A Generalized Comfort Function of Subway Systems Based on a Nested Logit Model

With rapid urbanization, subway systems are widely acknowledged as one of the best solutions to urban transportation problems. The operators or managers of subway systems should pay more attention to passenger’s perceptions of service quality to maintain its competitive position. Taking the traffic state, efficiency, and environmental impact into consideration, the concept of generalized comfort is proposed in this paper. Based on a nested logit model, the selection probability for each factor in a generalized comfort function can be estimated using a nested structure. A certain factor is considered to be more significant in a generalized comfort function than others, when the corresponding probability of this factor is higher in value. Using stated preference and revealed preference data about passenger travel behavior obtained from the Beijing subway, the parameters of generalized comfort function are estimated by maximum likelihood techniques.