On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices

Wang, Ke; Ye, Xin; Pendyala, Ram M.; Zou, Yajie

doi:10.1371/journal.pone.0186689

Cited by 18 publications

(9 citation statements)

References 35 publications

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“…In future practice with this kind of algorithms, it may be necessary to examine the quality of random draws with caution from different perspectives prior to application. Finally, it is worth mentioning that more flexible and complex error structures like semi-nonparametric specifications have been introduced into the transportation literature (40)(41)(42)(43). The developed method may also be applied to simulate more complex error structures, which could be an interesting topic for future research.…”

Section: Discussionmentioning

confidence: 99%

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Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures

Pinjari³

2019

Transportation Research Record: Journal of the Transportation R

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The multiple discrete-continuous generalized extreme value (MDCGEV) model has been derived from multivariate extreme value (MEV)-based stochastic specifications to relax the independence assumption in the multiple discrete-continuous extreme value (MDCEV) model. It is analogous to the situation where a generalized extreme value (GEV) model relaxes the same assumption in a multinomial logit (MNL) model. However, unlike the case of single discrete choice model where substitution patterns can be understood based on elasticity expressions for a change in the value of an explanatory variable, the MDCEV and its variants do not offer closed-form elasticity expressions. The predictions must be compared explicitly under the base case and policy case scenarios. To perform a prediction exercise with MDCEV or its variants, random samples have to be drawn from the relevant stochastic distributions, which is actually not a straightforward task. In this paper, a practical method is proposed for drawing from an MEV distribution and the method is demonstrated to examine substitution patterns in an MDCGEV model for household transportation expenditures. The empirical results show that the cross-elasticities of explanatory variables in the MDCGEV model exhibit more variations than those in MDCEV and multiple discrete-continuous nested extreme value (MDCNEV) models.

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Section: Discussionmentioning

confidence: 99%

“…Finally, it is worth mentioning that more flexible and complex error structures like semi-nonparametric specifications have been introduced into the transportation literature ( 40 – 43 ). The developed method may also be applied to simulate more complex error structures, which could be an interesting topic for future research.…”

Section: Discussionmentioning

confidence: 99%

Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures

Pinjari³

2019

Transportation Research Record: Journal of the Transportation R

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“…(identical and independent distribution) of standard Gumbel. The assumption is based upon two properties of the Gumbel distribution: One is its similarity to the normal distribution, while the other is the achievement of a closed-form expression for the likelihood function after being integrated with the utility maximization theory [38][39][40]. However, since error terms may have unequal variances when choice data are derived from different sources, the model cannot be developed by pooling those data directly.…”

Section: Joint Rp and Sp Mnl Modelmentioning

confidence: 99%

Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China

Duan

et al. 2020

Sustainability

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Carsharing is an emerging commute mode in China, which may produce social and environmental benefits. This paper aims to develop a commute mode choice model to explore influential factors and quantify their impacts on the potential demand for carsharing in Shanghai. The sample data were obtained from a revealed preference (RP) and stated preference (SP) survey and integrated with level-of-service attributes from road and transit networks. The RP survey collected commuters’ trip information and socioeconomic and demographic characteristics. In the SP survey, four hypothetical scenarios were designed based on carsharing’s unit price to collect commuters’ willingness to shift to carsharing. Data fusion method was applied to fuse RP and SP models. The joint model identified the target group of choosing carsharing with certain socioeconomic and demographic attributes, such as gender, age, income, household member, household vehicle ownership, and so on. It also indicates that the value of time (VOT) for carsharing is 35.56 RMB Yuan (5.08 US Dollar)/h. The elasticity and marginal effect analysis show that the direct elasticity of carsharing’s fare on its potential demand is −0.660, while the commuters, who have a more urgent plan on car purchase or are more familiar with the carsharing service, have much higher probabilities to choose carsharing as their commute modes. The developed model is expected to be applied to the urban travel demand model, providing references for the formulation of carsharing operation scheme and government policy.

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“…For example, Yuan et al (2017) proposed K−2FMM approach to improve the accuracy level of measuring travel time variability [30]. proposed a developed semi-nonparametric generalized multinomial logit model to extend the standard Gumbel distribution [31]. Tang et al (2017) proposed an improved hierarchical fuzzy inference method based on map-matching algorithm [32].…”

Section: Collaborative Vehicle Routing Problem Vehicle Routingmentioning

confidence: 99%

Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

Gui

2018

Journal of Advanced Transportation

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Urban traffic congestion has become a global problem and has garnered special importance in recent years in the transportation sector, especially in taxi markets. To unlock the potential of Internet of Vehicles (IoV) in Intelligent Transportation Systems (ITS), it was vital to make efficiency measurements. In this study, Distance Formula was built to calculate distances by GPS data based on mathematical equations, and Motorcade-Sharing (MS) Model was proposed to improve the efficiency of collaborative vehicles. The experimental data of 2191 GPS-equipped taxis in Sanya of China was adopted to make comparisons between original results and modelled results. Measurement results showed that MS Model had 10.54% more leisure taxis, reduced 5 overdriving taxis, and saved 33.73% running distance in total compared to the original. This indicated that the application of MS Model could not only alleviate urban traffic congestion but also optimize urban taxi markets, and it has a bright future in the field of taxi and other collaborative vehicles. Future directions could be improving MS Model and expanding data.

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On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices

Cited by 18 publications

References 35 publications

Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures

Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures

Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China

Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

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