Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model

Kamargianni, Maria; Dubey, Subodh; Polydoropoulou, Amalia; Bhat, Chandra R.

doi:10.1016/j.tra.2015.06.011

Cited by 74 publications

(57 citation statements)

References 53 publications

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“…In light of the name of PCD, data from revealed preference (RP) survey and stated preference (SP) survey may be called actively collected data (ACD) as per the process of data collection that requires active participation of travelers. RP/SP surveys are still the most widely used data collection approaches for modeling analysis of travel behavior [17][18][19][20]. With a complete set of records on travel modes, ACD provide not only exact trip information of timing, location, and purpose, but also the socioeconomic and demographic attributes of respondents, which are both essential in modeling analysis of travel choices.…”

Section: Introductionmentioning

confidence: 99%

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Zhang

Wang

et al. 2019

Sustainability

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Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

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Section: Introductionmentioning

confidence: 99%

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Zhang

Wang

et al. 2019

Sustainability

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“…Excellent literature reviews of modeling the disaggregate demand for cycling are provided in Sener, et al (2009), Heinen, et al (2010, Li et al (2013), Fernández-Heredia et al (2013), and Maldonado-Hinajeros et al (2014). In particular, Fernández-Heredia et al (2014), Habib et al (2014) and Maldonado-Hinajeros et al (2014) discuss the integration of discrete choice models of cycling decisions with subjective (latent) factors, using hybrid choice models Ben-Akiva et al, 2002; see also Bhat et al, 2015 andKamargianni et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Kamargianni et al, 2015). Habib et al (2014), for example, work with a combination of binary logit and bivariate probit models.…”

Section: Introductionmentioning

confidence: 99%

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

Motoaki

Daziano

2015

Transportation Research Part A: Policy and Practice

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In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices.

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“…In the last decade and a half, ICLV models have been increasingly applied in many fields to obtain a deeper understanding of the decision process of individuals [19,25,[28][29][30]. Within the field of transportation, ICLV models have been deployed to investigate travel mode choice [31,32], route choice [33], shared-ride transportation service choice [34], flight choice [35], and so on. All the same, the reviewed literature shows that the ICLV model has not been applied to investigate the choice of transportation ticketing channels.…”

Section: Model On Choicementioning

confidence: 99%

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

Jia

Zhang

et al. 2019

Journal of Advanced Transportation

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Buses represent the main mode for intercity passenger transportation in China. In recent years, a multichannel ticketing strategy has been widely employed in the bus passenger transportation industry. However, the mechanisms and key drivers of the channels through which bus passengers purchase tickets are underexplored. Thus, the aim of this study is to empirically apply an integrated choice and latent variable (ICLV) approach to analyze ticketing channel choice behavior and the heterogeneous preferences of bus passengers. The variables incorporated in the model include the socioeconomic characteristics of passengers, trip attributes, and latent attitudes with 12 ordinal indicators. Based on the data of 1800 participants collected from the city of Beijing, China, this study develops a ticketing channel choice ICLV model merging a discrete choice model with a structural equation model. The key factors that affect the channel preference are further discussed through a comparison with a conventional multinomial logit (MNL) model. The results reveal that the three attitudinal variables have a significant influence on ticketing channel choice. Furthermore, this study indicates that perceptual differences exist due to various socioeconomic and trip characteristics. Personal privacy is a major obstacle that prevents passengers from choosing online channels, especially for older passengers and those with lower education.

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Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model

Cited by 74 publications

References 53 publications

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

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