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Box 1 Example assessment and calculation

From: Recommendations for actions concerning supporting ITS developments for VRUs

The recommendation ‘designing traffic light control depending on traffic demand, taking into account VRU needs’ (R7) is used as an example. The waiting times for all users are expected to decrease, through optimisation of the traffic light control. The recommendation will logically affect the Intelligent Pedestrian Traffic signal (IPT) system positively. This is confirmed by the participants of the workshop held by the VRUITS project, as it scored highest in the topic HMI & acceptance. In this case, IPT is the only ITS that is expected to be affected by the recommendation, however, most of the time, multiple ITS systems are affected by a recommendation. A qualitative assessment of the effects of the recommendation on the system has been made:  
Safety: The number of red-light running accidents is expected to decrease as annoyance is decreased due to shorter waiting times. In addition, it indirectly decreases the number of annoyance related accidents. This all leads to a very small increase in safety.
Mobility: Is only marginally increased, due to optimised waiting times.
Comfort: Optimisation of algorithms increases comfort very little for all road users.
Penetration rate: Improvement of algorithms makes the system more mature and reliable, resulting in the ability to install these systems in a greater variety of (traffic) situations. Thus a small increase in penetration rate can be expected.
Cost: An accelerated deployment may reap economies of scale benefits, leading to a small increase in discount rate.
Recommendation R7 has a long time horizon and for that reason the effects are estimated to only take place beyond 2020. Linking this to Table 2, the parameter values are thus easily determined (Table 3).
The parameter values are then used in the CBAs. For Safety, Mobility and Comfort, this is done as described earlier, multiplying the respective benefits with the given percentage. The increase in penetration rates is calculated by using the figures for the medium-usage scenario in 2020, and the high-usage scenario in 2030. In the end, the difference with the original CBA output and the new CBA output is multiplied with ¼ to account for the corresponding “small” effect. The original CBA output is €160.9 mln, the new CBA output is €897.3 mln. Note that this new output seems fairly large in comparison with the original, but that’s because in this form it represents the benefits for a large increase in penetration rate. Multiplying the difference with ¼ gives the correct value (values in mln €): \( \frac{1}{4}\ast \left(897.3-160.9\right)=184.1 \). In this case, ¼ of the difference of the original CBA results and new one is between €100 mln and €1 bn, meaning the potential benefits are in the ‘++’ category.