Safety and associated parameters influencing performance of rail road grade crossings: A critical review of state of the art

Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework

OBJECTIVE

This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.

METHODS

The methodology integrates inventory and collision data from Federal Railroad Association (FRA) data set during years 2010 to 2022 . The XGBoost and random forest (RF) algorithms are employed to identify influential collision severity factors. Then, the deep latent class analysis (DLCA) method is utilized on selected inventory factors as important features to cluster similar HRGCs. Afterward, collision modification factor (CMF) and standard error (SE) measures are computed for each countermeasure through collisions within each HRGC cluster.

RESULTS

XGBoost successfully identified 20 important collision and inventory factors with importance levels exceeding 94%, such as the number of daily trains and the surface material. Then, the DCLA method achieved 4 distinct clusters optimized by high similarity within each cluster and significant independence among clusters. The effectiveness of countermeasures was computed in terms of CMF and SE. The CMF results demonstrated that bells achieved superior safety compared to other countermeasures in clusters with sharper track angles and high maximum train speeds. Implementing bells decreased collisions across Clusters 1 and 4, with reductions of 53% (CMF = 0.47) and 46% (CMF = 0.54), respectively.

CONCLUSIONS

The results highlight XGBoost's capability to identify important collision and inventory factors, successfully uncovering 20 of the most important factors. The DCLA clustering method forms 4 distinct groups marked by substantial internal similarity within each cluster. This approach contributes to a clearer understanding of how each countermeasure impacts collision frequency. The findings highlight the varying effectiveness of different countermeasures across clusters, improving decision making for safety at HRGCs. The study highlights the efficacy of crossbucks in addressing safety concerns during moderate traffic conditions, particularly evident in environments with a highway speed limit between 100 and 125 mph. Additionally, bells demonstrate notable effectiveness in areas with sharper track angles.

An observational study on pedestrian and bicyclist violations at railroad grade crossings: Exploring the impact of geometrical and operational attributes

INTRODUCTION

In order to enhance the existing body of research regarding the prevalence of violations committed by pedestrians and bicyclists, this observational study utilized personal-specific, train-specific, operational, and behavioral data extracted from recorded videos at seven railroad grade crossings (RRGCs).

METHOD

A total of 7,332 pedestrians and 1,684 bicyclist crossing events were observed during 279 gate operation phases at seven study locations. First, two separate violation models (binary logit) for pedestrians and bicyclists were developed to determine the influence of personal-specific characteristics and other behavioral traits on probability of pedestrians' and bicyclists' violation. Later, the impact of various geometrical (railway track, distance between gates, distance between gate and nearest track, and road width) and operational (train vehicle unit) parameters on the factors that caused non-motorist violations was examined by developing distinctbinary logit models.

RESULTS

The probability of pedestrians'and bicyclists' violationwas found to be significantly influenced by train characteristics (train speed and train occupancy time), gate operational attributes (warning time and gate blockage duration), and personal-specificcharacteristics (gender, age, crossing speed, and looking both side). Furthermore, geometrical attributes along with train vehicle unit were observed to have impact over various factors that significantly influenced the behavior of pedestrians and bicyclists to violate.

PRACTICAL APPLICATIONS

The findings of this research provide essential information that could be applied in the initiatives of user awareness and enforcement program, emphasis on situational awareness and design of operational and geometricalelementsto significantlylimitthe effects of risky walking andbicycling behavior at RRGCs.

Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections

Transportation agencies continue to pursue crash reduction. Initiatives include the design of safer facilities, promotion of safe behaviors, and assessments of collision risk as a precursor to the identification of proactive countermeasures. Collision risk assessment includes reliable prediction of vehicle trajectories. Unfortunately, in using traditional tracking equipment, such prediction can be impaired by occlusion. It has been suggested in recent literature that unmanned aerial vehicles (UAVs) can be deployed to address this issue successfully, given their wide visual field and movement flexibility. This paper presents a methodology that integrates UAVs to track the movement of road users and to assess potential collisions at intersections. The proposed methodology includes an existing deep-learning-based algorithm to identify road users, extract trajectories, and calculate collision risk. The methodology was applied using a case study, and the results show that the methodology can provide beneficial information for the purpose of measuring and analyzing the infrastructure performance. Based on vehicle movements it observes, the UAV can communicate its collision risk to each vehicle so that the vehicle can undertake proactive driving decisions. Finally, the proposed framework can serve as a valuable tool for urban road agencies to develop measures to reduce crash risks.