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Background: Time to hospital after a road traffic crash (RTC) plays a vital role in determining the outcome for crash victims. In Delhi, there are seven designated trauma centres where crash victims are typically taken, which may not be nearest hospital. We compare the transport time access (crash to hospital) depending on whether the victim is transported to a designated trauma centre or the nearest hospital. Data and methods: For each RTC geocoded manually from police records, the nearest hospital and the designated trauma centre is identified using Google Maps places nearby Search API and guidelines. Travel time matrix is generated between RTC's and identified hospitals using Google maps distance matrix API. Index accounting inter-district differences is developed. Results and conclusions: The network of designated trauma centres in New Delhi is located such that they can be accessed within 45 min of most crashes while nearest hospital within 30 min. As a result, the vast majority of crash victims are likely to receive timely care if they are rapidly transferred to either of these caregivers. However, for the most severely injured and time-sensitive cases, bypassing nearest hospital for trauma care, could substantially improve survival outcomes. [ABSTRACT FROM AUTHOR]

Hit-and-run crashes are significant concern for many countries. Due to lack of information of offending vehicles it is difficult to understand dynamics of these crashes to have a prevention plan. The paper aims to identify the impacting vehicle in hit-and-run crashes. We studied fatal road crashes of New Delhi for eleven years (2006–2016) and found that approximately 40% fatal crashes are hit-and-run with unknown impacting vehicles. We proposed a framework using eleven different machine learning-based classification algorithms – Logistic-Regression, KNN, SVM-Linear and RBF-Kernel, Naïve-Bayes, Random-Forest, DecisionTree, AdaBoost, Multilayer-Perceptron, CART and Linear-Discriminant-Analysis. We found SVM-linear-kernel gave best results. Results reveal that cars, buses, and heavy vehicles are involved vehicles in hit-and-run crashes. Buses were primary cause leading to 39% of hit-and-run during 2006-2009 thereafter cars increased drastically. Our framework is robust and scalable to any city. The outcomes provide inputs to traffic engineers for better policy prescription and road user safety. [ABSTRACT FROM AUTHOR]