The influence of high traffic density on transport times was substantial in the 1-TC scenarios (5-7) and in the 2-TC scenario with 2 TCs that are geographically near to each other (scenario 4), compared with the current situation with 3 TCs in the region (scenario 1) and the situation with 2 geographically well-spread TCs (scenarios 2 and 3). As mentioned before, the Dutch government set a time limit of approximately 20 minutes for transporting severely injured patients to the nearest TC. ![]() This study showed that the transport time in the geographically less well spread 2-TC scenario (scenario 4) and in the 1-TC scenarios (scenario 5-7) exceeds the maximum time with 4 to 10 minutes. Although in all scenarios roughly 98% of the population could reach the hospital within 45 minutes in both rush hour and low traffic, the transport time for the population living in the region’s periphery did increase substantially. The transport times for these patients would, in fact, be shorter if they were to be brought to the nearest TC in an adjacent region.Ī major strength of this study is that the outcomes of the model were validated, using factual data of a large number of high-urgency cases, largely publicly available data, and commercially available GIS-based technology. Although we validated the model, we must emphasize that the results are based on a mathematical model, which is of course a simplification of the real world. ![]() ![]() For example, instead of using the exact geographic coordinates of the accident scenes, the zip codes were used as a proxy. In our opinion, these 4-digit, 2-letter zip codes are an acceptable proxy because these codes cover areas of a few streets at most, meaning that the actual accident scene is in close proximity. Another limitation is that the data used to validate the model outcomes may have contained erroneous transport times that could not be corrected, which is illustrated by some extremely short or very long transport times in the database provided by the RAS.
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