Data-driven exploration of risk riding behaviors and traffic accident outcomes in Thailand

The dataset encompasses responses from 496 validated participants, representing a nationwide sample across Thailand. It provides a detailed six-month retrospective analysis of motorcycle riding behaviors and traffic incident experiences. The survey instrument was meticulously crafted to capture nuanced information on risky riding practices and their consequential outcomes. Key components of the dataset are structured as follows: 1. Socio-demographic Profile: This section presents a comprehensive array of variables including gender, age stratification, educational attainment, marital status, income brackets, occupational categories, geographical areas of motorcycle usage, licensing information, motorcycle engine specifications, typical riding schedules, daily riding durations, cumulative riding experience, and self-assessed proficiency in traffic regulations. 2. Risk Factors and Outcomes (6-month retrospective): a) Traffic Violations: Quantified on a frequency scale ranging from "Never" to "Over 2 times" b) Accident Occurrences: Documented both in terms of incidence and frequency c) Injury Severity: Categorized on a spectrum from "Never" to "Moderate injuries requiring hospitalization for observation" 3. Unsafe Riding Behaviors: This section comprises an extensive 39-factor assessment of various risky riding practices. Respondents were asked to quantify their engagement in each behavior using a 6-point Likert scale, where 1 signifies "never" and 6 indicates "always", allowing for a granular analysis of behavior frequency. This dataset offers a rich resource for researchers, policymakers, and road safety advocates, providing detailed insights into the complex landscape of motorcycle safety in Thailand.本数据集涵盖496名经过验证的受访者反馈数据，其样本覆盖泰国全国范围，包含对摩托车骑行行为与交通事故经历的六个月回顾性详细分析。本次调研工具经过精心设计，旨在精准捕捉高危骑行行为及其后续后果的精细化信息。 本数据集的核心组成部分结构如下： 1. 社会人口学特征（Socio-demographic Profile）：该板块包含一系列全面的变量指标，涵盖性别、年龄分层、受教育程度、婚姻状况、收入区间、职业类别、摩托车使用地域范围、驾驶资质信息、摩托车发动机规格、典型骑行时段、每日骑行时长、累计骑行经验，以及受访者自评的交通法规熟悉程度。 2. 危险因素与后果（六个月回顾性）： a) 交通违法情况：采用从“从未”到“超过2次”的频率量表进行量化 b) 事故发生情况：从发生率与发生频率两方面进行记录 c) 受伤严重程度：按从“未受伤”到“需住院观察的中度受伤”的梯度进行分类 3. 不安全骑行行为：该板块包含针对各类高危骑行行为的39项因子评估。受访者需通过6级李克特量表（Likert scale）对自身参与每项行为的频率进行量化，其中1代表“从未”，6代表“总是”，以此实现对行为发生频率的精细化分析。 本数据集为研究人员、政策制定者与道路安全倡导者提供了丰富的研究资源，可助力深入洞察泰国摩托车交通安全领域的复杂现状。