Comparing uncertainties in travel mode choice decisions for various trip chains

Abstract

The main purpose of this article is to compare the mode choice decisions between commuting and non-commuting trip chains and evaluate the uncertainties in the mode choice process. According to household survey data from a mediumsized city in China, the whole day of travel is divided into several types of travel chains. We used the multinomial logit model to estimate the impacts of factors on the choices of trip modes, which included walk, bike, public transit, and car. The entropy theory was introduced to evaluate the uncertainty of each traveler's mode choice decision. The results indicate that there are great differences in mode choice between commuting and non-commuting trip chains. It is found that the causes and effects of different chains are different. The results help to understand the decision-making process of mode.

Exploring Socio-Demographic and Urban Form Indices in Demand Forecasting Models to Reflect Spatial Variations: Case Study of Childcare Centres in Hobart, Australia

“…In practice, trip chaining studies use the multinominal logit model for modelling [11], while the bivariate ordered probit model [10] and ordered probit model [13,16] are also used. Regression analysis [12,25] has also been used for better insight into trip chaining behaviour and individuals' travel patterns.…”

“…Finally, most studies agree that the formation of complex trip chains is related to socioeconomic, demographic and urban form characteristics [10][11][12][13]. Specific variables explored in the literature include household composition and life cycle stage, age, gender, marital status, income, employment status, car availability, residential density and density of transit stops.…”

“…The literature shows that socioeconomic, demographic and urban form characteristics are influential in most traffic and transport attributes [1][2][3][4][5][6][7][8][9][10][11][12][13]. This can justify that local transport authorities and planners attempting to develop a local transport models and elements to reflect local variations and specific local attributes.…”

Passengers receptivity of a new public transport mode: case of a BRT project in Lebanon

“…From another viewpoint, Wan et al (2019) found that the factors affecting the mode choice for commuting trips as not necessarily having the same impacts on choice behavior for non-commuting trips but they rather could have opposite effects and vice-versa. Moreover, Chen and Li (2017) combined the discrete choice model for public transportation (PT) services with another mathematical model: Structural Equation Model to enhance the accuracy of modal split outputs.…”