This study reports on the effect of demand variation on the optimal location of road-pricing cordons. The optimal road-pricing cordon, in this study, aims to maximise the…
This study reports on the effect of demand variation on the optimal location of road-pricing cordons. The optimal road-pricing cordon, in this study, aims to maximise the social welfare function. This optimisation program is categorised as Bi-level optimisation programming which is a NP hard problem. The paper first describes the method developed to solve the optimal toll problem for a given set of chargeable links. The tests were carried out with a small toy network and a larger scale network. For the small network, four single user class demand characteristics were varied individually; these were the elasticity of trip generation with respect to increases in travel cost, value of travel time, volume of traffic, and traffic distribution pattern. For the larger scale network, only elasticity, value of time, and trip volume were tested. The results of the larger scale network are also analysed by including the cost per toll point. The tests with the larger scale network were modified so that the constraint of uniform charge is applied. The results showed that demand variation could influence the best location of toll points. This finding raises the question of whether the implementation of the same cordon all day in an urban traffic network is the optimal approach under the existence of demand variations by time of day, and also whether the evaluation process of the cordon location should consider the effect of different time periods together.
In this paper, we analyze statistical properties of stated choice experimental designs when model attributes are functions of several design attributes. The scheduling model is taken as an example. This model is frequently used for estimating the willingness to pay (WTP) for a reduction in schedule delay early and schedule delay late. These WTP values can be used to calculate the costs of travel time variability. We apply the theoretical results to the scheduling model and design the choice experiment using measures of efficiency (S-efficiency and WTP-efficiency). In the simulation exercise, we show that the designs based on these efficiency criteria perform on average better than the designs used in the literature in terms of the WTP for travel time, schedule delay early, and schedule delay late variables. However, the gains in efficiency decrease in the number of respondents. Surprisingly, the orthogonal design performs rather well in the example we demonstrated.