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Methodology to Derive National Estimates of Injuries and Fatalities in Road Traffic Crashes in India

机译:导出国家伤害和道路交通崩溃的国家估计的方法论

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The Road Accident Sampling System - India (RASSI) accident database being developed by an international consortium of manufacturers and safety researchers is currently India’s only source of in-depth crash data. The database includes information on accident, vehicle, and driver factors associated with each crash, which is collected through on-scene crash investigations conducted by trained crash investigators, from four key sample regions (Coimbatore, Pune, Ahmedabad, and Kolkata). As the RASSI database continues to grow, the next step is to ensure that the sample data can be reliably extrapolated to the whole of India. This paper is an initial attempt to develop national estimates by crash type based on a few sampling locations currently being investigated by the RASSI teams in India. RASSI data was treated as a stratified sample of Indian accidents, and the locations, where the crash data is being collected, were considered as primary sampling units. The “mark and recapture” statistical procedurefor population estimation was used to derive sampling weights by accident type and injury severity. Sampling weights were derived by comparing RASSI data with the police reported data from the sampling units for the same period. The weights were based on several factors, including crash types (single-/multiple-vehicle), injury severity, crash location (urban/rural) and type of road user (pedestrian/motorized two-wheeler/car). Data from police logs and RASSI were matched by selected strata (injury type/accident type), and the estimate of total population for that stratum was calculated using well-established statistical methods. Then, national estimates of the various single-vehicle accident types (collisions with fixed objects, rollover, pedestrian, motorcycle) and multiple-vehicle accident types (head-on, rear, side impact, and sideswipe) were derived. Driver contributing factors and consequences were also estimated. The derived estimates at an aggregate level were compared with published sources including MoRTH data to determine and improve adequacy and validity of the weights.
机译:道路事故采样系统 - 印度(Rassi)由国际美联社制造商和安全研究人员开发的意外数据库目前是印度唯一的深入崩溃数据。该数据库包括有关与每次崩溃相关的事故,车辆和驱动因素的信息,这些崩溃通过由培训的崩溃调查人员(CoimBatore,Pune,Ahmedab​​ad和Kolkata)从培训的崩溃调查人员进行的现场碰撞调查收集。随着RASSI数据库继续增长,下一步是确保样品数据可以可靠地推断到整个印度。本文是根据目前正在印度的RASSI团队调查的一些抽样场所通过碰撞类型制定国家估计的初步尝试。 Rassi数据被视为印度事故的分层样本,以及收集碰撞数据的位置被认为是主要采样单元。为群体估计的“标记和重新捕获”统计程序用于通过意外类型和伤害严重程度导出取样权重。通过将Rassi数据与警察报告的数据从采样单位进行同一时期,通过与警察报告的数据进行比较来得出的采样权重。重量基于若干因素,包括崩溃类型(单/多车辆),伤害严重程度,崩溃位置(城市/农村)和道路使用类型(行人/机动两轮车/汽车)。来自警察日志和Rassi的数据与选定的地层(伤害类型/事故类型)匹配,使用良好的统计方法计算了该层的总人口的估计。然后,衍生出各种单车事故类型(与固定物体,翻转,行人,摩托车)和多载体事故类型(正面,后,侧面撞击和侧面影响)的国家估计。还估计了司机贡献因素和后果。将聚合水平的衍生估计与公布的来源进行比较,包括Morth数据,以确定和改善重量的充分性和有效性。

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