Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling

D. Pena; A. Tchernykh; G. Radchenko; S. Nesmachnow; J. Ley-Flores; R. Nazariega

doi:10.1109/ICEnT.2017.31

Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling

D. Pena, A. Tchernykh, G. Radchenko, S. Nesmachnow, J. Ley-Flores, R. Nazariega

Research output: Contribution to conference (No Proceedings) › Paper › peer-review

Abstract

This paper presents a multiobjective cellular genetic algorithm to determine bus timetables using multiple vehicle types, considering restrictions of government agencies for public transport systems in the context of smart cities. The first objective is to reduce the greenhouse gas emissions by the minimization of number of vehicles wasting fuel transiting with low ridership. The second one is to minimize number of passengers that cannot move in a certain time-period increasing vehicles overload and waiting time. A set of non-dominated solutions represents different assignments of vehicles covering a given set of trips in a defined route. Our experimental analysis shows a competitive performance of the proposed algorithm in terms of convergence and diversity. It outperforms non-dominated sets provided by NSGA-n. © 2017 IEEE.

Original language	English
Pages	114-118
Number of pages	5
DOIs	https://doi.org/10.1109/ICEnT.2017.31
Publication status	Published - 2017
Event	4th International Conference on Engineering and Telecommunication - Moscow, Russian Federation Duration: 25 Nov 2020 → …

Conference

Conference	4th International Conference on Engineering and Telecommunication
Abbreviated title	En and T 2017
Country/Territory	Russian Federation
City	Moscow
Period	25/11/20 → …

Keywords

evolutionary algorithms
greenhouse gas
metaheuristics
multiobjective optimization
public transport
Evolutionary algorithms
Genetic algorithms
Greenhouse gases
Mass transportation
Multiobjective optimization
Quality of service
Scheduling
Urban transportation
Vehicles
Cellular genetic algorithms
Competitive performance
Experimental analysis
Meta heuristics
Nondominated solutions
Public transport
Public transport systems
Urban Public Transport
Gas emissions

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10.1109/ICEnT.2017.31

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049257636&doi=10.1109%2fICEnT.2017.31&partnerID=40&md5=fbf4a8fac4aee02d24a9a309220c0eee

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Pena, D., Tchernykh, A., Radchenko, G., Nesmachnow, S., Ley-Flores, J., & Nazariega, R. (2017). Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling. 114-118. Paper presented at 4th International Conference on Engineering and Telecommunication, Moscow, Russian Federation. https://doi.org/10.1109/ICEnT.2017.31

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title = "Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling",

abstract = "This paper presents a multiobjective cellular genetic algorithm to determine bus timetables using multiple vehicle types, considering restrictions of government agencies for public transport systems in the context of smart cities. The first objective is to reduce the greenhouse gas emissions by the minimization of number of vehicles wasting fuel transiting with low ridership. The second one is to minimize number of passengers that cannot move in a certain time-period increasing vehicles overload and waiting time. A set of non-dominated solutions represents different assignments of vehicles covering a given set of trips in a defined route. Our experimental analysis shows a competitive performance of the proposed algorithm in terms of convergence and diversity. It outperforms non-dominated sets provided by NSGA-n. {\textcopyright} 2017 IEEE.",

keywords = "evolutionary algorithms, greenhouse gas, metaheuristics, multiobjective optimization, public transport, Evolutionary algorithms, Genetic algorithms, Greenhouse gases, Mass transportation, Multiobjective optimization, Quality of service, Scheduling, Urban transportation, Vehicles, Cellular genetic algorithms, Competitive performance, Experimental analysis, Meta heuristics, Nondominated solutions, Public transport, Public transport systems, Urban Public Transport, Gas emissions",

author = "D. Pena and A. Tchernykh and G. Radchenko and S. Nesmachnow and J. Ley-Flores and R. Nazariega",

note = "Conference code: 134065 Cited By :3 Export Date: 27 August 2021 Funding details: Russian Foundation for Basic Research, РФФИ, 16-07-00931 Funding details: Consejo Nacional de Ciencia y Tecnolog{\'i}a, CONACYT, 178415 Funding details: Council on grants of the President of the Russian Federation, NSh-8081.2016.9 Funding text 1: The work is partially supported by RFBR, project no. 16-07-00931, Council for Grants of the President of Russian Federation, project NSh-8081.2016.9, and CONACYT, M{\'e}xico, grant no. 178415.; 4th International Conference on Engineering and Telecommunication, En and T 2017 ; Conference date: 25-11-2020",

year = "2017",

doi = "10.1109/ICEnT.2017.31",

language = "English",

pages = "114--118",

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Pena, D, Tchernykh, A, Radchenko, G, Nesmachnow, S, Ley-Flores, J & Nazariega, R 2017, 'Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling', Paper presented at 4th International Conference on Engineering and Telecommunication, Moscow, Russian Federation, 25/11/20 pp. 114-118. https://doi.org/10.1109/ICEnT.2017.31

Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling. / Pena, D.; Tchernykh, A.; Radchenko, G. et al.
2017. 114-118 Paper presented at 4th International Conference on Engineering and Telecommunication, Moscow, Russian Federation.

Research output: Contribution to conference (No Proceedings) › Paper › peer-review

TY - CONF

T1 - Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling

AU - Pena, D.

AU - Tchernykh, A.

AU - Radchenko, G.

AU - Nesmachnow, S.

AU - Ley-Flores, J.

AU - Nazariega, R.

N1 - Conference code: 134065 Cited By :3 Export Date: 27 August 2021 Funding details: Russian Foundation for Basic Research, РФФИ, 16-07-00931 Funding details: Consejo Nacional de Ciencia y Tecnología, CONACYT, 178415 Funding details: Council on grants of the President of the Russian Federation, NSh-8081.2016.9 Funding text 1: The work is partially supported by RFBR, project no. 16-07-00931, Council for Grants of the President of Russian Federation, project NSh-8081.2016.9, and CONACYT, México, grant no. 178415.

PY - 2017

Y1 - 2017

N2 - This paper presents a multiobjective cellular genetic algorithm to determine bus timetables using multiple vehicle types, considering restrictions of government agencies for public transport systems in the context of smart cities. The first objective is to reduce the greenhouse gas emissions by the minimization of number of vehicles wasting fuel transiting with low ridership. The second one is to minimize number of passengers that cannot move in a certain time-period increasing vehicles overload and waiting time. A set of non-dominated solutions represents different assignments of vehicles covering a given set of trips in a defined route. Our experimental analysis shows a competitive performance of the proposed algorithm in terms of convergence and diversity. It outperforms non-dominated sets provided by NSGA-n. © 2017 IEEE.

AB - This paper presents a multiobjective cellular genetic algorithm to determine bus timetables using multiple vehicle types, considering restrictions of government agencies for public transport systems in the context of smart cities. The first objective is to reduce the greenhouse gas emissions by the minimization of number of vehicles wasting fuel transiting with low ridership. The second one is to minimize number of passengers that cannot move in a certain time-period increasing vehicles overload and waiting time. A set of non-dominated solutions represents different assignments of vehicles covering a given set of trips in a defined route. Our experimental analysis shows a competitive performance of the proposed algorithm in terms of convergence and diversity. It outperforms non-dominated sets provided by NSGA-n. © 2017 IEEE.

KW - evolutionary algorithms

KW - greenhouse gas

KW - metaheuristics

KW - multiobjective optimization

KW - public transport

KW - Evolutionary algorithms

KW - Genetic algorithms

KW - Greenhouse gases

KW - Mass transportation

KW - Multiobjective optimization

KW - Quality of service

KW - Scheduling

KW - Urban transportation

KW - Vehicles

KW - Cellular genetic algorithms

KW - Competitive performance

KW - Experimental analysis

KW - Meta heuristics

KW - Nondominated solutions

KW - Public transport

KW - Public transport systems

KW - Urban Public Transport

KW - Gas emissions

U2 - 10.1109/ICEnT.2017.31

DO - 10.1109/ICEnT.2017.31

M3 - Paper

SP - 114

EP - 118

T2 - 4th International Conference on Engineering and Telecommunication

Y2 - 25 November 2020

ER -

Multiobjective optimization of greenhouse gas emissions enhancing the quality of service for urban public transport timetabling

Abstract

Conference

Keywords

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