Rewarding instead of charging road users: a model case study investigating effects on traffic conditions

Instead of giving a negative incentive such as transport pricing, a positive incentive by rewarding travelers for ‘good behavior’ may yield different responses. In a Dutch pilot project called Peak Avoidance (in Dutch: “SpitsMijden”), a few hundred travelers participated in an experiment in which they received 3 to 7 euros per day when they avoided traveling by car during the morning rush hours (7h30–9h30). Mainly departure time shifts were observed, together with moderate mode shifts. Due to the low number of participants in the experiment, no impact on traffic conditions could be expected. In order to assess the potential of such a rewarding scheme on traffic conditions, a dynamic traffic assignment model has been developed to forecast network wide effects in the long term by assuming higher participation levels. This paper describes the mathematical model. Furthermore, the Peak Avoidance project is taken as a case study and different rewarding strategies with varying participation levels and reward levels are analyzed. First results show that indeed overall traffic conditions can be improved by giving a reward, where low to moderate reward levels and participation levels of 50% or lower are sufficient for a significant improvement. Higher participation and reward levels seem to become increasingly counter-effective

Optimising Differentiated Tolls on Large Scale Networks, by using an Intellegent Search Algorithm

The design of an optimal road pricing scheme is not a trivial problem. Following the Dutch government’s kilometre charge plans, this paper focuses on the optimisation of link based toll levels differentiated in space and time. The optimal toll level design problem is formulated as a bi-level mathematical program. In the upper level we minimise an object function, e.g. the average travel time in the network, using a fixed number of price categories. At the lower level a dynamic traffic assignment model is used to determine the effects of differentiated road pricing schemes on the traffic system. Focus of the paper is on the upper-level where optimal toll levels are approximated. In the optimisation procedure different variants of a pattern search algorithm are tested in a case study. Inspection of the solution space shows that many local minima exist, so the selection of the initial solution becomes important. In the case study however it appears that in all local minima the value of the objective function is almost the same, indicating the fact that many different toll schemes result in the same average travel time. The case study is also used to test the performance of the different variants of the pattern search algorithm. It appears that it is beneficial to change more variables at a time and to use a memory to remember where improvement of the objective function has been made. First tests on a medium scale network showed that it is possible to apply the framework on this network, though further computational improvements are needed to apply the framework to large scale networks, for example by parallel processing