Инструкция tpsim

инструкция tpsim
Though crashes involving pedestrians were not reported, the potential for risk is higher with AVI technology. There were no delays in the ETC lanes. The data collected was analyzed to compare measures of effectiveness prior to and after plaza expansion. The RFB neural network was the best model for analyzing driver injury severity.


Merging and sideswipe collisions increased during Stage 3 as a result of the introduction of a new toll payment method (dedicated E-PASS lane). This was due to increased weaving and merging as a result of an additional choice in lane type. Data collection of the toll plaza was achieved by utilizing video cameras to capture queuing delay, service time, queue length and throughput. In addition, a distance-measuring instrument (DMI) was installed on five different vehicles that passed through the plaza during the morning peak hour. The model was calibrated based on inputs obtained from the NYS Bridge Authority to develop a base scenario. The group of signs indicates the type and location of the payment lanes. The inter-vehicle time is the difference between two consecutive vehicular departure times at the toll plaza at a specific lane. An overall average inter-vehicle time was derived by calculating the mean of all lanes for each lane type.

The average inter-vehicle time for the ETC lanes was reduced by almost 50 percent as a result of the plaza improvements. Rather, there are six lanes in each direction consisting of two manual lanes, two automatic lanes and two dedicated express ETC lanes. The estimated property damage rate was used to represent crash severity. This is due to the fact that there are more lanes on the approach side than the departure side. As was estimated, the probability of conflict was highest during the shoulder peak hours. The width of ETC lanes should be large enough to accommodate heavy trucks equipped with an ETC transponder.

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