A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records.

In recent years, individual drivers' crash risk assessments have received much attention for identifying high-risk drivers. To this end, we propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e.

Travel pattern-based bus trip origin-destination estimation using smart card data.

Smart card data are widely used in generating the origin and destination (O-D) matrix for public transit, which contains important information for transportation planning and operation. However, the generation of the O-D matrix is limited by the smart card data information that includes the boarding (origin) information without the alighting (destination) information. To solve this problem, trip chain methods have been proposed, thereby greatly contributing in estimating the destination using the smart card data.

Spatiotemporal filtering method for detecting kinematic waves in a connected environment.

Backward-moving kinematic waves (KWs) (e.g., stop-and-go traffic conditions and a shock wave) cause unsafe driving conditions, decreases in the capacities of freeways, and increased travel time.

Analysis of the duration of compliance between recidivism of drunk driving and reinstatement of license after suspension or revocation.

The objective of this study was to examine the specific deterrence effect of administrative license suspension and revocation regarding the duration of compliance. This study tried to capture the effort of the reinstated offenders to increase the duration of their compliance in spite of their subsequent conviction for DUI. Specifically, the difference between the recidivism rate and the duration of compliance was examined and compared in terms of drivers' characteristics, including gender, the type of license, and age.

Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions.

This study analyzes 86,622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury).

Hierarchical ordered model for injury severity of pedestrian crashes in South Korea.

Introduction: The high percentage of fatalities in pedestrian-involved crashes is a critical social problem. The purpose of this study is to investigate factors influencing injury severity in pedestrian crashes by examining the demographic and socioeconomic characteristics of the regions where crashes occurred.

Method: To understand the correlation between the unobserved characteristics of pedestrian crashes in a defined region, we apply a hierarchical ordered model, in which we set crash characteristics as lower-level variables and municipality characteristics as upper-level.