could be due to the limited capabilities of human to estimate the remaining distance and the prolonged duration of the additional flashing green interval. The proposed framework and findings of the study were promising that can be used for clustering drivers into different styles for different conditions and might be beneficial for policymakers, researchers, and engineers.

School Surrounding Region Traffic Commuting Analysis Based on Simulation

Student commuting is an important part of urban travel demand and private car commuting plays an important role in urban traffic, especially in areas near schools. Since parents, especially the parents of elementary and junior high school students, prefer to drive rather than take public transport, there will be a negative effect on traffic management. To address the challenge, a simulation model is established based on schools' surrounding regions to analyze traffic status. Specifically, the model focuses on urban construction and transportation near the entrance of schools and neighborhoods. In addition, four variable parameters consisting of the directional hourly volume, the parking demand of delivery vehicles, the distance between the school and intersection, and the average parking time for pick-up vehicles are set as influence factors, while traffic efficiency, energy consumption, and pollutant emissions are considered as the evaluation criteria of our model. Extensive simulated experiments show that comparing different scenarios, the traffic state of schools' surrounding areas can achieve much better performance when the distance between entrances and intersections is 400 m under the 1000 pcu/h condition. This research can provide a scientific basis for school regional traffic management and organization optimization.

Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.

A random parameter negative binomial model for signalized intersection accidents in Seoul, Korea

A variety of statistical models were generally considered to better understand the relationship between crash occurrences and diverse factors. However, most of statistical models adapted fixed parameters which cannot incorporate time variation or sement-specific effects. To relieve this problem, this study focuses on a traffic accident frequency model using a random parameter negative binomial approach. This method allows for the consideration of unobserved heterogeneity in accident data that current popular methods such as Poisson or Negative Binomial models cannot account for. A four-year (2007-2010) continuous panel of accident histories at 95 signalized intersections in Seoul, Korea, was used to estimate the random parameter negative binomial model with traffic volumes and various geometric characteristics at intersections. Results show that the presence of a left-turn exclusive lane on a major road, the existence and length of a median barrier, and the existence of a pedestrian island on a major road are random parameters, and an additional ten variables significantly affected the safety at the intersections as fixed parameters. The fixed parameters were associated with major and minor roadway heavy vehicle volume, exclusive turn lane presence on major and minor roadway, taxiway lane presence, median barrier presence, as well as the number of lanes on major and minor roadway. The insights from this study indicate the need for broader analysis of lane channelization, lane exclusion and lane geometry effects as potential random parameters in intersection accident propensities.

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NO_x), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM_2.5) concentration below the apartment building height. Atmospheric NO_x dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NO_x concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

Investigation of traffic and safety behavior of pedestrians while texting or web-surfing

Objective: More and more pedestrians use mobile phones in their daily traffic activities by the roadside or even when crossing the street. The objective of this research is to examine pedestrians' traffic and safety behavior while texting or web-surfing, when crossing signalized intersections.Methods: In order to compare the behavior of distracted and non-distracted pedestrians, an experimental process through video recording was carried out in real road conditions, in three signalized intersections in the center of Athens in Greece. Demographic and behavioral characteristics were observed, including use of mobile device. For the statistical analysis, two multiple linear regression models were developed to investigate the association of pedestrians' speed and distraction caused by mobile phone use. Additionally, binary logistic regression models were developed in order to determine the influence of distraction on pedestrians' safety characteristics and more specifically on near misses with oncoming vehicles.Results: Observers recorded crossing behaviors for 2,280 pedestrians and noticed that nearly one-fifth (16.6%) of them performed a phone-distracting activity while crossing. Distractions included texting or web-surfing (6.3%), listening to music (5.4%) and using a handheld phone (4.9%). Τhis research indicated that distraction caused by texting or web-surfing had a negative impact on pedestrians' main traffic and safety characteristics. Results pointed out that in high pedestrian traffic, distracted pedestrians who were texting or web-surfing on their mobile phone present lower speed than non-distracted pedestrians, regardless of their age, as they may be not aware of traffic conditions due to distraction and therefore, they have higher crossing times. Furthermore, their probability of a near miss increases with increasing pedestrian volume as the more pedestrians who occupy the pedestrian crossing the more difficult is for them to observe carefully the rest traffic.Conclusions: Mobile phones are integral to contemporary daily life and their use and penetration is increasing rapidly as well. For this reason, it is crucial to investigate the impacts of distracted walking on pedestrians' traffic and safety behavior. Various measures and strategies should be implemented and further research should be conducted as texting and web-surfing distraction is associated with a rather high risk.

Analysis of Emission Effects Related to Drivers' Compliance Rates for Cooperative Vehicle-Infrastructure System at Signalized Intersections

Unknown remaining time of signal phase at a signalized intersection generally results in extra accelerations and decelerations that increase variations of operating conditions and thus emissions. A cooperative vehicle-infrastructure system can reduce unnecessary speed changes by establishing communications between vehicles and the signal infrastructure. However, the environmental benefits largely depend on drivers’ compliance behaviors. To quantify the effects of drivers’ compliance rates on emissions, this study applied VISSIM 5.20 (Planung Transport Verkehr AG, Karlsruhe, Germany) to develop a simulation model for a signalized intersection, in which light duty vehicles were equipped with a cooperative vehicle-infrastructure system. A vehicle-specific power (VSP)-based model was used to estimate emissions. Based on simulation data, the effects of different compliance rates on VSP distributions, emission factors, and total emissions were analyzed. The results show the higher compliance rate decreases the proportion of VSP bin = 0, which means that the frequencies of braking and idling were lower and light duty vehicles ran more smoothly at the intersection if more light duty vehicles complied with the cooperative vehicle-infrastructure system, and emission factors for light duty vehicles decreased significantly as the compliance rate increased. The case study shows higher total emission reductions were observed with higher compliance rate for all of CO₂, NO_x, HC, and CO emissions. CO₂ was reduced most significantly, decreased by 16% and 22% with compliance rates of 0.3 and 0.7, respectively.

Predicting Driver Behavior during the Yellow Interval Using Video Surveillance

At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers' stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS). An enhanced Gaussian Mixture Model (GMM) is used to extract moving vehicles from target lanes, and the Kalman Filter (KF) algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers' stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers' stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers' RLR violations and improve traffic safety at signalized intersections.

Effects of digital countdown timer on intersection safety and efficiency: A systematic review

OBJECTIVE

To investigate the available evidence referring to the effectiveness of digital countdown timers (DCTs) in improving the safety and operational efficiency of signalized intersection.

METHODS

A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guidelines. Relevant literature was searched from electronic databases using key terms. Based on study selection and methodological quality assessment, 14 studies were included in the review. Findings of the studies were synthesized in a narrative analysis.

RESULTS

Three types of DCT had different effects on intersection safety and operational efficiency. Green signal countdown timers (GSCTs) reduced red light violations, type I dilemma zone distributions, and rear-end collision likelihood but increased crossing after yellow onset and had mixed impacts on type II dilemma zone distributions and intersection capacity. In contrast, red signal countdown timers (RSCTs) increased intersection capacity, although their effectiveness in reducing red light violations dissipated over time. Likewise, continuous countdown timers (CCTs) significantly enhanced intersection capacity but had mixed influences on red light violations and crossing after yellow onset.

CONCLUSIONS

Due to the limited and inconsistent evidence regarding DCTs' effects on intersection safety and efficiency, it is not sufficient to recommend any type of DCT to be installed at signalized intersections to improve safety and operational efficiency. Nevertheless, it is apparent that both RSCTs and CCTs enhance intersection capacity, though their impacts on intersection safety are unclear. Future studies need to further verify those anticipated safe and operational benefits of DCTs with enriched field observation data.

Comparative analysis of risky behaviors of electric bicycles at signalized intersections

OBJECTIVE

The primary objective of this study was to compare the risky behaviors of e-bike, e-scooter, and bicycle riders as they were crossing signalized intersections.

METHODS

Pearson's chi-square test was used to identify whether there were significant differences in the risky behaviors among e-bike, e-scooter, and bicycle riders. Binary logit models were developed to evaluate how various variables affected the behaviors of 2-wheeled vehicle riders at signalized intersections. Field data collection was conducted at 13 signalized intersections in 2 cities (Nanjing and Kunming) in China.

RESULTS

Three different types of risky behaviors were identified, including stop beyond the stop line, riding in motorized lanes, and riding against traffic. Two-wheeled vehicle riders' gender and age and traffic conditions were significantly associated with the behaviors of 2-wheeled vehicle riders at the selected signalized intersections.

CONCLUSIONS

Compared to e-bike and bicycle riders, e-scooter riders are more likely to take risky behaviors. More specifically, they are more likely to ride in motorized lanes and ride against traffic.

Safety impacts of red light cameras at signalized intersections based on cellular automata models

OBJECTIVE

This study applies a simulation technique to evaluate the hypothesis that red light cameras (RLCs) exert important effects on accident risks. Conflict occurrences are generated by simulation and compared at intersections with and without RLCs to assess the impact of RLCs on several conflict types under various traffic conditions.

METHOD

Conflict occurrences are generated through simulating vehicular interactions based on an improved cellular automata (CA) model. The CA model is calibrated and validated against field observations at approaches with and without RLCs. Simulation experiments are conducted for RLC and non-RLC intersections with different geometric layouts and traffic demands to generate conflict occurrences that are analyzed to evaluate the hypothesis that RLCs exert important effects on road safety.

RESULTS

The comparison of simulated conflict occurrences show favorable safety impacts of RLCs on crossing conflicts and unfavorable impacts for rear-end conflicts during red/amber phases. Corroborative results are found from broad analysis of accident occurrence.

CONCLUSIONS

RLCs are found to have a mixed effect on accident risk at signalized intersections: crossing collisions are reduced, whereas rear-end collisions may increase. The specially developed CA model is found to be a feasible safety assessment tool.

Effect of audio in-vehicle red light-running warning message on driving behavior based on a driving simulator experiment

OBJECTIVE

Drivers' incorrect decisions of crossing signalized intersections at the onset of the yellow change may lead to red light running (RLR), and RLR crashes result in substantial numbers of severe injuries and property damage. In recent years, some Intelligent Transport System (ITS) concepts have focused on reducing RLR by alerting drivers that they are about to violate the signal. The objective of this study is to conduct an experimental investigation on the effectiveness of the red light violation warning system using a voice message.

METHODS

In this study, the prototype concept of the RLR audio warning system was modeled and tested in a high-fidelity driving simulator. According to the concept, when a vehicle is approaching an intersection at the onset of yellow and the time to the intersection is longer than the yellow interval, the in-vehicle warning system can activate the following audio message "The red light is impending. Please decelerate!" The intent of the warning design is to encourage drivers who cannot clear an intersection during the yellow change interval to stop at the intersection.

RESULTS

The experimental results showed that the warning message could decrease red light running violations by 84.3 percent. Based on the logistic regression analyses, drivers without a warning were about 86 times more likely to make go decisions at the onset of yellow and about 15 times more likely to run red lights than those with a warning. Additionally, it was found that the audio warning message could significantly reduce RLR severity because the RLR drivers' red-entry times without a warning were longer than those with a warning.

CONCLUSIONS

This driving simulator study showed a promising effect of the audio in-vehicle warning message on reducing RLR violations and crashes. It is worthwhile to further develop the proposed technology in field applications.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models

OBJECTIVE

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models.

METHODS

Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared.

RESULTS

The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions.

CONCLUSIONS

The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.