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

Abstract

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). The company level factors reflect selected characteristics of 1,875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi company's type of vehicle ownership and municipality's level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.

Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

“…In the event of a crash, factors related to intersections and exclusive bus lanes were associated with more severe crashes. In the case of exclusive bus lanes, the excessive speed of buses with a separate travel priority may increase the injury severity of pedestrians in crashes, which was supported by the findings of previous studies [13,26]. However, the effect of intersections has proven to be controversial.…”

“…There is growing interest in the use of multi-level models to analyze hierarchically structured traffic crash data, and these models are often referred to as hierarchical models [1,[7][8][9][10][11][12][13][14][15]. Two research studies have summarized the use of multi-level analysis of traffic crash data and suggested further studies [14,15].…”

“…Bus-pedestrian crash data, which include both crash level and group level (region and company) factors, have a cross-classified hierarchical structure. Some research results have indicated that traffic crash data have a hierarchical structure, usually focused on geographical or regional effects, and multi-level modeling should be used to account for group-level factors [1,[7][8][9][10][11][12][13][14][15]. Concerning the severity of the injuries in bus or pedestrian crashes, two-level models were used to account for the correlation of the crashes in a defined region [1,7].…”

“…This means that bus-pedestrian crashes have two distinct hierarchical paths, and applying a two-level model to cross-classified hierarchical data may yield a biased estimate of the parameters [12]. Because cross-group heterogeneity is neglected in hierarchical data, a two-level model can underestimate the standard error, thereby resulting in a type I statistical error when testing statistical hypotheses [13]. To analyze these two higher group factors, a cross-classified, multi-level model (CCMM) is required, because it can consider non-nested groups simultaneously.…”

Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects

“…We thus distinguish three different levels, the individual offender level and two community levels. Cross-classified multilevel models, which have already been widely used in other domains [51,52], have not yet been used in journey-to-crime studies, but they are well-suited to analyze data of this structure [53]. In total, we had 3763 crime trips nested in 1117 home communities and 1382 target communities.…”