Effect of a community-based pedestrian injury prevention program on driver yielding behavior at marked crosswalks

How yielding cameras affect consecutive pedestrian-vehicle conflicts at non-signalized crosswalks? A mixed bivariate generalized ordered approach

Yielding cameras are considered to be an effective means of preventing drivers' non-yielding behavior. Notably, as pedestrians' perceived risk and behavior change dynamically during the crossing, the safety effectiveness of such facility could also vary across the consecutive conflicts. This study contributes to the literature by assessing the safety effectiveness of yielding camera from a novel perspective, focusing on the consecutive pedestrian-vehicle conflicts (primary conflict and secondary conflict), using Unmanned Aerial Vehicle (UAV) and roadside camera data. Another key contribution lies in the consideration of primary conflict related factors in the secondary conflict analysis, providing new insights into conflict analysis. The mixed bivariate generalized ordered probit model is proposed to analyze the consecutive conflicts simultaneously. The model results indicate that the yielding camera could decrease both slight and severe conflict probability in primary conflict. However, in secondary conflict, the yielding camera would lower severe conflict probability but increase slight conflict probability. Moreover, several primary conflict related factors reveal significant effects on the secondary conflict severity. Specifically, higher pedestrian speed and driver's yielding behavior in primary conflict could lead to higher crossing risks in the secondary conflict. Conversely, more unsuccessful attempts before primary conflict could decrease the severity level of secondary conflict. Based on the results, several practical implications are provided to improve the effectiveness of yielding camera and enhance pedestrian safety.

Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations

Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the "pedestrian priority" policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.

Safety effects of law enforcement cameras at non-signalized crosswalks: A case study in China

Pedestrians are vulnerable when crossing the street, especially at non-signalized crosswalks. In China, in spite of the priority that laws entitle the pedestrians, the yielding rates at non-signalized crosswalks are relatively low. In light of this situation, law enforcement cameras have been used to increase the percentage of drivers yielding to pedestrians. This study investigates the effectiveness of law enforcement cameras on drivers yielding behavior and vehicle-pedestrian conflicts at non-signalized crosswalks. Using Unmanned Aerial Vehicle (UAV) and roadside video recording, information including pedestrian characteristics, vehicular characteristics and environmental factors are collected. The conflict indicators used include Post-Encroachment Time (PET), Time to Collision (TTC), and Deceleration to Safety Time (DST). In this study, a conflict classification framework based on PET, TTC and DST using Support Vector Machine algorithm is employed. A multinomial logit regression model is used to identify the factors contributing to the conflicts. Then, binary logit regression models are constructed to analyze the effects of law enforcement cameras on drivers yielding behavior. Conflict study reveals that the implementation of law enforcement cameras would increase the probability of slight conflict but decrease the probability of serious conflict. Yielding behavior analysis shows that the illegitimate yielding behavior percentages are over 10 %, indicating the necessity of improving the awareness of yielding rules, and the implementation of law enforcement cameras would increase the yielding and legitimate yielding probability. Moreover, factors including the adjacent vehicle yielding behavior, number of lanes between pedestrian and vehicle, pedestrian speed change, pedestrian waiting time, pedestrian accepted gap time, vehicle upstream speed and vehicle speed change are significantly associated with conflict severity and drivers yielding behavior. We recommend that supplementary facilities and measures should be used to improve the safety performance of law enforcement cameras.

Road Users’ Behavior at Marked Crosswalks on Channelized Right-Turn Lanes at Intersections in the State of Qatar

At non-signalized marked crosswalks, pedestrian priority is neither well-defined nor well acknowledged by drivers. This paper presents the findings of an investigation on both driver and pedestrian behavior at non-signalized marked crosswalks located on channelized right-turn lanes at intersections in the State of Qatar. Five crosswalks in Doha city were video recorded from discrete locations on a typical working day. The results from the data analysis of 1620 pedestrians’ behavior indicated that waiting behavior, gap acceptance, and crossing speed are complex phenomena and depend upon both pedestrians’ characteristics as well as their crossing characteristics. The drivers’ yielding behavior was mainly linked to pedestrians’ gender and adjacent land use. Low driver yielding rates indicated that significant improvements are required to enhance pedestrian safety. Among pedestrian attributes, gender had the most significant effect on crossing behavior followed by distractions, crossing in a group or alone, and dressing style. Findings of this research will be useful for planners when designing crosswalks at new intersections and during simulations of pedestrian and driver behavior at marked crosswalks on exclusive right-turn lanes. The results of this study will also be directly applicable to the Arabian Gulf countries as they exhibit similar conditions as the State of Qatar.

Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models

Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers' risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention.