Optimization of airport bus timetable in cultivation period considering passenger dynamic airport choice under conditions of uncertainty |
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| Institution: | 1. College of Transportation Management, Dalian Maritime University, Dalian 116026, China;2. Department of the Built Environment, Technische Universiteit Eindhoven, Eindhoven 5600 MB, Netherlands;1. Beijing Key Lab of Traffic Engineering, Beijing University of Technology, Beijing, 100124, China;2. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street Honolulu, HI 96822, USA;3. China Airport Construction Group Corporation, Beijing, 100102, China;1. Technical Innovation Cluster on Aeronautical Management, Universitat Autónoma de Barcelona, Sabadell (Barcelona), Spain;2. School of Aerospace Transport and Manufacturing (SATM), Cranfield University, Cranfield (Bedfordshire), United Kingdom;3. Department of Research and Innovation, Aslogic, Rubí, Spain;1. Department of Systems Engineering, Northeastern University, Shenyang 110819, PR China;2. College of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 110625, PR China;1. Division of Computing Science & Mathematics, University of Stirling, UK;2. School of Engineering and Materials Science, Queen Mary University of London, UK;3. IBM Global Business Services, Zurich, Switzerland;4. School of Electronic Engineering and Computer Science, Queen Mary University of London, UK;1. School of Electronic and Information Engineering, Beihang University, Beijing 100091, China;2. National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, Beijing 100091, China;3. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;4. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA |
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| Abstract: | An airport bus service, which is newly introduced in a multi-airport region, commonly leads to a gradually increasing market share of airports until a new state of equilibrium is reached. With the goal of speeding up and enlarging the increase in market share, this paper proposes a timetable optimization model by incorporating reactions of airport-loyal passengers to bus service quality. The simulation part of the model, which uses cumulative prospect theory to formulate discrete airport choices, results in predicted passenger demand needed in the optimization part. Then a genetic algorithm for multi-objective optimization problems called NSGA-II is applied to solve the model. To illustrate the model, the “Lukou airport-Wuxi” airport bus in China is taken as an example. The results show that the optimized timetables shorten the cultivation period and impel the market share to grow rapidly. |
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| Keywords: | Airport bus Cultivation period Timetable Dynamic optimization Airport choice |
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