A fuzzy logic-based method for designing an urban transport network using a shark smell optimisation algorithm

Transportation is a significant issue due to providing people to participate in human activities. Due to an increase in population, the need for transportation has also been increased. Therefore, more traffic is visible on streets that produce more issues related to mobility like noise pollution, ai...

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Bibliographic Details
Published inJournal of experimental & theoretical artificial intelligence Vol. 34; no. 4; pp. 673 - 694
Main Author Nazif, Habibeh
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 04.07.2022
Taylor & Francis Ltd
Subjects
Algorithms
Fuzzy Logic
Network design
Noise pollution
Optimisation
Optimization
Road design
Shark Smell Algorithm
Transportation networks
Transportation planning
Travel time
Urban areas
Urban Management
Urban Transport Network
Urban transportation
Online AccessGet full text
ISSN0952-813X
1362-3079
DOI10.1080/0952813X.2021.1924867

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Summary:Transportation is a significant issue due to providing people to participate in human activities. Due to an increase in population, the need for transportation has also been increased. Therefore, more traffic is visible on streets that produce more issues related to mobility like noise pollution, air pollution, and accidents. This study pays attention to an impressive transit network design in urban areas. Because of the NP-hard nature of this problem, a shark smell optimisation (SSO) algorithm based on fuzzy logic is employed. A developed system is utilised to produce, optimise, and analyse frequencies and routes of transit in the level of a network. Its target is maximising the direct travellers per unit length, i.e., subject to route length, direct traveller density, and nonlinear rate constraints (a route length ratio to the shortest road interval between the beginning and destination). Since designing an urban transport network issue is in heterogeneous environments is involved, this article provides a new method for lowering the feasible urban travel time, the urban traffic, and the feasible urban travel cost using a well-known SSO algorithm. According to the results, the proposed method has higher efficiency compared to the previous methods. In addition, the results showed that the proposed technique offers fewer transfers and travel time.
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ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2021.1924867