Optimizations of network layout and transport service frequencies in view of interests of transit line operators and utilizers

• Autorzy: Zhu Xiaojing , Feng Xuesong , Zhang Lukai , Hua Weixin

Identyfikatory

DOI

10.5604/01.3001.0013.5619

• Języki publikacji: EN

Abstrakty

EN

Layouts of bus networks in cities are always irrational currently, transport service frequencies also need to be optimized according to the real network layouts, operation conditions and travel experience of passengers, so it is essential to optimize bus transit network layouts and transport service frequencies systematically. Different stakeholders are involved in the optimization of urban bus transit network layouts like the government, operators and passengers, whose interests are always contradictory. In order to optimize transit network layout and service frequencies from the view point of operators and utilizers, this research constructs a multi-objective model and proposes a solution algorithm. The proposed multi-objective model is established from the perspective of operators with the goal of minimizing total operating costs for one day, and from the perspective of the utilizers to minimize the total travel time, respectively. Also with the application of electric bus in cities, buses in this research are electric buses all for green travel. Moreover, a solution algorithm is proposed in this research to solve the proposed multi-objective model with simulated annealing algorithm and genetic algorithm. Simulated annealing algorithm is used as the main framework of the solution algorithm from the perspective of operators to minimize operating costs, while genetic algorithm is used as the subroutine of simulated annealing algorithm to optimize total travel time. Verification of the proposed model and the solution algorithm is based on an intuitive network. The application results of a numerical experiment verified that the proposed optimization model and the solution algorithm are able to optimize the network layout and service frequencies at the same time.

Słowa kluczowe

EN

transit network layout design; transportation service; service optimization; interests of transit line operators; interests of utilizers; simulated annealing algorithm; genetic algorithm

PL

projekt układu sieci transportowej; usługi transportowe; optymalizacja usług; interesy operatorów linii tranzytowych; interes użytkowników; algorytm genetyczny

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2019
• Tom: Vol. 50, iss. 2
• Strony: 47--55
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Zhu Xiaojing

• Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China

autor

Feng Xuesong

• Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China

autor

Zhang Lukai

• Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China

autor

Hua Weixin

• Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China

Bibliografia

• [1] ARBEX, R. O., da CUNHA, C. B., 2015. Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm. Transportation Research Part B: Methodological. 81, part. 2, 355-376.
• [2] BELTRAN, B., CARRESE, B., CIPRIANI, E., et al., 2009. Transit network design with allocation of green buses: A genetic algorithm approach. Transportation Research Part C: Emerging Technologies, 17(5), 475-483.
• [3] BUBA, A. T., LEE, L. S., 2018. A differential evolution for simultaneous transit network design and frequency setting problem. Expert Systems with Applications, 106, 277-289.
• [4] CANCA, D., DELOS-SANTOS, A., LAPORTE, G. MESA, J. A., 2017. An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem. Computers and Operations Research, 78, 1-14.
• [5] CHAKROBORTY, P., 2003. Genetic algorithms for optimal urban transit network design. Computer‐Aided Civil and Infrastructure Engineering, 18(3), 184-200.
• [6] CIPRIANI, E., GORI, Z. PETRELLI, M., 2012. Transit network design: A procedure and an application to a large urban area. Transportation Research Part C: Emerging Technologies, 20(1), 3-14.
• [7] FENG, X., ZHU, X., QIAN, X., et al., 2019. A new transit network design study in consideration of transfer time composition. Transportation Research Part D: Transport and Environment, 66, 85-94.
• [8] ILEWICZ, G., HARLECKI, A., 2018. Multi-objective optimization of a medical robot model in transient states. Scientific Journal of Silesian University of Technology. Series Transport, 99, 79-88.
• [9] GUTIÉRREZ-JARPA, G., LAPORTE, G., MARIANOV, V., et al., 2017. Multi-objective rapid transit network design with modal competition: The case of Concepción, Chile. Computers and Operations Research, 78, 27–43.
• [10] LIU, Y., FENG, X., JIA, Y., WU, J., 2018. Bi-objective optimization of transit network and frequencies design using Pareto genetic algorithm. Advances in Transportation Studies: An International Journal, 46, 43-56.
• [11] LÓPEZ-RAMOS, F., CODINA, E., MARÍN, A., et al., 2017. Integrated approach to network design and frequency setting problem in railway rapid transit systems. Computers and Operations Research, 80, 128-146.
• [12] MANDL, C. E., 1979. Evaluation and optimization of urban public transportation network. European Journal of Operational Research, 5, 396–404.
• [13] NIKOLIĆ, M., TEODOROVIĆ, D., 2013. Transit network design by Bee Colony optimization. Expert Systems with Applications, 40(15), 5945-5955.
• [14] NIKOLIĆ, M., TEODOROVIĆ, D., 2014. A simultaneous transit network design and frequency setting: Computing with bees. Expert Systems with Applications, 41(15), 7200-7209.
• [15] OWAIS, M., OSMAN, M. K., 2018. Complete Hierarchical Multi-objective Genetic Algorithm for Transit Network Design Problem. Expert Systems with Applications, 114, 143-154.
• [16] SOTO, G., LARRAIN, H., MUÑOZ, J. C., 2017. A new solution framework for the limited-stop bus service design problem. Transportation Research Part B: Methodological, 105, 67-85.
• [17] ZHAO, H., HE, R., SU, J., 2018. Multi-objec-tive optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections. Archives of Transport, 46(2), 85-97.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)