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This study aims to assemble and present the status of the published information available regarding pedestrian safety near crosswalks and bus stops through a comprehensive search of various databases. It focuses on the influence of crossing environment and bus stops, underlining land-use components, crosswalk quality, traffic control, placement of facilities, and other aspects of pedestrian crossing safety. It also offers a detailed discussion of various study designs and statistical modeling techniques implemented in this context over the years, followed by the suggested interventions to improve safety at such high-risk locations. The existing literature elucidates that traffic management strategies and designs have often prioritized the movement of motorized vehicles, resulting in higher risk to pedestrians. This is especially true for low- and middle-income countries. Also, previous research is mostly aligned with offering solutions with limited reference to the reported crash data. Thus, a holistic approach is needed to identify the contributing factors associated with fatal crashes, assess the safety performance at such locations, and thereby offer sustainable planning and design for pedestrian facilities. [ABSTRACT FROM AUTHOR]

The location of curb ramps, crosswalk markings, and curb radius are important contributors to pedestrian safety at intersections, particularly for conflicts and crashes involving right turns. Drivers tend to have higher turning speeds at locations with larger curb radii, which also requires greater stopping sight distance. If the crosswalk is placed further away from the intersection corner (i.e., offset or setback from the intersection corner), it could potentially provide drivers better visibility of pedestrians and increase the available stopping distance. However, no study has previously been conducted to investigate the relationship between crosswalk location and curb radius. This paper presents the results of research that quantifies the relationship between the offset of crosswalks (setback crosswalk) and indirect safety measures (driver stopping, yielding, and speed selection) at intersections with the consideration of other characteristics during simulated driving. The study included field-based observations and driving simulator experiments. A driving simulator experiment with 50 participants was conducted to determine how setback distances, sharpness of curb radii, and presence of pedestrians affect driver stopping decision and position, and speed choice. Approximately 12 h of video (7:00 a.m.-7:00 p.m.) were collected at nine study sites that closely matched the designs tested in the driving simulator. Comparisons of drivers' speed in a similar scenario obtained from field and simulator data were performed to benchmark the observations from the driving simulator against field data. Stop line speeds were found to be consistent between experiments and turning speeds were found to be slightly higher in the driving simulator experiment. The research results suggest that curb radius should be smaller [less than 9 m (30 ft)] to control driver speed. Additionally, a setback distance of 6 m (20 ft) is a suitable upper bound for crosswalk placement when reconstructing intersections. [ABSTRACT FROM AUTHOR]

Pedestrian accidents contribute significantly to the high number of annual traffic casualties. It is therefore crucial for pedestrians to use safety measures, such as a crosswalk, and to activate pedestrian signals. However, people often fail to activate the signal or are unable to do so – those who are visually impaired or have occupied hands may be unable to activate the system. Failure to activate the signal can result in an accident. This paper proposes an improvement to crosswalk safety by designing a system that can detect pedestrians and trigger the pedestrian signal automatically when necessary. In this study, a dataset of images was collected in order to train a Convolutional Neural Network (CNN) to distinguish between pedestrians (including bicycle riders) when crossing the street. The resulting system can capture and evaluate images in real-time, and the result can be used to automatically activate a system such as a pedestrian signal. A threshold system is also implemented that triggers the crosswalk only when the positive predictions pass the threshold. This system was tested by deploying it at three real-world environments and comparing the results with a recorded video of the camera's view. The CNN prediction model is able to correctly predict pedestrian and cyclist intentions with an average accuracy of 84.96% and had an absence trigger rate of 0.037%. The prediction accuracy varies based on the location and whether a cyclist or pedestrian is in front of the camera. Pedestrians crossing the street were correctly predicted more accurately than cyclists crossing the street by up to 11.61%, while passing (i.e., non-crossing) cyclists were correctly ignored more than passing pedestrians, by up to 18.75%. Based on the testing of the system in real-world environments, the authors conclude that it is feasible as a back-up system that can complement existing pedestrian signal buttons, and thereby improve the overall safety of crossing the street. Further improvements to the accuracy can be achieved with a more comprehensive dataset for a specific location where the system is deployed. Implementing different computer vision techniques optimized for tracking objects should also increase the accuracy. [ABSTRACT FROM AUTHOR]

Researchers are studying CNN (convolutional neural networks) in various ways for image classification. Sometimes, they must classify two or more objects in an image into different situations according to their location. We developed a new learning method that colored objects from images and extracted them to distinguish the relationship between objects with different colors. We can apply this method in certain situations, such as pedestrians in a crosswalk. This paper presents a method for learning pedestrian situations on CNN using Mask R-CNN (Region-based CNN) and CDA (Crosswalk Detection Algorithm). With this method, we classified the location of the pedestrians into two situations: safety and danger. We organized the process of preprocessing and learning images into three stages. In Stage 1, we used Mask R-CNN to detect pedestrians. In Stage 2, we detected crosswalks with the CDA and placed colors on detected objects. In Stage 3, we combined crosswalks and pedestrian objects into one image and then, learned the image to CNN. We trained ResNet50 and Xception using images in the proposed method and evaluated the accuracy of the results. When tested experimentally, ResNet50 exhibited 96.7% accuracy and Xception showed 98.7% accuracy. We then created an image that simplified the situation with two colored boxes of crosswalks and pedestrians. We confirmed that the learned CNN with the images of colored boxes could classify the same test images applied in the previous experiment with 96% accuracy by ResNet50. This result indicates that the proposed system is suitable for classifying pedestrian safety and dangerous situations by accurately dividing the positions of the two objects. [ABSTRACT FROM AUTHOR]

The article is devoted to an analysis of accidents involving pedestrians in traffic. An analysis of the statistical data of accidents involving pedestrians in the Republic of Belarus has been made. The main patterns and trends of accidents involving pedestrians are identified. A detailed analysis of the accident rate at pedestrian crossings and intersections was carried out, which made it possible to establish the most dangerous types of collisions during the interaction of transport and pedestrian flows. Experimental studies of collision situations were also carried out, which made it possible to establish the dependence of the violation rate on the composition of the right-handed traffic flow, on the type of traffic signal that regulates traffic on the right, as well as on the size of groups of pedestrians in front of cars. [ABSTRACT FROM AUTHOR]

The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian-vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the crosswalk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections. [ABSTRACT FROM AUTHOR]

Left- 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. [ABSTRACT FROM AUTHOR]

This paper presents the design, analysis and results of a driving simulator experiment conducted to study the interaction between drivers and pedestrians in a mixed-street environment. Ninety-six students of the American University of Beirut (AUB) participated in the experiment that took place in the Transportation and Infrastructure Laboratory of AUB. The study looked at the driver-pedestrian interaction from the driver's perspective, by quantifying the effects of different scenario variables on the driving behavior of the participants. Kruskall-Wallis test shows that drivers’ behavior in proximity of pedestrians tends to be statistically significantly less aggressive when their approach velocity is lower, curb-side parking is not allowed, a crosswalk exists, and the number of pedestrians crossing the street is higher. A discrete choice model for the yielding behavior of the drivers was also developed as a function of different predictor variables. Five out of the six predictors considered (except for gender) had a statistically significant effect on the yielding behavior, particularly the effects of curb-side parking, number of pedestrians crossing, and approach velocity. The model was then used to evaluate the effect of policy variables on the yielding probabilities of the drivers. The results of this study enrich current knowledge and understanding of drivers’ behavior and their interaction with pedestrians, especially with studying the effects of scenario variables that were not addressed before; this would help planners propose and evaluate safety measures and traffic calming techniques to reduce the risks on pedestrians. The study also confirms the effectiveness of driving simulators in studying driver-pedestrian interactions. [ABSTRACT FROM AUTHOR]

Objective . To evaluate if precrash vehicle movement is associated with the severity of pedestrian injury. Methods . We used comprehensive information on pedestrian, vehicle, and injury-related characteristics gathered in the Pedestrian Crash Data Study (PCDS), conducted by the National Highway Traffic Safety Administration (NHTSA) (1994–1998). The odds ratio of severe injuries (injury severity score ≥ 15) and crash fatality rate for right- and left-turn collisions at intersection compared with straight vehicle movement were compared using a logistic regression model and taking into consideration the type of vehicle and age of the pedestrians as potential effect modifiers. Later we evaluated the intermediate effect of impact speed on the association by adding it to the logistic regression model. Results . Of 255 collisions eligible for this analysis, the proportion of pedestrian hit during straight movement, right turns, and left turns were 48%, 32%, and 10%, respectively. Sixty percent of the pedestrians in left-turn crashes and 67% of them in right-turn collisions were hit from their left side. For straight movements the pedestrians were equally likely to be struck beginning from the left or right side of the street. After adjustment for pedestrian's age, vehicle movement was a significant predictor of severe injuries (p < 0.0001) and case fatality (p = 0.003). The association between vehicle precrash movement and severe injuries (p = 0.551) and case fatality (p = 0.912) vanished after adjusting for impact speed. This indicated that the observed association was probably the result of the difference in impact speed and not the precrash movement of the vehicle. Conclusion . Pedestrian safety interventions that aim at environmental modifications, such as crosswalk repositioning, might be the most efficient means in reducing right- or left-turn collisions at intersection, while pedestrians' behavioral modifications should be the priority for alleviating the magnitude of the collisions that happen in vehicles' straight movements. [ABSTRACT FROM AUTHOR]

• Detection-based strategies significantly improved drivers yielding rates. • LED and VMS conditions were helpful in reducing the vehicle-pedestrian conflicts. • Detection-based strategies were effective in terms of drivers' speed reduction. • Do detection-based warning strategies improve vehicle yielding behavior at uncontrolled midblock crosswalks? Pedestrians being the most vulnerable road users account for a large proportion of injuries and fatalities from road traffic crashes. Pedestrians are involved in around one-third of the whole fatalities coming from the road traffic crashes in the state of Qatar. In areas with uncontrolled midblock crosswalks, it is very crucial to improve drivers' alertness and yielding behavior. The objective of this driving simulator study is to investigate the impact of pedestrian detection strategies and pavement markings on driving behavior at high-speed uncontrolled crosswalks. To this end, an untreated condition (i.e. Control) was compared with three treatment conditions. The three treated conditions included two detection strategies, i.e., advance variable message sign (VMS) and LED lights, and road markings with pedestrian encircled. Each condition was tested with a yield/stop controlled marked crosswalk for two situations, i.e. with vs. without a pedestrian present. The experiment was conducted using the driving simulator at Qatar University. In total, 67 volunteers possessing a valid Qatari driving license participated in the study. Different analyses were conducted on vehicle-pedestrian interactions, driving speed, variations in acceleration/deceleration and lateral position. The results showed that both the LED and VMS conditions were helpful in increasing yielding rates up to 98.4 % and reducing the vehicle-pedestrian conflicts significantly. Furthermore, both treatments were effective in motivating drivers to reduce vehicle speed in advance. Considering the findings of this study, we recommend LED and VMS conditions as potentially effective solutions to improve safety at yield/stop controlled crosswalks. [ABSTRACT FROM AUTHOR]