38th CONGRESS OF THE EUROPEAN REGIONAL SCIENCE ASSOCIATION B6 (MONDAY, 31.AUG.1998, 11:30- 13:00). A New Approach for Transport Network Design and Optimization (original) (raw)
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A new approach for transport network design and optimization
ERSA conference …, 1998
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 evaluated by means of a suitable software package. For each new solution some indicators will be calculated. A unique FF will be finally evaluated by means of a multicriteria method. Altough the research is still in a preliminary stage, the emerging first results concerning numerical cases show very good perspectives for this new approach. A test in real cases will also follow.
Genetic algorithms in bus network optimization
2002
This paper focuses on a new method to compute ®tness function () values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus networks). Each member of the population is evaluated by computing a number of performance indicators obtained by the analysis of the assignment of the O/D demand associated to the considered networks. Thus, values are computed by means of a multicriteria analysis executed on the performance indicators so found. The goal is to design a heuristic that allows to achieve the best bus network satisfying both the demand and the oer of transport. Ó (M. Bielli), caramia@disp.uniroma2.it (M. Caramia), carotenuto@disp. uniroma2.it (P. Carotenuto).
A new methodology for the public transport network design
The 9th International Conference "Environmental Engineering 2014", 2014
The present paper deals with the bus network design problem. Such problem is formulated as an optimization problem involving the minimization of all resources and costs related to the public transport system. The optimization problem is subject to user equilibrium on public transport network as well as to the bus capacity constraints and a set of feasibility constraints on route length and line frequency. The objective function is defined as the sum of operator's costs and users' costs. The input data are the public transport demand matrix, the characteristics of road network, the operating and users unit costs. Outputs are routes and frequencies for the lines of the public transport network. The performances of the network are estimated by a hyperpath transit assignment model, which reproduces the choice behaviour of transit users. The solving procedure consists of a set of heuristics, which includes a first routine for the definition of the roads and the zones to be served, a second step for the routes generation and then a genetic algorithm for finding a sub-optimal set of routes and associated frequencies. The GA is implemented in the C# language as a parallel genetic algorithm while the fitness evaluation requires computing, for each solution generated, the two terms of the objective function by simulating the public transport network with the EMME software. The proposed procedure will be implemented on a real large size network (two districts in the city of Rome), in order to compare its effectiveness with the performances of the existing transit network and to provide an extensive sensitivity analysis in bus frequency changes.
Optimizing Bus Lines Using Genetic Algorithm for Public Transportation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021
Abstract. Due to increasing human population, the need for quality public transportation has also increased. This study takes stop density, stop layout, and passenger population of those stops into consideration to offer a better regulated public transportation network design that can satisfy the increased demand. In this study, the boarding data is provided by the public transportation department of the city of Antalya, Turkey. Remaining required data was automatically generated using web services and stored in a PostgreSQL database hosted on a cloud server. After visualizing inputs such as bus routes, stop layout, and passenger density on Google Maps and KeplerGL, with the use of the K-Means algorithm, data was clustered to find ”hot” (i.e. attraction) areas on a macro scale. A novel means of connecting hot spots suggested by the outcome of the Genetic Algorithm was developed. To compare the effectiveness of the proposed approach with the existing network, current bus stops were m...
Transportation, 2019
Evolutionary algorithms have been used extensively over the past 2 decades to provide solutions to the Transit Network Design Problem and the Transit Network and Frequencies Setting Problem. Genetic algorithms in particular have been used to solve the multi-objective problem of minimizing transit users' and operational costs. By finding better routes geometry and frequencies, evolutionary algorithms proposed more efficient networks in a timely manner. However, to the knowledge of the authors, no experimentation included precise and complete pedestrian network data for access, egress and transfer routing. Moreover, the accuracy and representativeness of the transit demand data (Origin Destination matrices) are usually generated from fictitious data or survey data with very low coverage and/or representativity. In this paper, experiments conducted with three medium-sized cities in Quebec demonstrate that performing genetic algorithm optimizations using precise local road network data and representative public transit demand data can generate plausible scenarios that are between 10 and 20% more efficient than existing networks, using the same parameters and similar fleet sizes.
Optimization of Transfer Time and Initial Waiting Time for a Bus Network Using Genetic Algorithm
Genetic algorithms are search algorithm based on natural selection and genetics. It is first pioneered by John Holland in the 60’s and based on the Charles Darwin’s principle of “survival of the fittest”. The genetic algorithm method differs from other search methods in that it searches among a population of points and works with a coding of parameters set, rather than the parameter values themselves. GA Randomly generates an initial population of individual and a fitness function is used to evaluate individuals, and reproductive success varies with fitness. In this study optimization of Transfer Time (TT) and Initial Waiting Time (IWT) of existing city bus schedule of Indore city bus network for a given constraint is done. Problem has been formulated in the form of objective function by considering departure and arrival time of buses on different routes as variables and constraint. Data has been collected from Indore City Transport Services Limited (ICTSL) and problem is formulated for various buses and routes. Data based on particular route are considered as input for objective function and constraint. The same is considered as input of GA in MATLAB in the form of X1, X2, ..., Xn parameters and is optimized in the form of minimization problem. Based on the data considered and result obtained verification for minimization of TT & IWT has been done. It is concluded that GA can be considered as a tool to solve optimization problem for city bus route of Indore city.
A Genetic Algorithm Based Bus Scheduling Model for Transit Network
… of the Eastern Asia Society for …, 2005
Farhan Ahmad KIDWAI Lecturer Department of Civil Engineering University of Malaya 50603 Kuala Lumpur Malaysia Fax: +60-3-7967-5318 E-mail: farhan@um.edu.my ... Kalyanmoy DEB Professor Department of Mech. Engineering Indian Institute of Technology Kanpur - ...
Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design
Proc. Transport Systems Telematics Conf. TST 2010 , 2010
A new method − combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.
Evolutionary Modeling of Large-Scale Public Transport Networks
2018
1 A genetic algorithm to design efficient large-scale public transport networks is extended. It goes 2 beyond existing approaches by incorporating a dynamic demand response towards both changes 3 in the network and external disruptions. The algorithm is based on an agent-based (MATSim) 4 simulation and tested for the city of Zurich. Compared to the existing public transport system, it 5 proposes a sparser network with substantially higher frequencies. By doing so, the algorithm 6 predicts a higher transit ridership at a lower level of subsidies, thus increasing the effectiveness 7 of public transportation. Moreover, it reliably identifies corridors for potential capacity upgrades. 8 The approach may help transport planners to assess their existing public transport networks and 9 to plan public transport infrastructure for the era of automated vehicles. 10 Manser, P., H. Becker, S. Hörl and K.W. Axhausen 2
Transit network design by genetic algorithm with elitism
The transit network design problem is concerned with the finding of a set of routes with corresponding schedules for a public transport system. This problem belongs to the class of NP-Hard problem because of the vast search space and multiple constraints whose optimal solution is really difficult to find out. The paper develops a Population based model for the transit network design problem. While designing the transit network, we give preference 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 Genetic Algorithm (GA) optimization. The Genetic Algorithm is similar to evolution strategy which iterates through fitness assessment, selection and breeding, and population reassembly. In this paper, we will show two different experimental results performed on known benchmark problems. We clearly show that results obtained by Genetic Algorithm with increasing population is better than so far best technique which is really difficult for future researchers to beat.