Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique.

Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety.

Investigating factors affecting bus/minibus accident severity in a developing country for different subgroup datasets characterised by time, pavement, and light conditions.

Developing countries are primarily associated with poor roadway and lighting infrastructure challenges, which has a considerable effect on their traffic accident fatality rates. These rates are further increased as bus/minibus drivers indulge in risky driving, mainly during weekends when traffic and police surveillance is low to maximise profits. Although these factors have been mentioned in the literature as key indicators influencing accident severity of buses/minibuses, there is currently no study that explored the complex mechanisms underpinning the simultaneous effect of pavement and light conditions on the generation of accident severity outcomes while considering weekly temporal stability of the accident-risk factors. This study seeks to investigate the variations in the effect of contributing factors on the severity of bus/minibus accidents in Ghana across various combinations of pavement and light conditions and to identify the exact effects of weekdays and weekends on severity outcomes using a random parameter ordered logit model with heterogeneity in the means to account for unobserved heterogeneity in the police-reported data. Preliminary analysis demonstrated that accident-risk factors used in the models were temporally unstable, warranting the division of the data into both weekend and weekday time-periods. A wide variety of factors such as sideswipes, median presence, merging, and overtaking had significantly varying effects on bus/minibus accident severities under different combinations of pavement and light conditions for both weekdays and weekends. Insights drawn from this study, together with the policy recommendations provided, can be employed by engineers and policymakers to improve traffic safety in developing nations.

Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway.

Detecting high-collision-concentration locations based solely on collision frequency may produce different results compared to those considering the severities of the collisions. In particular, it can lead government agencies focusing sites with a high collision frequency while neglecting those with a lower collision frequency but a higher percentage of injury and fatal collisions. This study developed systematic ways of detecting reproducible fatal collision locations (R) using the naïve Bayes approach and a continuous risk profile (CRP) that estimates the true collision risk by filtering out random noise in the data. The posterior probability of fatal collisions being reproducible at a location is estimated by the relationship between the spatial distribution of fatal-collision locations (i.e., likelihood) and the CRP (i.e., prior probability). The proposed method can be used to detect sites with the highest proxy measure of the posterior probability (PMP) of observing R. An empirical evaluation using 5-year traffic collision data from six routes in California shows that detecting R based on the PMP outperform those based on the SPF-based approaches or random selection, regardless of various conditions and parameters of the proposed method. This method only requires traffic collision and annual traffic volume data to estimate PMP that prioritize sites being R and the PMPs can be compared across multiple routes. Therefore, it helps government agencies prioritizing sites of multiple routes where the number of fatal collisions can be reduced, thus help them to save lives with limited resources of data collection.

Geocoding police collision report data from California: a comprehensive approach.

BACKGROUND: Collision geocoding is the process of assigning geographic descriptors, usually latitude and longitude coordinates, to a traffic collision record. On California police reports, relative collision location is recorded using a highway postmile marker or a street intersection. The objective of this study was to create a geocoded database of all police-reported, fatal and severe injury collisions in the California Statewide Integrated Traffic Records System (SWITRS) for years 1997-2006 for use by public agencies. RESULTS: Geocoding was completed with a multi-step process. First, pre-processing was performed using a scripting language to clean and standardize street name information. A state highway network with postmile values was then created using a custom tool written in Visual Basic for Applications (VBA) in ArcGIS software. Custom VBA functionality was also used to incorporate the offset direction and distance. Intersection and address geocoding was performed using ArcGIS, StreetMap Pro 2003 digital street network, and Google Earth Pro. A total of 142,007 fatal and severe injury collisions were identified in SWITRS. The geocoding match rate was 99.8% for postmile-coded collisions and 86% for intersection-coded collisions. The overall match rate was 91%. CONCLUSIONS: The availability of geocoded collision data will be beneficial to clinicians, researchers, policymakers, and practitioners in the fields of traffic safety and public health. Potential uses of the data include studies of collision clustering on the highway system, examinations of the associations between collision occurrence and a variety of variables on environmental and social characteristics, including housing and personal demographics, alcohol outlets, schools, and parks. The ability to build maps may be useful in research planning and conduct and in the delivery of information to both technical and non-technical audiences.

Enforcement avoidance behavior near automated speed enforcement areas in Korean expressways.

Automated speed enforcement system (ASES) has been deployed as a safety countermeasure on Korean roadways to reduce speeding-related traffic crashes; information on ASES locations is mandated to be open to the public. However, because drivers are alerted about enforcement via on-board navigation systems and roadside signs, they can avoid enforcement by momentarily reducing their speeds near ASES locations. This enforcement avoidance behavior (EAB) can induce sudden changes in speed near the enforcement locations and thereby increase risk of crash occurrence. In light of this situation, the present study evaluates the effects of ASES on traffic behavior and safety. An analysis of traffic data shows that drivers indeed diminish their speeds near enforcement locations, and accelerate shortly after passing the locations. To investigate how this behavior affects safety performance, this study, by using Empirical Bayes analysis with comparison groups, compares crash occurrences along a certain section before and after the installation of ASES. The comparative analysis shows that overall crash occurrence dropped by 7.6% on average near the enforcement locations, although the reduction was not significant. However, an average 11% non-significant increase in crash occurrence is also observed in the upstream segment, where enforcement is announced to drivers and traffic starts to diminish speed. The findings suggest that the sudden changes in traffic speed induced by EAB substantially negate the benefits of ASES. Therefore, modification of the design of current ASES is required to mitigate EAB and further improve the effectiveness of ASES.