Optimal Modification of Urban Bus Network Routes Using a Genetic Algorithm

Amiripour, S M Mahdi; Mohaymany, Afshin Shariat; Ceder, Avishai

doi:10.1061/(asce)te.1943-5436.0000741

Cited by 24 publications

(19 citation statements)

References 34 publications

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“…In addition, hybrid DE-PSO achieves lower average T wt and T tr as compared with hybrid PSO-DE. Furthermore, both algorithms produced solutions in which the (9) set current particle � 1 st particle in swarm (10) select a particle randomly (except the selected 1 st particle) in the swarm (11) apply particle modification scheme to generate a modified particle (repair if infeasible) (12) fitness evaluation using passenger assignment on the modified particle (13) if modified particle is better than personal best (14) update personal best and its fitness (15) else if modified particle is better than global best (16) update global best and its fitness (17) end if (18) end for (19) (1) Generate N p candidate route set using construction heuristic with iSRR repair mechanism (2) for i ≔ 1 to N p (3) fitness evaluation using passenger assignment model (4) end for (5) for n ≔ 1 to G (6) for i ≔ 1 to N p (7) set Target vector � X i,n (8) select randomly a vector (except the selected Target vector, X i,n ) in the population (9) apply identical point mutation to generate a Noisy Random vector, V i,n (repair if infeasible) (10) apply uniform route crossover between X i,n and V i,n to generate a pair of Trial vectors, U i,n (repair if infeasible) (11) fitness evaluation of U i,n using passenger assignment model (12) elitism selection (13) if Trial vector fitness ≤ Target vector fitness (14) new_population Table 6.…”

Section: Comparison Of Hybrid De-pso and Hybrid Pso-dementioning

confidence: 99%

“…In particular, Ngamchai and Lovell [10] and Fan and Machemehl [11] utilized a GA on theoretical networks, while Arbex and da Cunha [12] experimented on Mandl's Swiss network. Furthermore, Gundaliya et al [13] and Tom and Mohan [14] studied medium-size network in Chennai, India, while Amiripour et al [15] studied a network in Iran. More recently, Owais and Osman [16] experimented a GA on a transportation network in Rivera City, Northern Uruguay.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Differential Evolution‐Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

Buba

Lee

2019

Mathematical Problems in Engineering

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This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.

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Section: Comparison Of Hybrid De-pso and Hybrid Pso-dementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Differential Evolution‐Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

Buba

Lee

2019

Mathematical Problems in Engineering

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“…It is possible to do this using PRIVOL as well, but not in the exactly optimal manner. Moreover, it is not certain that, comparing with [16], such a high percentage of the existing lines remains intact. Using PRIVOL in this case requires sensitive changes of the initial "candidate" set of routes L 0 .…”

Section: Cost Minimization In the Case Of One System With A Homogeneomentioning

confidence: 99%

Alternate Criteria in LP Solutions of Public Transport Line Planning

Černý

Pesko

Černá

2019

PROMET

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In the paper, the public transportation line planning means planning of routes and frequencies of vehicles on them. In the world literature, different criteria are used in this context; mainly the variable costs of lines, the fixed costs of lines, the fixed plus variable costs of lines, the number of direct travellers, the total or average riding time and the total or average travelling time. The current paper adds two more: the total number of used vehicles (to be minimized when all passengers are transported) and relative excess of supply over demand (to be maximized without exceeding the number of available vehicles). Basic mathematical models for both cases are presented and the motivation of such approach is described including a brief excursion into the history of the Czech and Slovak research of line planning where the use of these objectives has arisen. Further, the basic models were modified for the cases of fourteen special practical requirements, e.g. heterogeneous vehicle fleet (= rolling stock), limitation of transfers, elastic demand, limited total number of lines, etc. The brief outline of the experience with practical use is added as well.

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“…GA is widely used to find optimal solutions by examining only a small fraction of the possible candidates (Senouci & Al-Derham, 2008). It can also be used as a filter to screen out unwanted paths in order to find the optimal/ near-optimal solution (Mahdi Amiripour, Mohaymany, & Ceder, 2014). Other evolution-inspired techniques, such as swarm intelligence, ant colony optimization, particle swarm optimization, hill-climbing, bee algorithm-simulated annealing, and tabu search, are considered as effective alternatives to exact algorithms for solving complex optimization problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of artificial intelligence methodologies and algorithms into the civil engineering curriculum using knowledge-based expert systems: A case study

Wanyan

Chen

Olowokere

2017

Engineering Education Letters

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The aim of this study was to strengthen the engineering curriculum by integrating innovative electrical and computer engineering (ECE)-specialized artificial intelligence (AI) methodologies and algorithms into traditional civil engineering (CE) problem-solving methods. An interactive and comprehensive knowledge-based expert system (KBES) was developed to document, compare, and analyze cutting-edge AI applications in the field of CE. With a large amount of successful/unsuccessful AI applications being tried and tested in the CE field, this unique intelligent database can be used as the platform and educational media for the development and implementation of curricula for the problembased learning approach to bridge the gap in the current curricula between conventional mathematics, physics, engineering methods and state-of-the-art AI techniques. This study is the first of its kind to (1) develop an intelligent KBES platform to increase the intellectual rigor, breadth, and depth of undergraduate engineering study and lay a foundation for students pursuing master's degree or PhD in engineering; (2) establish a new interdisciplinary AI curriculum as a capstone course, enrich existing curricula by integrating case studies of AI applications into different levels of undergraduate CE courses, and include knowledge automation software in an ECE course; (3) foster Cite this article as: Wanyan Y, Chen X, Olowokere D. Integration of artificial intelligence methodologies and algorithms into the civil engineering curriculum using knowledge-based expert systems: A case study, Engineering Education Letters 2017:3

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Optimal Modification of Urban Bus Network Routes Using a Genetic Algorithm

Cited by 24 publications

References 34 publications

Hybrid Differential Evolution‐Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

Hybrid Differential Evolution‐Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

Alternate Criteria in LP Solutions of Public Transport Line Planning

Integration of artificial intelligence methodologies and algorithms into the civil engineering curriculum using knowledge-based expert systems: A case study

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