Abstract
A real-time road pricing system in the case of a two-link parallel network is proposed in this paper. The system that is based on a combination of Dynamic Programming and Neural Networks makes “on-line” decisions about road toll values. In the first phase of the proposed model, the best road toll sequences during certain time period are calculated off-line for many different patterns of vehicle arrivals. These toll sequences are computed using Dynamic Programming approach. In the second phase, learning from vehicle arrival patterns and the corresponding optimal toll sequences, neural network is trained. The results obtained during on-line tests are close to the best solution obtained off-line assuming that the arrival pattern is known. Scope and purpose The basic idea behind the concept of congestion pricing is to force drivers to travel and use transportation facilities more during off-peak hours and less during peak hours, as well as to increase the usage of underutilized routes. In this paper, congestion pricing that offers variable tolls to road users based on the time of day and level of traffic is analyzed. There is a need to develop a more concrete methodology to establish time-variable tolls that will optimize the objectives of stakeholders. This research proposes the methodology that can calculate in real-time, appropriate amount of toll to be charged based on the time of day, traffic volumes, value of time distributions and other user and system variables.
Published Version
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