An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment

Abstract

This study develops an algorithm to detect the risk of collision between trucks (i.e., yard tractors) and pedestrians (i.e., workers) in the connected environment of the port. The algorithm consists of linear regression-based movable coordinate predictions and vertical distance and angle judgments considering the moving characteristics of objects. Time-to-collision for port workers (TTCP) is developed to reflect the characteristics of the port using the predictive coordinates. This study assumes the connected environment in which yard tractors and workers can share coordinates of each object in real time using the Internet of Things (IoT) network. By utilizing microtraffic simulations, a port network is implemented, and the algorithm is verified using data from simulated workers and yard trucks in the connected environment. The risk detection algorithm is validated using confusion matrix. Validation results show that the true-positive rate (TPR) is 61.5∼98.0%, the false-positive rate (FPR) is 79.6∼85.9%, and the accuracy is 72.2∼88.8%. This result implies that the metric scores improve as the data collection cycle increases. This is expected to be useful for sustainable transportation industry sites, particularly IoT-based safety management plans, designed to ensure the safety of pedestrians from crash risk by heavy vehicles (such as yard tractors).

A Before-and-After Study of a Collision Risk Detecting and Warning System on Local Roads

“…Under clear weather, accuracy, TPR, and FPR were found to be 80.9%, 76.1%, and 15.4%, whereas, under rainy weather, they were 80.0%, 77.4%, and 18.3%, respectively. Te fndings confrmed that the performance of the system particularly for local roads is comparable to that of other systems for other kinds of roads in terms of overall performance [3,4,9]. However, there was a diference between day and night.…”

“…SSMs, including timeto-collision (TTC) and post encroachment time, have been extensively utilized to assess trafc safety performance and identify potential accident hazards. For example, Son et al developed an algorithm to detect the risk of collisions between trucks and pedestrians [9]. Using microscopic simulations, the authors established a road network, and the algorithm was then validated using a confusion matrix.…”

Development of a Residential Road Collision Warning Service Based on Risk Assessment

“…Terefore, we were able to use the homogeneity test. Te formula for the test statistics, χ 2 -value, is mathematically expressed as equation (8).…”

“…Using microscopic trafc characteristics such as vehicle speed, acceleration, time headway, and space headway, an SSM method assesses the collision risk of particular trafc scenarios [7]. SSMs, such as time-to-collision (TTC) and post encroachment time (PET), have been widely used to evaluate trafc safety performance and identify potential accident risks [8][9][10][11][12][13]. One study assumed a connected environment in which pedestrians and vehicles shared real-time location information using IoT devices.…”

“…One study assumed a connected environment in which pedestrians and vehicles shared real-time location information using IoT devices. According to object locations, velocity, relative distance, angle, and TTC, dangerous situations were determined [8]. In another study, an algorithm was developed using onboard cameras in vehicles.…”