Sampling bias and weight factors for in-depth motorcycle crash data in Thailand |
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Institution: | 1. Department of Civil and Infrastructure Engineering, Asian Institute of Technology, 58, Khlong Luang, Pathumthani 12120, Thailand;2. Research and Development, Autoliv Japan Ltd., 1764-12 Kamiinayoshi, 315-8520 Kasumigaura, Japan;3. Autoliv Research, Wallentinsvägen 22, 44783 Vårgårda, Sweden |
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Abstract: | Motorcycle crashes are documented in Thailand's national records but are underreported and lacking detail. In-depth motorcycle crash data, collected by Thailand Accident Research Center (TARC), contains a smaller number of motorcycle crashes but more detail. However, to draw conclusions at a national level, representativeness of the TARC in-depth data is currently unknown, and the correction of sampling biases may be required. In this study, the Capture-recapture method was used to examine the underreporting in the national crash data (from the government insurance company). It was found that 69% of fatal and 70% of non-fatal injuries were underreported, respectively. The in-depth crash data was found to be biased. The weighting methods post-stratification and iterative proportional fitting were applied to compensate for the bias and are shown to improve the representativeness of the in-depth motorcycle crash data. Weighted in-depth crash data appears to be suitable to draw conclusions on motorcyclist safety in Thailand. |
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