TY - JOUR
T1 - Operating cost and quality of service optimization for multi-vehicle-type timetabling for urban bus systems
AU - Peña, D.
AU - Tchernykh, A.
AU - Nesmachnow, S.
AU - Massobrio, R.
AU - Feoktistov, A.
AU - Bychkov, I.
AU - Radchenko, G.
AU - Drozdov, A.Y.
AU - Garichev, S.N.
N1 - Cited By :14
Export Date: 27 August 2021
CODEN: JPDCE
Correspondence Address: Tchernykh, A.; CICESE Research Center, Mexico; email: chernykh@cicese.mx
Funding details: Russian Foundation for Basic Research, RFBR, 16-07-00931, 18-07-01224-a
Funding details: Consejo Nacional de Ciencia y TecnologÃa, CONACYT, 178415
Funding details: Federal Agency for Scientific Organizations, 0348-2017-0010
Funding text 1: The work is partially supported by RFBR , project no. 16-07-00931 , 18-07-01224-a , Federal Agency of Scientific Organizations (FASO) , project no. 0348-2017-0010 , and CONACYT, México , grant no. 178415 .
PY - 2019
Y1 - 2019
N2 - In this paper, we propose a timetable optimization method based on a Multiobjective Cellular genetic algorithm to tackle the multiple vehicle-type problems. The objective is to determine bus assignment in each time period to optimize a quality of service and transport operating cost. The quality of service, represented by the unsatisfied user demand, guarantees a good experience in terms of comfort, safety, availability, improving effects on how passengers perceive wait times. The operational cost contributes to reducing the traffic jams, the flux of unfilled vehicles and fuel consumption, helping to diminish the negative environmental impact. With the operation data of Los Angeles bus route 217 northbound, at peak and off-peak hours, we obtain a set of non-dominated solutions that represent different assignments of vehicles covering a given set of trips in a defined route. The experimental analysis based on several quality indicators, like Hypervolume, Spread, ε-Indicator, and Set Coverage, indicates that our algorithm is a competitive technique comparing with well-known techniques presented in the literature. © 2018 Elsevier Inc.
AB - In this paper, we propose a timetable optimization method based on a Multiobjective Cellular genetic algorithm to tackle the multiple vehicle-type problems. The objective is to determine bus assignment in each time period to optimize a quality of service and transport operating cost. The quality of service, represented by the unsatisfied user demand, guarantees a good experience in terms of comfort, safety, availability, improving effects on how passengers perceive wait times. The operational cost contributes to reducing the traffic jams, the flux of unfilled vehicles and fuel consumption, helping to diminish the negative environmental impact. With the operation data of Los Angeles bus route 217 northbound, at peak and off-peak hours, we obtain a set of non-dominated solutions that represent different assignments of vehicles covering a given set of trips in a defined route. The experimental analysis based on several quality indicators, like Hypervolume, Spread, ε-Indicator, and Set Coverage, indicates that our algorithm is a competitive technique comparing with well-known techniques presented in the literature. © 2018 Elsevier Inc.
KW - Evolutionary algorithms
KW - Metaheuristics
KW - Multiobjective optimization
KW - Multiple vehicle types
KW - Public transport
KW - Smart cities
KW - Buses
KW - Costs
KW - Environmental impact
KW - Genetic algorithms
KW - Operating costs
KW - Scheduling
KW - Smart city
KW - Traffic congestion
KW - Cellular genetic algorithms
KW - Experimental analysis
KW - Meta heuristics
KW - Nondominated solutions
KW - Optimization method
KW - Quality indicators
KW - Vehicle types
KW - Quality of service
U2 - 10.1016/j.jpdc.2018.01.009
DO - 10.1016/j.jpdc.2018.01.009
M3 - Article
SN - 0743-7315
VL - 133
SP - 272
EP - 285
JO - J. Parallel Distrib. Comput.
JF - J. Parallel Distrib. Comput.
ER -