Surrogate safety indicator for unsignalised pedestrian crossings

Olszewski, Piotr; Dąbkowski, Paweł; Szagała, Piotr; Czajewski, Witold; Buttler, Ilona

doi:10.1016/j.trf.2020.02.011

Cited by 21 publications

(6 citation statements)

References 18 publications

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“…The research employed both quantitative and qualitative methods to analyze the compliance of pedestrians while crossing at un-signalized sampled selected sites (Nkurunziza et al, 2021a;Nkurunziza and Tafahomi, 2020;Yang et al, 2022;Sun et al, 2022;Basile et al, 2010) within the City of Kigali. Sampling selected sites to analyze crossing were chosen to be characterized by lane two-way roads; this is based on the finding that two lanes involve more risk than crossing one lane, given similar traffic flow (Olszewski et al, 2020). Road crossings through two or more lanes in one direction are dangerous due to very limited sight distance and high vehicle speeds (Szagala et al, 2022).…”

Section: Methodology Aim and Sampling Techniquesmentioning

confidence: 99%

Pedestrian crossing: Analysis of habits and compliance through unsignalized crosswalk in the city of Kigali

Nkurunziza,

Kumaran,

Tafahomi

et al. 2024

Int. j. adv. appl. sci.

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Pedestrian safety is a global issue that can be improved by monitoring and adjusting walking behaviors. This study focuses on how people walk across streets without traffic signals. It looks at whether pedestrians follow the rules and regulations for safely crossing streets. The unique aspect of this research is that it examines pedestrian behaviors on roads with two lanes for two-way traffic at various informal crosswalks rather than on one-way streets. The researchers watched video recordings to study different instances of people crossing streets. They found that 27.21% of pedestrians followed the safety rules when crossing, but a significant number did not and should be taught better crossing practices. The study identified three main behaviors that often led to rule violations: not looking both ways before crossing, being distracted, and not crossing cautiously. Pedestrians who were cautious were much more likely to follow the crossing rules compared to those moving at any speed, and those not distracted were more likely to follow the rules than those who were cautious. Additionally, 32.05% of pedestrians were distracted by activities like using phones, talking, or wearing headphones. Among these, 17.7% walked directly across the crosswalk, and 26.21% crossed in a diagonal or zigzag pattern. The study also found that 60% to 87% of the observed behaviors could predict whether a pedestrian would follow crossing rules based on statistical models.

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Section: Methodology Aim and Sampling Techniquesmentioning

confidence: 99%

Pedestrian crossing: Analysis of habits and compliance through unsignalized crosswalk in the city of Kigali

Nkurunziza,

Kumaran,

Tafahomi

et al. 2024

Int. j. adv. appl. sci.

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“…• DR is the highest rate at which a vehicle must decelerate to avoid a conflict [18]. • DRS is the constant deceleration rate required for the vehicle to stop and give the right-of-way to pedestrians [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Results suggested that stop sign-controlled intersections had lower DRS than unprotected intersections, which indicated that stop signs provided better protections for pedestrians. Olszewski et al [18] used the value of deceleration to identify pedestrian-vehicle conflicts at intersections. A threshold of 4m/s 2 was used to detect the abrupt braking behaviors of vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

Li¹,

Guo²,

Bao³

et al. 2022

Preprint

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Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs. However, most existing SSMs were developed based on the assumption that road users would maintain constant velocity and direction. Risk estimations based on this assumption are less unstable, more likely to be exaggerated, and unable to capture the evasive maneuvers of drivers. Considering the limitations among existing SSMs, this study proposes a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework loosen restrictions of constant speed by predicting trajectories using a Gaussian Process Regression and accounts for the different possible driver maneuvers with a Random Forest model. Real-world LiDAR data collected at an intersection was used to evaluate the performance of the proposed framework. The newly developed framework is able to identify all pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed framework provides a more stable risk estimation and captures the evasive maneuvers of vehicles. Moreover, the proposed framework does not require expensive computation resources, which makes it an ideal choice for real-time proactive pedestrian safety solutions at intersections.

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“…Common indicators include time-based, distance-based, and velocity-based metrics. Alternative indicators like Risk Index [4] and Danger Exposure Index [5] have been proposed. However, and combining multiple indicators is preferable to assess road risk levels.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment, prediction, and factor analysis for lanes at urban intersection

Wang,

Xu,

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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To determine the risk levels of urban signal-controlled intersection lanes, we propose two comprehensive risk assessment sets that incorporate time proximity, spatial proximity, and velocity-based indicators. Using actual trajectory data of intersection users, we analyse the interactions between three common vulnerable road users and motor vehicles. Bayesian inference algorithm is employed to evaluate lane risk levels. Additionally, XGBoost, random algorithm, and SVM algorithm with a Gaussian kernel are utilized to predict intersection lane risk. The importance and correlation of risk factors are explored using SHAP value theory. Results indicate that while extremely dangerous collisions are less likely due to signal control, moderate risk remains prevalent. The XGBoost model outperforms the other methods with an accuracy rate of 91.51% and precision rate of 94.17%. Among the analysed interaction risk factors, the speed and acceleration of road users have the greatest impact on lane risk levels. In low traffic flow conditions, higher speeds of motorcycles and cars increase collision risks. Lanes three and four exhibit higher probabilities of hazardous interactions between pedestrians and cars, resulting in more severe pedestrian injuries. Additionally, motorcycles at lower speeds are more prone to risky interactions with pedestrians.

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Surrogate safety indicator for unsignalised pedestrian crossings

Cited by 21 publications

References 18 publications

Pedestrian crossing: Analysis of habits and compliance through unsignalized crosswalk in the city of Kigali

Pedestrian crossing: Analysis of habits and compliance through unsignalized crosswalk in the city of Kigali

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

Risk assessment, prediction, and factor analysis for lanes at urban intersection

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