scholarly journals Mixed Logit Models for Travelers’ Mode Shifting Considering Bike-Sharing

2020 ◽  
Vol 12 (5) ◽  
pp. 2081 ◽  
Author(s):  
Mao Ye ◽  
Yajing Chen ◽  
Guixin Yang ◽  
Bo Wang ◽  
Qizhou Hu
Keyword(s):  
Public Transportation ◽  
Negative Impact ◽  
Stated Preference ◽  
Mixed Logit ◽  
Revealed Preference ◽  
Logit Models ◽  
Frequency Model ◽  
Mixed Logit Models ◽  
Bike Sharing ◽  
The Impact

This study quantifies the impact of individual attributes, the built environment, and travel characteristics on the use of bike-sharing and the willingness of shifting to bike-sharing-related travel modes (bike-sharing combined with other public transportation modes such as bus and subway) under different scenarios. The data are from an RP (Revealed Preference) survey and SP (Stated Preference) survey in Nanjing, China. Three mixed logit models are established: an individual attribute–travel characteristics model, a various-factor bike-sharing usage frequency model, and a mixed scenario–transfer willingness model. It is found that age and income are negatively associated with bike-sharing usage; the transfer distance (about 1 km), owning no car, students, and enterprises are positively associated with bike-sharing usage; both weather and travel distance have a significant negative impact on mode shifting. The sesearch conclusions can provide a reference for the formulation of urban transportation policies, the daily operation scheduling, and service optimization of bike-sharing.

Willingness-to-Pay Estimation with Mixed Logit Models: Some New Evidence

2005 ◽  
Vol 37 (3) ◽  
pp. 525-550 ◽  
Author(s):  
Mauricio Sillano ◽  
Juan de Dios Ortúzar
Keyword(s):  
Willingness To Pay ◽  
Stated Preference ◽  
Mixed Logit ◽  
Revealed Preference ◽  
Random Parameter ◽  
Estimation Methods ◽  
Logit Models ◽  
Choice Modelling ◽  
Mixed Logit Models ◽  
Discrete Choice Modelling

Mixed-logit models are currently the state of the art in discrete-choice modelling, and their estimation in various forms (in particular, mixing revealed-preference and stated-preference data) is becoming increasingly popular. Although the theory behind these models is fairly simple, the practical problems associated with their estimation with empirical data are still relatively unknown and certainly not solved to everybody's satisfaction. In this paper we use a stated-preference dataset—previously used to derive willingness to pay for reduction in atmospheric pollution and subjective values of time—to estimate random parameter mixed logit models with different estimation methods. We use our results to discuss in some depth the problems associated with the derivation of willingness to pay with this class of models.

scholarly journals The Impact of Grants on Homeowner Decisions to Retrofit to Reduce Hurricane-Induced Wind and Flood Damage

2020 ◽  
Vol 12 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Esther Chiew ◽  
Rachel A. Davidson ◽  
Joseph E. Trainor ◽  
Linda K. Nozick ◽  
Jamie L. Kruse
Keyword(s):  
Stated Preference ◽  
Mixed Logit ◽  
Revealed Preference ◽  
Flood Damage ◽  
Substantial Impact ◽  
Grant Program ◽  
State And Local ◽  
Using Data ◽  
Dollar Amount ◽  
The Impact

AbstractAn increasing number of national, state, and local programs have offered grants or other monetary incentives to encourage homeowners to retrofit their homes to reduce damage from natural hazard events. Despite this fact, little is known about how these offerings influence a homeowner’s decision to carry out such structural retrofits. This paper studies the impact that different grant program designs in particular have on the decision to undertake different types of retrofits to mitigate against hurricane damage. Using data from a survey of homeowners in the eastern half of North Carolina, we implement a mixed logit model that allows for the combination of both revealed-preference and stated-preference data available from the survey. Our findings show that offering a grant results in households being, on average, 3 times as likely to retrofit as when a grant is not offered. In addition, both the percentage of retrofit cost and the maximum dollar amount covered by the grant have a substantial impact on the probability that households choose to retrofit. Living closer to the coastline also has a significant impact on the probability that households will choose to retrofit. Counter to some previous research, we find that households who have experienced two or more hurricanes are less likely to choose to retrofit their homes. From our research, we find that the percentage of retrofit cost covered by the grant and the total cost are both important factors when deciding on the best grant program configuration.

How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

2020 ◽  
Vol 2674 (7) ◽  
pp. 494-503 ◽  
Author(s):  
Daniel J. Reck ◽  
Kay W. Axhausen
Keyword(s):  
Public Transportation ◽  
Modular Design ◽  
Stated Preference ◽  
Revealed Preference ◽  
Central Question ◽  
Preference Data ◽  
Stated Preference Data ◽  
Shared Mobility ◽  
Bike Sharing ◽  
Season Tickets

Mobility as a service (MaaS) seeks to integrate emerging shared mobility modes with existing public transportation (PT). Decisive to its uptake will be attractive subscription plans that cater for heterogeneous mobility needs. Research on willingness to pay for such plans has commenced, yet remains divided on a central question: how much to include of which mode, and how? Complementing previous research building on stated preference data, in this study revealed preference data is used to analyze the viability of different subscription plan components (PT, car-sharing, bike-sharing, taxi), modes of inclusion (budgets in minutes and season tickets) and subscription cycles (weekly, monthly). PT season tickets are found to be viable for 83% of all respondents. Interestingly, the viability of minute budgets of car- and bike-sharing depends on subscription cycle length. Using a monthly subscription cycle, car-/bike-sharing appears viable to include in a bundle for 35%/31% of all respondents, respectively. Using a weekly subscription cycle, these figures drop to 1.4%/0.4%, respectively, as weekly variation in demand is much higher than monthly variation. In contrast to many current MaaS pilots, taxi use remains too infrequent to include as recurring credit in MaaS plans. Rather, pay-as-you-go is the economically more sensible option for consumers. This research therefore challenges the idea of all-inclusive mobility flat rates and suggests a more modular design.

Including cost income ratio into utility function as a way of dealing with ‘exploding’ implicit prices in mixed logit models

2012 ◽  
Vol 18 (4) ◽  
pp. 370-380 ◽  
Author(s):  
Marek Giergiczny ◽  
Sviataslau Valasiuk ◽  
Mikolaj Czajkowski ◽  
Maria De Salvo ◽  
Giovanni Signorello
Keyword(s):  
Utility Function ◽  
Mixed Logit ◽  
Logit Models ◽  
Mixed Logit Models ◽  
Income Ratio ◽  
Implicit Prices

scholarly journals A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kai Lu ◽  
Alireza Khani ◽  
Baoming Han
Keyword(s):  
Open Systems ◽  
Choice Model ◽  
Mixed Logit ◽  
Random Coefficients ◽  
Logit Models ◽  
Transit Systems ◽  
Trip Purpose ◽  
Mixed Logit Models ◽  
Key Factor ◽  
Rich Data

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.

scholarly journals Fitting Mixed Logit Models by Using Maximum Simulated Likelihood

2007 ◽  
Vol 7 (3) ◽  
pp. 388-401 ◽  
Author(s):  
Arne Risa Hole
Keyword(s):  
Mixed Logit ◽  
Logit Models ◽  
Maximum Simulated Likelihood ◽  
Mixed Logit Models ◽  
Simulated Likelihood

This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood.

Mixed logit models: accuracy and software choice

2010 ◽  
Vol 26 (1) ◽  
pp. 167-172 ◽  
Author(s):  
Jae Bong Chang ◽  
Jayson L. Lusk
Keyword(s):  
Mixed Logit ◽  
Logit Models ◽  
Mixed Logit Models
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