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A latent class choice based model system for railway optimal pricing and seat allocation
Affiliation:1. Resource Systems Group, Inc., 55 Railroad Row, White River Junction, VT 05001, USA;2. Department of Civil and Environmental Engineering, University of Maryland, 1173 Glenn L. Martin Hall, Bldg #088, College Park, MD 20742, USA;1. School of Transportation Engineering, Hefei University of Technology, Hefei 230009, China;2. School of Economics and Management, Beihang University, Beijing 100191, China;1. Department of Civil and Environmental Engineering, University of Maryland, 3250 Kim Bldg., College Park, MD 20742, USA;2. Departement Informatique et Recherche Opérationnelle, Université de Montréal, Pavillon André-Aisenstadt, CP 6128 Succursale Centre-Ville, Montréal, QC H3C 3J7, Canada;3. Lead Marketing Science at Facebook, Bangkok Metropolitan Area, Thailand;1. Department of Urbanism, Facultad de Arquitectura y Urbanismo, Universidad de Chile, Portugal 84, Santiago, Chile;2. CITEC Ingénieurs Conseils SA, Genève, Switzerland;3. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland;1. The Korea Transport Institute, 370 Sicheong-daero, Sejong-si, 339-007, South Korea;2. Korea Aerospace University, 200-1 Hwajeon-dong, Goyang-City, South Korea;1. School of Transportation and Logistics, Southwest Jiaotong University, No.111, North Second Ring Road, Chengdu, 610031, China;2. Lab of National United Engineering Laboratory of Integrated and Intelligent Transportation, No.111, North Second Ring Road, Chengdu, 610031, China
Abstract:In this paper, discrete choice methods in the form of multinomial logit and latent class models are proposed to explain ticket purchase timing of passenger railway. The choice model and demand functions are incorporated into a revenue optimization problem which jointly considers pricing and seat allocation. The framework provides insightful policy implications in term of fare and capacity distribution derived from actual passenger behavior. It shows that accepting short-haul demand provides greater revenue than long-haul demand using the same capacity. Revenue improvement ranges from 16.24% to 24.96% in multinomial logit models and from 13.82% to 21.39% in latent class models respectively.
Keywords:Heterogeneity  Latent class model  Multinomial logit model  Pricing  Revenue management  Seat allocation
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