Journal ArticleDOI
Motorcyclist injury severity analysis: a comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances
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In this article , the effects of a wide range of associated risk characteristics on the severity outcomes of the motorcyclist were explored using Artificial Neural Networks (ANN) and random parameters binary probit model with heterogeneity in means and variances (RPBPHM).Abstract:
Abstract In Thailand, the motorcyclist mortality rate is steadily on the rise and remains a serious concern for highway administrators and burden on both economic and local people. Using motorcycle-crash data in Thailand from 2016 to 2019, this study empirically employed and compared the Artificial Neural Networks (ANN) model and random parameters binary probit model with heterogeneity in means and variances (RPBPHM) to explore the effects of a wide range of associated risk characteristics on the severity outcomes of the motorcyclist. Study results revealed that probabilities of injury or fatal crash increase for crashes that involve male riders, riding with pillion, speeding, improper overtaking, riders under influence of alcohol, fatigue riders, undivided road and so on. The probability of non-injury crash increases for crashes on main or frontage traffic lane, four-lane road, concrete road, during rain, involving collision with other motorcycles, rear-end crashes, sideswipe crashes, single-motorcycle crashes and crashes within urban areas. The RPBPHM models were found to outperform the ANN model (quadratic support vector machine) in all performance metrics. The findings could potentially assist policymaker, safety professionals, practitioners, trainers, government agencies or highway designers in future planning and serve as guidance for mitigation policies directed at safety improvement for motorcyclists.read more
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The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: accounting for temporal influence with unobserved effect and insight from out-of-sample prediction
Chamroeun Se,Thanapong Champahom,Sajjakaj Jomnonkwao,Nopadon Kronprasert,Vatanavongs Ratanavaraha +4 more
TL;DR: In this paper , the authors examined the differences between weekday, weekend, and holiday crashes on the severity of motorcyclist injury using four-year motorcycle crash data in Thailand from 2016 to 2019.
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Evaluating gender differences in injury severities of non-helmet wearing motorcyclists: accommodating temporal shifts and unobserved heterogeneity
Chenzhu Wang,Muhammad Ijaz,Fei Chen,Yunlong Zhang,Jianchuan Cheng,Muhammad Ayoob Memon Ruba Ali Zahid +5 more
TL;DR: In this article , the authors investigated possible temporal instability and differences in the factors that determine resulting injury severities between male and female non-helmet wearing motorcyclists, using a random parameter logit approach with heterogeneity in means and variances.
Journal ArticleDOI
Application of Feature Selection Approaches for Prioritizing and Evaluating the Potential Factors for Safety Management in Transportation Systems
Giuseppe Guido,Samira Haghshenas,Sina Shaffiee Haghshenas,Alessandro Vitale,Vittorio Astarita +4 more
TL;DR: Two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic.
Journal ArticleDOI
Temporal Instability of Motorcycle Crash Fatalities on Local Roadways: A Random Parameters Approach with Heterogeneity in Means and Variances
Thanapong Champahom,Chamroeun Se,Sajjakaj Jomnonkwao,Tassana Boonyoo,Amphaphorn Leelamanothum,Vatanavongs Ratanavaraha +5 more
TL;DR: In this article , the authors identified the root causes of fatal motorcycle accidents on local roads, which consist of four groups: rider characteristics, maneuvers prior to the crash, temporal and environmental characteristics, and road characteristics.
Journal ArticleDOI
Differences in single-vehicle motorcycle crashes caused by distraction and overspeed behaviors: considering temporal shifts and unobserved heterogeneity in prediction.
Chenzhu Wang,Muhammad Ijaz,Fei Chen,Dong‐Ling Song,Mingyu Hou,Yunlong Zhang,Jianchuan Cheng,Muhammad Zahid +7 more
TL;DR: In this article , two groups of random parameter logit models with heterogeneity in means and variances were estimated to explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors.
References
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Statistical and Econometric Methods for Transportation Data Analysis
TL;DR: My overall impression of this text is that it contains a wealth of useful upto-date information and examples from the health sciences, but I find the presentation of material in this text difficult to follow for students.