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Hazardous traffic scenarios for motorcyclists in Indonesia: a comprehensive insight from police accident data and self-reports

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International Journal for Consumer and Product Safety 2024年第3期31卷

作者： Naomi Srie Kusumastutie Bhina Patria Sri Kusrohmaniah Thomas Dicky Hastjarjo a Faculty of Psychology Universitas Gadjah Mada Yogyakarta Indonesiab Road Transportation System Engineering Program Politeknik Keselamatan Transportasi Jalan Tegal Indonesia a Faculty of Psychology Universitas Gadjah Mada Yogyakarta Indonesia

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.

关键词： Hazardous traffic scenarios motorcyclist Indonesia accident data two-step cluster self-reports

V2V spatiotemporal interactive pattern recognition and risk analysis in lane changes

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arXiv 2021年

作者： Zhang, Yue Zou, Yajie Wu, Lingtao Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University No. 4800 Cao'an Road Shanghai201804 China Texas A&M Transportation Institute Texas A&M University System 3135 TAMU College StationTX77843-3135 United States

In complex lane change (LC) scenarios, semantic interpretation and safety analysis of dynamic interactive pattern are necessary for autonomous vehicles to make appropriate decisions. This study proposes an unsupervised learning framework that combines primitive-based interactive pattern recognition methods and risk analysis methods. The Hidden Markov Model with the Gaussian mixture model (GMM-HMM) approach is developed to decompose the LC scenarios into primitives. Then the Dynamic Time Warping (DTW) distance based K-means clustering is applied to gather the primitives to 13 types of interactive patterns. Finally, this study considers two types of time-to-collision (TTC) involved in the LC process as indicators to analyze the risk of the interactive patterns and extract high-risk LC interactive patterns. The results obtained from The Highway Drone Dataset (highD) demonstrate that the identified LC interactive patterns contain interpretable semantic information. This study explores the spatiotemporal evolution law and risk formation mechanism of the LC interactive patterns and the findings are useful for comprehensively understanding the latent interactive patterns, improving the rationality and safety of autonomous vehicle's *** Codes 40-08. Copyright © 2021, The Authors. All rights reserved.

关键词： Hidden Markov models

Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps

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arXiv 2021年

作者： Zhang, Yue Zou, Yajie Wu, Lingtao Han, Wanbing Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai201804 China Texas A&M Transportation Institute Texas A&M University System 3135 TAMU College StationTX77843-3135 United States

Understanding the merging behavior patterns at freeway on-ramps is important for assistanting the decisions of autonomous driving. This study develops a primitive-based framework to identify the driving patterns during merging processes and reveal the evolutionary mechanism at freeway on-ramps in congested traffic flow. The Nonhomogeneous Hidden Markov Model is introduced to decompose the merging processes into primitives containing semantic information. Then, the time-series K-means clustering is utilized to gather these primitives with variable-length time series into interpretable merging behavior patterns. Different from traditional state segmentation methods (e.g. Hidden Markov Model), the model proposed in this study considers the dependence of transition probability on exogenous variables, thereby revealing the influence of covariates on the evolution of driving patterns. This approach is evaluated in the merging area at a freeway on-ramp using the INTERACTION dataset. Results demonstrate that the approach provides an insight about the complicated merging processes. The findings about interpretable merging behavior patterns as well as the evolutionary mechanism can be used to design and improve the merging decision-making for autonomous vehicles. Copyright © 2021, The Authors. All rights reserved.

关键词： Hidden Markov models

Investigation of the synthesis and thermal insulation properties of K2Ti6O13 whisker-reinforced SiO2 micro powder composite coating fabrics

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Energy 2025年 328卷

作者： Man, Yi Ge, Chang Wang, Yao He, Yongqing Yang, Wei Qi, Yumin Wang, Bohong Varbanov, Petar Sabev Wang, Jin Nanjing Vocational University of Industry Technology Jiangsu Nanjing210023 China School of Materials Science and Engineering Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of New Functional Materials School of Material Science and Engineering Hebei University of Technology Tianjin300401 China School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China Chongqing Key Laboratory of Micro-Nano System and Intelligent Transduction National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing400067 China National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control Zhejiang Ocean University No.1 Haida South Road Zhoushan316022 China Széchenyi István University Egyetem tér 1 Győr9026 Hungary

Developing functional textiles with thermal insulation and hydrophobic properties is of significant interest. This study successfully synthesizes K2Ti6O13 (potassium titanate, KTO) whiskers via the hydrothermal method and prepares KTO composite silica polyester fabric (KSP) through impregnation technology, exhibiting excellent thermal insulation and hydrophobic qualities. X-ray diffraction (XRD) analysis verifies the high purity and excellent crystallinity of KTO whiskers and SiO2 micro-powder. Scanning electron microscopy (SEM) images reveal that the KTO whiskers retain their original morphology and exhibit uniform size distribution, with an average length of around 1 μm and an aspect ratio of 40. Transmission electron microscopy (TEM) images further validate the planar growth properties of the whiskers. Raman spectroscopy research elucidates the vibrational modes of various chemical bonds in the KTO whiskers. The ultraviolet–visible–near-infrared spectrophotometer test results demonstrate that the KSP fabric reflects 43.5 % more light than standard polyester fabric and substantially lowers the temperature in the covered chamber under simulated sunlight exposure, achieving a maximum reduction of 7.4 °C. The KSP fabric has exceptional hydrophobic properties, completing a contact angle of 153.2° and maintaining reflectance stability, with a mere 5.92 % reduction after 20 days of outside exposure. This work offers substantial reference value for the advancement of practical textiles. © 2025

关键词： Bond strength (chemical)

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

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arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Li, Bai Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Na, Xiaoxiang Li, Zixuan Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd United Kingdom BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The College of Mechanical and Vehicle Engineering Hunan University China The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The Centre for Sustainable Road Freight University of Cambridge United Kingdom The School of Mechanical Engineering Tsinghua University China The College of Artificial Intelligence Xi’an Jiaotong University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

Assignment of Freight Traffic in a Large-Scale Intermodal Network under Uncertainty

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arXiv 2021年

作者： Uddin, Majbah Huynh, Nathan N. Ahmed, Fahim National Transportation Research Center Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States Department of Civil and Environmental Engineering University of Nebraska-Lincoln 2200 Vine St 262D LincolnNE68583-0851 United States Associate System Performance Engineer South Carolina Department of Transportation 955 Park St ColumbiaSC29202 United States

This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, and Hurricane Harvey) severely disrupted the U.S. freight transportation network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider three types of natural disasters that have different risks and impacts on transportation networks: earthquakes, hurricanes, and floods. It is found that for all disaster scenarios, freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario;this is because there are more states in the flood-risk areas, and they are scattered throughout the U.S. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

Self-Healing Performance and Prediction Model of Microcapsule Asphalt

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SSRN

SSRN 2021年

作者： Li, Jia Ji, Xiaoping Fang, Xiangzheng Hu, Yonglin Hua, Wenlong Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Shanghai China School of Highway Chang’an University Xi’an710064 China Jinhua Highway and Transportation Management Center Jinhua321013 China Jindong District Highway Management Section of Jinhua Jinhua321014 China

To deeply investigate the evolution process and influence factors of self-healing behavior of microcapsule asphalt, a fatigue–healing–fatigue test was performed using a dynamic shear rheometer. The ratio of complex dynamic modulus decay rate before and after asphalt healing (HI) was employed to evaluate the self-healing performance of microcapsule asphalt;in addition, the influence of healing time and temperature, damage degree, microcapsule content, and asphalt type on HI were investigated. The micro mechanism of repairing asphalt microcracks by microcapsules was revealed via fluorescence microscopy. The results showed that HI increased nonlinearly with the increase in healing time, healing temperature, and microcapsule content and decreased nonlinearly with the increase in damage degree. The self-healing rate and HI of a microcapsule-containing SBS modified asphalt were higher than those of microcapsule matrix asphalt. The stress at the tip of the microcrack could pierce the shell of the microcapsule and release the core material to repair the microcrack in asphalt through wetting and molecular diffusion. Finally, based on the polymer self-healing theory, prediction models for the self-healing performance of microcapsule asphalt were established. The models could accurately predict the HI values under different healing times, healing temperatures, damage degrees, and microcapsule contents. The models provide a basis for designing microcapsule asphalt mixture. © 2021, The Authors. All rights reserved.

关键词： Forecasting

Malicious mode attack on EV coordinated charging load and MIADRC defense strategy

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arXiv 2021年

作者： Zhou, Yichen Liu, Weidong Ma, Jing Zhen, Xinghao Li, Yonggang Electrical and Electronic Engineering Department North China Electric Power University No. 619 Yonghua North Road Baoding071003 China State Grid Tianjin Electric Power Company Tianjin300000 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University No. 2 Beinong Road Beijing102206 China China Institute of Energy and Transportation Integrated Development North China Electric Power University No. 2 Beinong Road Beijing102206 China

The Internet of Things (IoT) provides a salient communication environment to facilitate the coordinated charging of electric vehicle (EV) load. However, as IoT is connected with the public network, the coordinated charging system is in a low-level cyber security and greatly vulnerable to malicious attacks. This paper investigates the malicious mode attack (MMA), which is a new cyber-attack pattern that simultaneously attacks massive EV charging piles to generate continuous sinusoidal power disturbance with the same frequency as the poorly-damped wide-area electromechanical mode. Thereby, high amplitude forced oscillations could be stimulated by MMA, which seriously threats the power system stability. First, the potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system, and an MMA process like Mirai is pointed out as an example. And then, based on the attack process, an MMA model is established for impact analysis where expressions of the mean and stochastic responses of the MMA forced oscillation are derived to discover main impact factors. Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability of the power system, which considers the impact factors of both mode damping and disturbance compensation. Simulations are conducted to verify the existence and characteristics of MMA threats, and the efficiency of the proposed defense strategy is also validated. © 2021, CC BY.

关键词： Stochastic systems

Data-driven control loop performance evaluation of electromagnetic levitation systems

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Data-driven control loop performance evaluation of electroma...

Chinese Automation Congress (CAC)

作者： Yifeng Song Fei Ni Guobin Lin Junqi Xu Laisheng Tong Chen Chen The Key Laboratory of Road and Traffic Engineering Tongji University Shanghai China Maglev Transportation Engineering R&D Center Tongji University Shanghai China Maglev System Research Institute CRRC Zhuzhou Locomotive Co. Ltd. Zhuzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Electromagnetic levitation system is the core component of maglev trains, and the performance of its control loop directly affects the stability, safety and riding comfort. After a long time of operation, the control loop performance of the electromagnetic levitation system will be reduced from its initial condition, due to changes in external environment, actuator failure, etc. Therefore, monitoring and evaluating the control loop performance is of great significance to ensure a smooth operation of the maglev train. In this paper, the concept and framework of control loop performance evaluation are introduced to the electromagnetic levitation system for the first time; and then the fundamental for control loop performance evaluation based on data-driven methods is presented; with the operational data of commercial maglev train, performances of different evaluation methods are tested and compared. In addition, by taking advantages of the geometrical significance of covariance matrix, evaluation results of the control loop performance can be visualized in a compact and intuitive fashion.

关键词： Magnetic levitation vehicles Levitation Performance evaluation Indexes Research and development roads Electromagnetics