This paper explores how advanced reservations, coupled with dynamic pricing (based on booking limits) can be used to maximize parking revenue. An integer programing formulation that maximizes parking ...revenue over a system of garages is presented. Furthermore, an intelligent parking reservation model is developed that uses an artificial neural network procedure for online reservation decision-making. Finally, the paper provides some strategic and managerial implications of multi-garage revenue management systems, and discusses techniques for identifying and implementing micro-market segmentation in the parking industry.
Bee colony optimization (BCO) is a relatively new meta-heuristic designed to deal with hard combinatorial optimization problems. It is biologically inspired method that explores collective ...intelligence applied by the honey bees during nectar collecting process. In this paper we apply BCO to the
p-center problem in the case of symmetric distance matrix. On the contrary to the constructive variant of the BCO algorithm used in recent literature, we propose variant of BCO based on the improvement concept (BCOi). The BCOi has not been significantly used in the relevant BCO literature so far. In this paper it is proved that BCOi can be a very useful concept for solving difficult combinatorial problems. The numerical experiments performed on well-known benchmark problems show that the BCOi is competitive with other methods and it can generate high-quality solutions within negligible CPU times.
•We study the transit network design problem.•We propose the simple greedy algorithm for generating the initial solution.•We develop the model based on the Bee Colony Optimization (BCO) metaheuristic ...to discover the best transit network topology.•The numerical experiments are performed on the known benchmark problems.•The obtained numerical results show that the proposed approach can find high-quality solutions.
The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate high-quality solutions.
•The fuzzy logic models (type 1 and type 2) for bus priority decisions are developed.•The models consider connected intersections in actuated-time control mode.•Car and bus passengers delay are the ...main performance measures.•The models are implemented in a VISSIM environment.•The proposed approach is tested on real-life intersections.
We proposed in this paper Type-2 Fuzzy Logic-based transit priority strategy. The developed strategy belongs to the class of active priority strategies. At the same time, it is a conditional priority strategy. We tried to find out the finest trade-off between transit and traffic delay. The suggested approach used the data collected from the VISSIM analysis. The simulation tests were performed on the section of an arterial with two intersections in downtown Belgrade, Serbia. The obtained results are promising. It looks that the developed strategy could improve traffic operations in cities, or areas, with high bus frequencies and/or low or medium traffic volume.
•The transit network design problem with simultaneous frequency setting is considered in the paper.•Greedy heuristic and Bee Colony Optimization (BCO) metaheuristic are developed for the problem ...considered.•The numerical experiments are performed on the well-known benchmark problem.•The proposed model outperformed the other models known from the literature.
The transit network design problem belongs to the class of hard combinatorial optimization problem, whose optimal solution is not easy to find out. We consider in this paper the transit network design problem in a way that we simultaneously determine the links to be included in the transit network, assemble chosen links into bus routes, and determine bus frequency on each of the designed routes. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristic. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm is competitive with the other approaches in the literature and that can generate high-quality solutions.
After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification ...of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naïve Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason’s score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models’ performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.
This article formulates the Collaborative Gate Allocation (CGA) problem as a novel concept to reduce aircraft gate-waiting time and to increase aircraft gate utilization. The proposed CGA concept ...assumes voluntary collaboration and negotiation among airlines and airport operators, enabling them to share real-time information on gate-utilization and gate-sharing. The proposed policy is especially applicable to U.S. airports, as an alternative to exclusive or preferential gate-sharing practices, at terminal buildings that accommodate a large number of smaller airlines during periods of high congestion and delays caused by high traffic demands, bad weather conditions, or any ad hoc situations. The CGA problem belongs to the class of complex combinatorial optimization problems, whose optimal solution is difficult to discover. This article develops a Swarm Intelligence-based model for the CGA problem. Our approach to solving the CGA problem is based on the Bee Colony Optimization (BCO) metaheuristic. The BCO algorithm belongs to the class of population-based algorithms. This technique uses a similarity between the way in which bees in nature look for nectar, and the way in which optimization algorithms search for an optimum solution to a combinatorial optimization problem. Numerical experiments are performed using Denver International Airport as a real-world case study. We show that the proposed CGA problem can be efficiently solved by our BCO algorithm, and that applications of the CGA concept can significantly reduce gate delays.
Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual “agents” and that each agent interacts with other agents according to localized knowledge. ...Special kinds of artificial agents are the agents created by analogy with social insects. Social insects (bees, wasps, ants, and termites) have lived on Earth for millions of years. Their behavior is primarily characterized by autonomy, distributed functioning, and self-organizing capacities. Social insect colonies teach us that very simple organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. Swarm intelligence is the branch of artificial intelligence based on study of behavior of individuals in various decentralized systems. The paper presents a classification and analysis of the results achieved using swarm intelligence (SI) to model complex traffic and transportation processes. The primary goal of this paper is to acquaint readers with the basic principles of Swarm Intelligence, as well as to indicate potential swarm intelligence applications in traffic and transportation.
The superstreet intersection (or restricted crossing U-turn-, J-turn intersection) fixed-time traffic control system was developed in this study. The optimal (or near-optimal) values of cycle length, ...splits, and offsets were discovered by minimizing the experienced travel time of all network users traveling through the superstreet intersection. The optimization procedure used was based on the bee colony optimization (BCO) metaheuristic. The BCO is a stochastic, random-search, population-based technique, inspired by the foraging behavior of honey bees. The BCO belongs to the class of swarm intelligence methods. A set of numerical experiments was performed. Superstreet intersection configurations that allowed direct left turns from the major street, as well as configurations with no direct left turns, were analyzed within numerical experiments. The obtained results showed that BCO outperformed the traditional Webster approach in the superstreet geometrical configurations considered.
•We considered the Area-wide urban traffic control problem.•For this problem we developed control system based on the Bee Colony Optimization.•Numerical experiments were performed on well-known ...traffic network.•Obtained results were compared with the results obtained by Simulated Annealing (SA) metaheuristic.•It has been shown that the proposed BCO approach outperforms the SA algorithm.
The paper describes a new method of optimizing traffic signal settings. The area-wide urban traffic control system developed in the paper is based on the Bee Colony Optimization (BCO) technique. The BCO method is based on the principles of the collective intelligence applied by the honeybees during the nectar collecting process. The optimal (or near-optimal) values of cycle length, offsets, and splits are discovered by minimizing the total travel time of all network users travelling through signalized intersections. The set of numerical experiments is performed on well-known traffic benchmark network. The results obtained by the BCO approach are compared with the results found by Simulated Annealing (SA). It has been shown that the suggested BCO approach outperformed the SA.
PDF
