No abstract

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.

runs, using the TRANSYT-7F simulation engine. Each simulation run is assigned a unique signal timing plan by the optimization processor. The optimizer applies the Hill-Climbing (HC) or Genetic Algorithm (GA) searching strategies. The trial simulation run resulting in the best performance is reported as optimal. Although the GA is mathematically better suited for determining the absolute or global optimal solution, relative to HC optimization, it generally requires longer program running times, relative to HC optimization [4]. This chapter proposes Ant Colony Optimization (ACO) based algorithm called ACORSES proposed by [5] for finding optimum signal parameters in coordinated signalized networks for given fixed set of link flows. The ACO is the one of the most recent techniques for approximate optimization methods. The main idea is that it is indirect local communication among the individuals of a population of artificial ants. The core of ant's behaviour is the communication between the ants by means of chemical pheromone trails, which enables them to find shortest paths between their nest and food sources. This behaviour of real ant colonies is exploited to solve optimization problems. The proposed algorithm is based on each ant searches only around the best solution of the previous iteration with reduced search space. It is proposed for improving ACO's solution performance to reach global optimum fairly quickly. In this study, for solving the ATC problem, Ant Colony Optimization TRANSYT (ACOTRANS) model is developed. TRANSYT-7F traffic model is used to estimate total network PI.

A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Markov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is generated around the best solution found in the feasible solution space and compared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the results obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and performed better than many of the optimization methods to which it was compared in terms of the robustness measures adopted.

ÖzTraffic congestion is one of the major problems in transportation field. To reduce unfavorable impact of that problem, one of the conventional applications is to find optimal or near-optimal configuration of one-way streets on road networks. When some of two-way streets in the network are converted to one-way, which may be cheaper than other possible improvements, the performance of the road system may increase. Thus, this arrangement should be evaluated in order to determine its possible effects. For this purpose, a bilevel heuristic solution algorithm is proposed to find optimal configuration of one-way streets on road networks in this study. The upper level deals with finding optimal configuration of one-way streets by minimizing the total flow (demand) weighted shortest path travel costs (distance) while user equilibrium link flows are determined in the lower level. Shortest path travel costs between OD pairs are obtained after executing traffic assignment module of VISUM software by considering link travel times according to created network configuration in the upper level. To make more attractive one-way streets, we have used the parameter of α which is multiplied with the length of one-way streets to increase its speed. The bilevel heuristic solution algorithm is combined with VISUM and applied to Sioux-Falls city network. The results of near-optimal arrangement of one-way streets are compared with those of base case in terms of objective function considered. Additionally, sensitivity analysis was performed to investigate how the algorithm reacts to the variation of the parameter of α. Results showed that developed algorithm may be considered for finding optimal configuration of one-way streets on urban road networks.