A hybrid genetic algorithm for road (original) (raw)
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A hybrid genetic algorithm for road congestion minimization
2009
ABSTRACT One of the main goals in a transportation planning process is to achieve solutions for two classical problems: the traffic assignment problem, which minimizes the total travel delay among all travelers, and the toll pricing problem which settles, based on data derived from the first problem, the tolls that would collectively benefit all travelers and would lead to a user equilibrium solution. Acquiring precision for this framework is a challenge for large networks.
A biased random-key genetic algorithm for road congestion minimization
Optimization Letters, 2010
One of the main goals in transportation planning is to achieve solutions for two classical problems, the traffic assignment and toll pricing problems. The traffic assignment problem aims to minimize total travel delay among all travelers. Based on data derived from the first problem, the toll pricing problem determines the set of tolls and corresponding tariffs that would collectively benefit all travelers and would lead to a user equilibrium solution. Obtaining high-quality solutions for this framework is a challenge for large networks. In this paper, we propose an approach to solve the two problems jointly, making use of a biased random-key genetic algorithm for the optimization of transportation network performance by strategically allocating tolls on some of the links of the road network. Since a transportation network may have thousands of intersections and hundreds of road segments, our algorithm takes advantage of mechanisms for speeding up shortest-path algorithms.
On the minimization of traffic congestion in road networks with tolls
Annals of Operations Research, 2015
Population growth and the massive production of automotive vehicles have lead to the increase of traffic congestion problems. Traffic congestion today is not limited to large metropolitan areas, but is observed even in medium-sized cities and highways. Traffic engineering can contribute to lessen these problems. One possibility, explored in this paper, is to assign tolls to streets and roads, with the objective of inducing drivers to take alternative routes, and thus better distribute traffic across the road network. This assignment problem is often referred to as the tollbooth problem and it is NPhard. In this paper, we propose mathematical formulations for two versions of the tollbooth problem that use piecewise-linear functions to approximate congestion cost. We also apply a biased random-key genetic algorithm on a set of real-world instances, analyzing solutions when computing shortest paths according to two different weight functions. Experimental results show that the proposed piecewise-linear functions approximate the original convex function quite well and that the biased random-key genetic algorithm produces high-quality solutions.
Fixed and Variable Toll Pricing in Road Networks with Direct Search Meta-Heuristics
Lecture Notes in Economics and Mathematical Systems, 2009
This study describes the use of a Direct Search (DS) metaheuristic algorithm for solving the fixed and variable toll design problem, in terms of finding (adequately near-)optimum toll level(s), in roads operated by private, profitmaximizing firms. The problem is formulated as a nonconvex, bilevel nonlinear mathematical program, which seeks to maximize toll revenues while taking into account the travel responses of network users, through a multi-class stochastic user equilibrium traffic assignment model with elastic demand. The algorithm is implemented onto a real-life urban sub-network which includes a private highway. The results of the case study show the ability of the DS algorithm to relatively quickly converge to a solution and signify its potential to provide a competitive alternative to the currently used genetic algorithm (GA) approach for solving such types of nonconvex bilevel programs in the sector of road transport services. 4, c Springer-Verlag Berlin Heidelberg 2009 45 46 L. Dimitriou and T. Tsekeris which possibly deviate from the welfare maximization. Such cases give rise to the imposition of "second-best" tolls . In some cases, the "second-best" pricing strategies are designed by Private Public Partnerships (PPPs) under Build-Operate-Transfer (BOT) concession schemes. Such schemes allow private investors to build new highways and operate them by collecting toll charges for a given number of years (concession period), sufficient to attain an agreed level of investment benefit, and then transfer them to the government. This study adopts the design strategy of the firm, which is oblivious to social welfare and aims at maximizing its own revenue (profit), in order to payback the investment costs and afford the operation and maintenance costs of the road services.
Routing in Road Networks: the toll booth problem
2012
Due to population growth and the massive production of automotive vehicles, traffic congestion problems have become larger and more common. This is a reality that governments are facing everywhere, even in medium sized cities that were not used to this scenario. However, despite the growth of the number of vehicles, traffic congestion can be lessened using different strategies. One possibility, that is explored in this research, is assigning tolls to roads, inducing users to take alternative paths, and thus better distributing the traffic across the road network. This problem is called the toll booth problem and is NP-hard. We propose mathematical formulations for variations of the toll booth problem, using two piecewise linear functions to approximate the congestion cost. We test these models on a set of real-world instances, and apply a previously proposed genetic algorithm to all instances. The experimental results show that the proposed piecewise linear functions approximates th...
A study of both optimal locations and toll levels road pricing using genetic algorithm
This paper takes advantage of a GA based Bi-Level Programming Problem to find the optimal scheme of second-best road pricing problem, the method optimizes locations and toll levels simultaneously. The highlight of this paper is that the case study was taken on the actual network of Nagoya Metropolitan Area instead of a numeral network, in order to evaluate the effect of the method, we made comparison between a cordon pricing scheme and the same number of links tolled optimal locations and toll levels schemes. The result showed that under the situation of the same number of links, the optimal locations and toll levels scheme works better than the cordon pricing one.
Evolutionary minimization of traffic congestion
Proceedings of the Genetic and Evolutionary Computation Conference, 2021
Traffic congestion is a major issue that can be solved by suggesting drivers alternative routes they are willing to take. This concept has been formalized as a strategic routing problem in which a single alternative route is suggested to an existing one. We extend this formalization and introduce the Multiple-Routes problem, which is given a start and destination and aims at finding up to different routes that the drivers strategically disperse over, minimizing the overall travel time of the system. Due to the NP-hard nature of the problem, we introduce the Multiple-Routes evolutionary algorithm (MREA) as a heuristic solver. We study several mutation and crossover operators and evaluate them on real-world data of Berlin, Germany. We find that a combination of all operators yields the best result, improving the overall travel time by a factor between 1.8 and 3, in the median, compared to all drivers taking the fastest route. For the base case = 2, we compare our MREA to the highly tailored optimal solver by Bläsius et al. [ATMOS 2020] and show that, in the median, our approach finds solutions of quality at least 99.69 % of an optimal solution while only requiring 40 % of the time. CCS CONCEPTS • Computing methodologies → Heuristic function construction; Randomized search.
2016
ABSTRACT: The solution of the transportation network optimization problem actually requires, in most cases, very intricate and powerful computer resources, so that it is not feasible to use classical algorithms. One promising way is to use stochastic search techniques. In this context, Genetic Algorithms (GAs) seem to be- among all the available methodologies- one of the most efficient methods able to approach transport network design and optimization. Particularly, this paper will focus the attention on the possibility of modelling and optimizing Public Bus Networks by means of GAs. In the proposed algorithm, the specific class of Simple GAs (SGAs) and Cumulative GAs (CGAs) will be used for solving the first level of the network optimization problem, while a classical assignment model,or alternatively a neural network approach,will be adopted for the Fitness Function (FF) evaluation. CGAs will then be utilized in order to generate new populations of networks, which will be evaluate...
Elastic Multi-User Stochastic Equilibrium Toll Design with Direct Search Meta-Heuristics
This study describes the use of a Direct Search (DS) meta-heuristic algorithm for solving the toll design problem, in terms of finding the optimum toll level, in private roads. The problem is formulated as a nonconvex, bilevel nonlinear mathematical program which seeks to maximize toll revenues while taking into account the travel responses of network users, through a multi-class stochastic user equilibrium traffic assignment model with elastic demand. The algorithm is implemented onto a real-life urban subnetwork which includes a private highway. The results show the ability of the DS algorithm to relatively quickly find an optimal solution and signify its potential to provide a competitive alternative to the currently used genetic algorithm (GA) approach for solving such types of nonconvex bilevel programs in the sector of road transport services.