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Kusumastutie NS, Patria B, Kusrohmaniah S, Hastjarjo TD. Hazardous traffic scenarios for motorcyclists in Indonesia: a comprehensive insight from police accident data and self-reports. Int J Inj Contr Saf Promot 2024; 31:408-419. [PMID: 38683671 DOI: 10.1080/17457300.2024.2335495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 05/02/2024]
Abstract
Motorcycle safety remains a concern in low- and middle-income countries. This study addresses this issue by identifying hazardous scenarios for motorcyclists in Indonesia. We conducted a two-step cluster analysis and injury analysis to examine motorcycle accidents based on the police accident dataset (2020-2021) of Brebes Regency, Indonesia. We integrated the findings with accident self-reports from 104 young motorcyclists using a joint display to obtain a more comprehensive insight. As a result, we identified four hazardous traffic scenarios: motorcycle-to-vehicle collisions on median roads, motorcycle-to-vehicle collisions on non-median roads, motorcycle-to-pedestrian collisions, and single-motorcycle collisions. We suggest countermeasures for each scenario and propose a public transport policy as a safer mobility solution. Applying a two-step cluster analysis on accident data and integrating the findings of accident data and self-report analysis proved beneficial in this study. Therefore, we encourage the use of this novel approach in future studies.
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Affiliation(s)
- Naomi Srie Kusumastutie
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Road Transportation System Engineering Program, Politeknik Keselamatan Transportasi Jalan, Tegal, Indonesia
| | - Bhina Patria
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Sri Kusrohmaniah
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Huang H, Huang X, Zhou R, Zhou H, Lee JJ, Cen X. Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data. ACCIDENT; ANALYSIS AND PREVENTION 2024; 203:107616. [PMID: 38723335 DOI: 10.1016/j.aap.2024.107616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 06/03/2024]
Abstract
Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.
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Affiliation(s)
- Helai Huang
- School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
| | - Xiangzhi Huang
- School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Rui Zhou
- School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
| | - Hanchu Zhou
- School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
| | - Jaeyoung Jay Lee
- School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA; School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia
| | - Xuekai Cen
- School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
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Kome Fondzenyuy S, Shingo Usami D, González-Hernández B, Brown L, Morris A, Persia L. Developing improved crash prevention approaches through in-depth investigation of motorcycle crash causation patterns. Heliyon 2024; 10:e32866. [PMID: 38975199 PMCID: PMC11225811 DOI: 10.1016/j.heliyon.2024.e32866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 05/20/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
Despite advancements in road safety, Powered Two-Wheelers (PTWs) remain a vulnerable group with disproportionately high crash rates. This paper presents an in-depth analysis of PTW crashes in six European countries, with a case study of Loss of Control in Curves (LoCC), to address the gap between crash causation and prevention. By examining crash causation factors and their linkage to prevention strategies, the study illustrates various approaches for connecting causes and countermeasures. These approaches, which are applicable to different crash scenarios, include looking forward in the crash causation chains, looking backward, looking at only the last cause (critical events), or the first cause, or following a systemic approach. The research introduces a set of guidelines following the safe system approach, aiming to enhance the understanding of crash prevention among policymakers. The systemic approach to countermeasures, bridges the shortcomings of traditional crash causation studies that may exhibit bias or a narrow focus on "root causes". The proposed approach emphasizes the need for a comprehensive view of crash scenarios (i.e., considering the entire crash causation chain or multiple causation chains) and ensuring that preventive measures address the full spectrum of the system. It also takes in to account external factors such as cost, benefits, and politics, leading to improved road safety outcomes. The study findings are significant for researchers, since it is a step forward in in-depth crash causation studies, as well as road practitioners and policymakers, in providing a strategic framework for more effective and efficient road safety interventions.
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Affiliation(s)
- Stephen Kome Fondzenyuy
- Centro di Ricerca per il Trasporto e la Logistica (CTL), Sapienza Università di Roma, Via Eudossiana, 18, 00184, Roma, Italy
| | - Davide Shingo Usami
- Centro di Ricerca per il Trasporto e la Logistica (CTL), Sapienza Università di Roma, Via Eudossiana, 18, 00184, Roma, Italy
| | - Brayan González-Hernández
- Department of Civil, Environmental Engineering (DICEA), University of Naples Federico II, Via Claudio 21, P.O. 80125, Naples, Italy
| | - Laurie Brown
- Transport Safety Research Centre, Loughborough Design School, Loughborough University, Epinal Way, Loughborough, Leicestershire, LE11 3TU, UK
| | - Andrew Morris
- Transport Safety Research Centre, Loughborough Design School, Loughborough University, Epinal Way, Loughborough, Leicestershire, LE11 3TU, UK
| | - Luca Persia
- Centro di Ricerca per il Trasporto e la Logistica (CTL), Sapienza Università di Roma, Via Eudossiana, 18, 00184, Roma, Italy
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Qian Q, Shi J. Comparison of injury severity between E-bikes-related and other two-wheelers-related accidents: Based on an accident dataset. ACCIDENT; ANALYSIS AND PREVENTION 2023; 190:107189. [PMID: 37390750 DOI: 10.1016/j.aap.2023.107189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/24/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023]
Abstract
This study aims to compare the accident injury severity of e-bikes with that of other types of two-wheelers based on accident data and to analyze the factors influencing them. Using 1015 police accident records from Zhangjiakou City in 2020 and 2021, the accident injury severity of e-bikes was firstly compared with that of other two-wheelers based on five levels of accident injury severity classified according to the records. Two ordered Probit regression models were secondly used to compare the factors influencing the accident injury severity of e-bikes with that of other two-wheelers and the magnitude of their effects. At the same time, the contributions of each influential factor to the degree of accident injury of two-wheelers were estimated with the assistance of classification trees. Results show that e-bikes are closer to bicycles than motorcycles in terms of injury severities and the factors influencing them, in which the factors "accident configuration," "division of responsibility for the accident," and "collision with a heavy vehicle or four-wheeled vehicle" are significant. Based on the findings, potential measures are suggested to reduce e-bike accident casualties, such as improving rider education, ensuring speed limit enforcement, promoting safety equipment wearing, and making road design friendly to non-motorized and elderly riders. The results of this study can provide an essential reference for traffic management and rider education measures on e-bikes.
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Affiliation(s)
- Qian Qian
- Department of Civil Engineering, Tsinghua University, Beijing, China
| | - Jing Shi
- Department of Civil Engineering, Tsinghua University, Beijing, China.
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Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19106013. [PMID: 35627556 PMCID: PMC9141871 DOI: 10.3390/ijerph19106013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023]
Abstract
The purpose of this paper is to analyze the complex coupling relationships among accident factors contributing to the automobile and two-wheeler traffic accidents by establishing the Bayesian network (BN) model of the severity of traffic accidents, so as to minimize the negative impact of automobile to two-wheeler traffic accidents. According to the attribution of primary responsibility, traffic accidents were divided to two categories: the automobile and two-wheeler traffic as the primary responsible party. Two BN accident severity analysis models for different primary responsible parties were proposed by innovatively combining the Kendall correlation analysis method with the BN model. A database of 1560 accidents involving an automobile and two-wheeler in Guilin, Guangxi province, were applied to calibrate the model parameters and validate the effectiveness of the models. The result shows that the BN models could reflect the real relationships among the influential factors of the two types of traffic accidents. For traffic accidents of automobiles and two-wheelers as the primary responsible party, respectively, the biggest influential factors leading to fatality were weather and visibility, and the corresponding fluctuations in the probability of occurrence were 32.20% and 27.23%, respectively. Moreover, based on multi-factor cross-over analysis, the most influential factors leading to fatality were: {Off-Peak Period → Driver of Two-Wheeler: The elderly → Driving Behavior of Two-Wheeler: Parking} and {Drunk Driving Two-Wheeler → Having a License of Automobiles → Visibility: 50 m~100 m}, respectively. The results provide a theoretical basis for reducing the severity of automobile to two-wheeler traffic accidents.
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Pan D, Han Y, Jin Q, Wu H, Huang H. Study of typical electric two-wheelers pre-crash scenarios using K-medoids clustering methodology based on video recordings in China. ACCIDENT; ANALYSIS AND PREVENTION 2021; 160:106320. [PMID: 34358751 DOI: 10.1016/j.aap.2021.106320] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/19/2021] [Accepted: 07/22/2021] [Indexed: 06/13/2023]
Abstract
Crash safety of electric two-wheelers (ETWs) has been one of the most important safety issues in China due to their high proportion of involvement in traffic accidents. Automated Emergency Braking (AEB) systems have proven to be effective in reducing the number of fatalities and injuries in traffic accidents. Providing test scenarios is one of the fundamental tasks required for establishing a set of AEB test programs for ETWs. Compared to traditional in-depth accident data, accident data accompanied with video recordings provide more accurate accident information prior to a crash as both the traffic environment and the crash process can be observed from the video. In this study, a set of typical AEB test scenarios for ETWs was developed using accident data with video information. Video recordings of 630 car-to-ETW crashes in China from 2010 to 2021 were selected from the VRU Traffic Accident database with Video (VRU-TRAVi). A K-medoids1 cluster analysis was carried out based on variables including the collision time, visual obstruction, motion of the car and ETW before the collision, relative motion direction between the car and ETW, and the ETW type. The velocity information of cars and ETWs was also accounted for in each clustering scenario. Seven typical pre-crash scenarios were obtained, including five electric-scooter (E-scooter) scenarios (representing two scenarios where the ETWs are approaching the car from the left side, two scenarios where the ETWs are approaching the car in the same direction and another scenario where the ETWs are approaching the car in the opposite direction) and two electric-bike (E-bike) scenarios where the E-bikes are approaching the car in the perpendicular direction. Both E-bike scenarios are consistent with the E-scooter scenario except for the ETW type and velocity range; therefore, by combining the E-bike and E-scooter scenarios, five ETW scenarios were finally recommended as AEB test scenarios. By comparing with typical scenarios extracted based on the China In-Depth Accident Study (CIDAS) data and the China New Car Assessment Program (C-NCAP) test scenarios, the results show that future AEB test scenarios for ETWs should focus on scenarios with visual obstructions and scenarios where either the car or the ETW is turning, with a velocity range of 15-30 km/h for ETWs.
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Affiliation(s)
- Di Pan
- School of Aerospace Engineering, Xiamen University, Xiamen, China; School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China
| | - Yong Han
- School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China; Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle, Xiamen, China.
| | - Qianqian Jin
- School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China; Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle, Xiamen, China
| | - He Wu
- School of Aerospace Engineering, Xiamen University, Xiamen, China; School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China
| | - Hongwu Huang
- School of Aerospace Engineering, Xiamen University, Xiamen, China; School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China; Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle, Xiamen, China
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Kamnik R, Nekrep Perc M, Topolšek D. Using the scanners and drone for comparison of point cloud accuracy at traffic accident analysis. ACCIDENT; ANALYSIS AND PREVENTION 2020; 135:105391. [PMID: 31835075 DOI: 10.1016/j.aap.2019.105391] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 11/05/2019] [Accepted: 12/01/2019] [Indexed: 06/10/2023]
Abstract
The purpose of the paper is to describe, compare and analyse the instruments used, time needed and accuracy of gathered data, sketches, 3D models and to enhance the extracted information about the accident. Simple sketches and tape measurements were performed. Also complex 3D measurements and 3D modelling of the scene with Terrestrial Laser Scanners (TLS) and Unmanned Aerial Vehicle (UAV) technology were used. A classical police work dealing with a simulated traffic accident was compared to sketches obtained from 3D models from Riegl VZ-400i 3D, Faro Focus S70, Geoslam ZebRevo 3D TLS and Topcon Falcon 8 drone. For 3D modelling an orthophoto from drone photos and point clouds were obtained. 3D models were graphically compared in CloudCompare software. Sketches were made for each measuring method and their accuracies were also compared one to each other. The graphical distance accuracy in scene measurements ranged up to 17 cm in comparison to police measurement but in the most course point cloud. Average absolute difference in compared distances amounts up to 6 cm. As expected, more points in the cloud means better 3D model and easier analysis. There is considerable reduction of time needed for collecting the accident scene data. The obtained 3D model is a permanent archive of the scene of a traffic accident. From the cadre, both visual and dimensional information subsequently can be obtained.
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Affiliation(s)
- Rok Kamnik
- Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Maribor, Slovenia.
| | - Matjaž Nekrep Perc
- Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Maribor, Slovenia
| | - Darja Topolšek
- Faculty of Logistics, University of Maribor, Celje, Slovenia
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