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Traffic crashes are the major public health problem, leading to cause of death, injury and disability around the world. Statistical record states that, despite of insignificant decline in traffic crashes during the year 2011, the issue of traffic safety still remains acute in India. It was observed that there were around 4,98,000 traffic crashes, in which 1,42,485 people were killed and more than 5,00,000 persons were injured in the year 2011. More than 90 per cent of these deaths occur in low-income and middle-income countries, which have less than half of the world’s vehicles. This is due to the reason that they lack in effective road safety strategies. Therefore, this study attempts to analyze the factors affecting the crash severity and crash frequency in urban mid-blocks. Safety performance measures are those parameters which influences the traffic crashes that should be constantly maintained and checked for Urban mid-blocks. Severity prediction models can be developed which aids in developing safety performance indicators which work as an effective tool for the evaluation of the effectiveness of potential safety treatments for the roads. Hence, Mixed modelling can handle correlated data and unequal variances. The methodology includes the identification of study area, data collection, preparation of database, modelling, interpretation of results obtained from the modelling and to derive the conclusions. Adequate treatment measures like reduction in speed of the vehicles, imposition of strict lane discipline, incorporation of proper driving skills to the road users were suggested to reduce the traffic crashes at mid-block sections.
"Development Of Safety Performance Measures For Urban Mid-Blocks", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 8, page no.101 - 107, August-2017, Available :http://www.ijrti.org/papers/IJRTI1708018.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator