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Influential vectors in fuel consumption by an urban bus operator: Bus route,driver behavior or vehicle type?
Institution:1. National Lab of Auto Performance and Emission Test, School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China;2. Technical Development Center of China National Heavy Duty Truck Group Co., Ltd, Jinan 250002, China;1. Sustainability Measurement and Modeling Lab (SUMMLab), Universitat Politècnica de Catalunya (UPC), EET-Campus Terrassa, 08222 Barcelona, Spain;2. Observatori de la Sostenibilitat d’Andorra (OBSA), Plaça de la Germandat 7, AD600 Sant Julià de Lòria, Andorra;1. Department of Mechanical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands;2. Department of Human-Technology Interaction, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands;1. Social Psychology Department, Universidad Nacional de Educación a Distancia (UNED), Spain;2. Transport Research Centre, TRANSyT-Universidad Politécnica de Madrid, Spain
Abstract:Energy costs account for an important share of the total costs of urban and suburban bus operators. The purpose of this paper is to expand empirical research on bus transit operation costs and identify the key factors that influence bus energy efficiency of the overall bus fleet of one operator and aid to the management of its resources.We estimate a set of multivariate regression models, using cross-section dataset of 488 bus drivers operating over 92 days in 2010, in 87 routes with different bus typologies, of a transit company operating in the Lisbon’s Metropolitan Area (LMA), Rodoviária de Lisboa, S.A.Our results confirm the existence of influential variables regarding energy efficiency and these are mainly: vehicle type, commercial speed, road grades over 5% and bus routes; and to a lesser extent driving events such as: sudden longitudinal decelerations and excessive engine rotation. The methodology proved to be useful for the bus operator as a decision-support tool for efficiency optimization purpose at the company level.
Keywords:Fuel efficiency  Bus operator  Regression models  Elasticities  Lisbon metropolitan area
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