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ABSTRACT

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This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.

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The aim of this study is to find a suitable methodology for planning the locations of intermodal terminals in an urban transit context. The location planning approach, which has been developed and makes this possible, consists of three phases. The first phase is the making of the geographic information system (GIS) database which enables determining the potential locations of intermodal terminals. For every potential location of the terminal, the number of citizens gravitating to a certain terminal is calculated, which at the same time represents the output from the first phase of the model. The second phase uses an optimization algorithm in order to determine the locations of the intermodal terminals. The optimization algorithm provides several solutions for a different number of terminals, and such solutions need to be evaluated. The main contribution of this research is in upgrading the location planning approach by introducing an additional step in assessing the solutions obtained by the optimization algorithm.

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