Browsing by Author "Sasidharan, Lekshmi"

Now showing 1 - 3 of 3

Crash sequence based risk matrix for motorcycle crashes
(2018-08-01)
Wu, Kun-Feng
;
Sasidharan, Lekshmi
;
Thor, Craig P.
;
Chen, Sheng-Yin
;
運輸與物流管理系註：原交通所+運管所
Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers' age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011-2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-offroad crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.
Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland
(2015-12-01)
Sasidharan, Lekshmi
;
Wu, Kun-Feng
;
Menendez, Monica
;
土木工程學系
;
Department of Civil Engineering
One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury seventies and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. (C) 2015 Elsevier Ltd. All rights reserved.
Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland (vol 85, pg 219, 2015)
(2016-09)
Sasidharan, Lekshmi
;
Wu, Kun-Feng
;
Menendez, Monica
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management