zbMATH — the first resource for mathematics

The multimodal and multiperiod urban transportation integrated timetable construction problem with demand uncertainty. (English) Zbl 1412.90093
Summary: The urban transport planning process has four main activities: Network design, Timetable construction, Vehicle scheduling and Crew scheduling; each activity has subactivities. In this paper the authors work with the subactivities of timetable construction: minimal frequency calculation and departure time scheduling. The authors propose to solve both subactivities in an integrated way. The developed mathematical model allows multi-period planning and it can also be used for multimodal urban transportation systems. The authors consider demand uncertainty and the authors employ fuzzy programming to solve the problem. The authors formulate the urban transportation timetabling construction problem as a bi-objective problem: to minimize the total operational cost and to maximize the number of multi-period synchronizations. Finally, the authors implemented the SAUGMECON method to solve the problem.

MSC:
 90C11 Mixed integer programming 90-08 Computational methods for problems pertaining to operations research and mathematical programming
Full Text:
References:
 [1] P. Avila; F. López, Two multiobjective metaheuristics for solving the integrated problem of frequencies calculation and departures planning in an urban transport system, Annals of Management Science, 3, 29, (2014) [2] R. Baskaran; K. Krishnaiah, Simulation model to determine frequency of a single bus route with single and multiple headways, Int. J. Business Performance and Supply Chain Modelling, 4, 40, (2012) [3] L. Cadarso; A. Marín, Integration of timetable planning and rolling stock in rapid transit networks, Annals of Operations Research, 199, 113, (2012) · Zbl 1251.90082 [4] L. Campos; J. L. Verdegay, Linear programming problems and ranking of fuzzy numbers, Fuzzy Sets and Systems, 32, 1, (1989) · Zbl 0674.90061 [5] P. Chakroborty, Genetic algorithms for optimal urban transit network design, Computer-Aided Civil and Infrastructure Engineering, 18, 184, (2003) [6] H. Chen, Stochastic optimization in computing multiple headways for a single bus line, Proceedings of the 35th Annual Simulation Symposium, , 316, (2002) [7] C. Daraio; D. Marco; F. Di Costa; C. Leporelli; G. Matteucci; A. Nastasi, Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research, European Journal of Operational Research, 248, 1, (2016) · Zbl 1346.90094 [8] G. Desaulniers and M. D. Hickman, Public transit, in Handbook in OR & MS (eds C. Barnhart and G. Laporte), Elsevier, (2007), 69-127. [9] A. Eranki, A model to create bus timetables to attain maximum synchronization considering waiting times at transfer stops, Thesis University of South Florida, 2004. [10] H. Fazlollahtabar; M. Saidi-Mehrabad, Optimizing multi-objective decision making having qualitative evaluation, Journal of Industrial and Management Optimization, 11, 747, (2016) · Zbl 1404.90082 [11] Y. Hadas; M. Shnaiderman, Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time, Transportation Research Part B: Methodological, 46, 1068, (2012) [12] O. J. Ibarra-Rojas; Y. A. Rios-Solis, Synchronization of bus timetabling, Transportation Research Part B: Methodological, 46, 599, (2012) [13] J. Jensen, O. Nielsen and C. Prato, Public transport optimisation emphasising passengers’ travel behaviour, Thesis Technical University of DenmarkDanmarks Tekniske Universitet, 2015. [14] L. Linzhong; Y. Juhua; M. Haibo; L. Xiaojing; W. Fang, Exact algorithms for multi-criteria multi-modal shortest path with transfer delaying and arriving time-window in urban transit network, Applied Mathematical Modeling, 38, 2613, (2014) · Zbl 1427.90283 [15] S. H. Nasseri; E. Behmanesh, Linear programming with triangular fuzzy numbers–A case study in a finance and credit institute, Fuzzy Information and Engineering, 5, 295, (2013) [16] F. Perez; T. Gomez; R. Caballero, Un modelo difuso para la selección de carteras de proyectos con incertidumbre en los costes, Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA, 13, 129, (2012) [17] F. Perez; T. Gomez, Multiobjective project portfolio selection with fuzzy constraints, Annals of Operation Research, 245, 7, (2016) · Zbl 1349.91255 [18] T. Rasmussen; M. Anderson; O. Nielsen; C. Prato, Timetable-based simulation method for choice set generation in large-scale public transport networks, EJTIR, 16, 467, (2016) [19] V. Sahinidis Nikolaos, Optimization under uncertainty: state-of-the-art and opportunities, Computers and Chemical Engineering, 28, 971, (2004) [20] Y. Shangyao; C. Chin-Jen; T. Ching-Hui, Inter-city bus routing and timetable setting under stochastic demands, Transportation research part A, 40, 572, (2006) [21] L. Sun; Z. Gao; Y. Wang, A Stackelberg game management model of the urban public transport, Journal of Industrial and Management Optimization, 8, 507, (2012) · Zbl 1364.90112 [22] W. Y. Szeto; W. Yongzhong, A simultaneous bus route design and frequency setting problem for tin shui wai, Hong Kong, European Journal of Operational Research, 209, 141, (2011) · Zbl 1208.90023 [23] S. L. Tilahun; H. C. Ong, Bus timetabling as a fuzzy multiobjective optimization problem using preference based genetic algorithm, Promet -Traffic & Transportation, 24, 183, (2012) [24] I. Verbas; C. Frei; H. Mahmassani; R. Chan, Stretching resources: sensitivity of optimal bus frequency allocation to stop-level demand elasticities, Public Transport, 7, 1, (2015) [25] I. Verbas; H. Mahmassani, Exploring trade-offs in frequency allocation in a transit network using bus route patterns: methodology and application to large-scale urban systems, Transportation Research Part B: Methodological, 81, 577, (2015) [26] Y. Wang; X. Zhu; L. B. Wu, Integrated multimodal metropolitan transportation model, Procedia Social and Behavioral Sciences, 96, 2138, (2013) [27] J. Zhang; T. Arentze; H. Timmermans, A multimodal transport network model for advanced traveler information system, Journal of Ubiquitous System and Pervasive Networks, 4, 21, (2012) [28] W. Zhang; M. Reimann, A simple augmented e-constraint method for multi-objective mathematical integer programming problems, European Journal of Operations Research, 234, 15, (2014) · Zbl 1305.90378 [29] F. Zhao; Z. Xiaogang, Optimization of transit route network, vehicle headways and timetables for large-scale transit networks, European Journal of Operational Research, 186, 841, (2008) · Zbl 1138.90350 [30] Y. Zhu, B. Mao, L. Liu and M. Li, Timetable design for urban rail line with capacity constraints Discrete Dynamics in Nature and Society2015 (2015), Art. ID 429219, 11 pp.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.