Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Abstract

______________________________________________________________________ AbstractModeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of without measurement errors. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

“…It is, in general, based on the utility maximization hypothesis that assumes that an individual's mode choice is a reflection of underlying preferences for each of the available alternatives and that the individual selects the mode with the highest utility among several alternative modes (Badoe and Miller 1995;Rajamani et al,2003;Bhatta and Larsen 2011). Among various types of discrete choice models, the multinomial logit model (MNL) is a typical formulation, as it has the advantage of a closed form mathematical structure, which simplifies computation in both estimation and prediction (Koppelman and Wen 2000;Ben-Akiva and Lerman 1985;Schwanen and Mokhtarian 2005;Dissanayake and Morikawa 2010).…”

Door-to-Door Travel Times in RP Departure Time Choice Models: An Approximation Method Based on GPS Data

“…One would expect that the finding that OLS coefficients predict travel times less precisely than GWR coefficients (see Table 2) results in a measurement error of the travel time, which biases downwards the time coefficient (Bhatta and Larsen, 2011;Train, 2003). However, probably as a consequence of the experimental setup, we find that the lower VOTs are mainly the result of a higher reward coefficient rather than lower time coefficients.…”

Aplicación de modelos econométricos para estimar la aceptabilidad de una tasa por congestión vehicular

“…Se plantearon varios modelos de elección discreta para elegir el mejor ajuste y significancia de sus coeficientes, de esto se prefirió la elección de un modelo Logit mixto en lugar de un modelo MNL, ya que la evidencia existente de los modelos MNL indica que con éste se pueden obtener estimaciones sesgadas de los parámetros de las funciones de utilidad, induciendo, además, cálculos erróneos de varias tasas marginales de sustitución [43]. Las alternativas de transporte utilizadas se identificaron así: 1: bus, 2: taxi y 3: auto particular.…”