Pedestrian-vehicle interaction severity level assessment at uncontrolled intersections using machine learning algorithms

• Published in: Safety science 2022-09, Vol.153, p.105806, Article 105806
• Main Authors: Govinda, Lalam, Sai Kiran Raju, M.R., Ravi Shankar, K.V.R.
• Format: Article
• Language: English
• Subjects: Algorithms; Encroachment; Gender; Interaction models; Machine learning; Pedestrian crossings; Pedestrian-vehicle interaction; Pedestrians; Position (location); Post encroachment time; Regression models; Risk indicator; Risk management; Roads; Roads & highways; Safety measures; Social sciences; Software; Statistical analysis; Support vector machines; Surrogate safety measures; Threshold values; Traffic accidents & safety; Traffic information; Traffic intersections; Videography; Visual observation
• ISSN: 0925-7535; 1879-1042
• DOI: 10.1016/j.ssci.2022.105806

•Threshold risk indicator values were proposed for severe P-V interactions.•Support Vector Machine (SVM) algorithm was used for classification purpose.•Multilinear regression model was developed for pedestrian-vehicle interactions.•Severity of pedestrian-vehicle interactions depends on pedestrian crossing speed. As a consequence of the rapid growth of vehicular traffic, there is an increase in interactions between vehicles and pedestrians. The severity of these interactions varies with pedestrian, vehicle and roadway geometric characteristics. In the absence of real crash data, Surrogate Safety Measures (SSMs) are used to analyse the pedestrian-vehicle (P-V) interactions. The present study is intended to propose threshold risk indicator (RI) values for severe P-V interactions using both pedestrian and vehicle characteristics. A multilinear regression (MLR) P-V interaction model was developed using SPSS (Statistical Package for the Social Sciences) software. Videography method was used to collect traffic data from two 4-legged uncontrolled intersections. Pedestrian and vehicular data were extracted from the video using DataFromSky viewer software and risk indicator was calculated using post encroachment time and approaching vehicular speed. The interactions between pedestrians and vehicles were classified as normal conflicts and severe conflicts based on visual observations during the data extraction process. Python interface with support vector machines (SVM) algorithm was used to get threshold RI values for various pedestrian (gender and speed) and vehicle (type) characteristics. From SVM results, it was observed that the threshold RI value for severe interactions decreases as the pedestrian crossing speed increases for the same vehicle and pedestrian characteristics. MLR results showed that pedestrian gender, age and speed, vehicle type and speed, interaction location and crossing position have a significant effect on RI. The results can be used to evaluate pedestrian-vehicle interaction severity level at an uncontrolled intersection.