Role of Attitudes in Transit and Auto Users’ Mode Choice of Ridesourcing

This paper presents the results of a study that examines the factors that influence travelers’ mode choice between transit and ridesourcing, for two distinct market segments—transit users in a regular context and auto users in occasional situations when a private vehicle is not available. Data from a stated preference (SP) survey were used for this study. Specifically, responses to a set of attitude-related questions were used to extract latent attitude factors that represent various aspects of attitudes toward mobility options. Mixed logit models were developed for the two classes. Socioeconomic and demographic attributes, as well as the attitudinal factors, were explored as independent variables. Model results revealed distinct behavior patterns between transit users and auto users. For transit users, the decision to shift to ridesourcing is highly affected by the perceptions of time and cost as well as motivations for technology, while the concerns on traveling with strangers and joy of driving were major barriers for auto users to use ridesourcing. Auto users would use ridesourcing when they believed that they would receive higher utilities, in relation to time, cost, reliability, convenience, comfort, stress relief, and so forth. This study provides further insights into the contributing factors to the choice between transit and ridesourcing. The results present a better understanding of the potential market for ridesourcing and highlight underlying attitudes that have significant influences on choice behavior. The findings could be helpful for planners and service providers to better plan for and address the needs and concerns of travelers.

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

Sustainability ◽

10.3390/su12052081 ◽

2020 ◽

Vol 12 (5) ◽

pp. 2081 ◽

Cited By ~ 1

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.

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

Environment and Planning A Economy and Space ◽

10.1068/a36137 ◽

2005 ◽

Vol 37 (3) ◽

pp. 525-550 ◽

Cited By ~ 165

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.

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Incorporating Inertia in Mode Choice and Influential Factors of Car Stickiness: Implications for Shifts to Public Transit

PROMET - Traffic&Transportation ◽

10.7307/ptt.v30i3.2507 ◽

2018 ◽

Vol 30 (3) ◽

pp. 293-303

Author(s):

Kun Gao ◽

Lijun Sun

Keyword(s):

Public Transit ◽

Stated Preference ◽

Mode Choice ◽

Influential Factors ◽

Travel Time Reliability ◽

Transit System ◽

Travel Mode ◽

And Gender ◽

Demographic Attributes ◽

Willingness To Use

To explore efficient strategies of adjusting travel mode structure and support scientific implements of public transit system, this paper investigated travelers’ mode choice behavior in a multimodal network incorporating inertia in utility specifications. Comprehensive stated preference surveys considering four modes and four key decisive variables were designed, and face-to-face investigations were conducted to collect reliable data in Shanghai. The discrete choice technique considering mode-specific inertias was employed for modeling. The influencing factors of car stickiness were particularly explored. The results show that there are significant and mode-specific inertias in travelers’ choices of travel mode. The inertia of car users shifting to other modes is considerably large compared to inertias of public transit users. Travel time reliability and crowdedness in public transit are identified to be crucial factors influencing car users’ willingness to use public transit. Demographic attributes (age, income, education level and gender), spatial context features (commuting duration) and the regime of flexible work time are found to be significant influential variables of car stickiness. Moreover, direct and cross elasticity analyses were executed to show practical implications of shifting car users to public transit. The results provide serviceable support for transport planning and strategy making.

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Mixed-use development in Christchurch, New Zealand: Do you want to live there?

Urban Studies ◽

10.1177/0042098017725475 ◽

2017 ◽

Vol 55 (12) ◽

pp. 2682-2702 ◽

Cited By ~ 6

Author(s):

Diana Kusumastuti ◽

Alan Nicholson

Keyword(s):

Stated Preference ◽

Design Method ◽

Mixed Logit ◽

Low Density ◽

Efficient Design ◽

Mixed Logit Models ◽

Suburban Areas ◽

Mixed Use ◽

Single Use ◽

The City

Christchurch, one of New Zealand’s major cities, has been dealing with a housing shortage after a series of major earthquakes struck in 2010 and 2011, causing extensive damage to the city. Consequently, two distinct types of housing development appeared in the suburban areas of Christchurch: low-density single-use neighbourhoods and higher-density mixed-use neighbourhoods. The latter type is relatively new for Christchurch suburban areas where low population densities dominated prior to 2011. Thus, this study aimed to investigate the preferences of the residents of Christchurch and its surrounding districts for living in mixed-use neighbourhoods. Specifically, it sought to identify the weights that those residents place on the costs of house purchase and transport, versus neighbourhood costs associated with mixed-use development, when purchasing a residential property in the suburban areas of Christchurch. For this, a stated preference survey was developed, using the efficient design method, and mixed-logit models were estimated using the data. The results show that most of those residents prefer to live in low-density single-use neighbourhoods rather than in higher-density mixed-use neighbourhoods, and are sensitive to increases in the land price, density of development and diversity of land use in the areas.

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Investigating contributing factors to injury severity levels in crashes involving pedestrians and cyclists using latent class clustering analysis and mixed logit models

Journal of Transportation Safety & Security ◽

10.1080/19439962.2021.1958037 ◽

2021 ◽

pp. 1-28

Author(s):

Shaojie Liu ◽

Zijing Lin ◽

Wei (David) Fan

Keyword(s):

Clustering Analysis ◽

Latent Class ◽

Injury Severity ◽

Mixed Logit ◽

Logit Models ◽

Contributing Factors ◽

Mixed Logit Models ◽

Severity Levels

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A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

Journal of Science and Technology Issue on Information and Communications Technology ◽

10.31130/jst.2017.40 ◽

2017 ◽

Vol 3 (1) ◽

pp. 76 ◽

Cited By ~ 1

Author(s):

Duong Tran Duc ◽

Pham Bao Son ◽

Tan Hanh ◽

Le Truong Thien

Keyword(s):

Data Mining ◽

Bayesian Network ◽

Web Applications ◽

Service Providers ◽

Main Idea ◽

Classification Methods ◽

Cost Sensitive Learning ◽

Network Method ◽

Demographic Attributes ◽

Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

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Analyzing Prospective Owners’ Choice Decision towards Plug-in Hybrid Electric Vehicles in Urban India: A Stated Preference Discrete Choice Experiment

Sustainability ◽

10.3390/su13147725 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7725

Author(s):

Reema Bera ◽

Bhargab Maitra

Keyword(s):

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Stated Preference ◽

Mixed Logit ◽

Road Transport ◽

Home Based ◽

Urban India ◽

Choice Decision ◽

The Mean ◽

Hybrid Electric

Plug-in Hybrid Electric Vehicles (PHEVs) can help decarbonize road transport in urban India. To accelerate the diffusion of PHEVs, investigation of commuter preferences towards the attributes of PHEVs is necessary. Therefore, the present study analyzes prospective owners’ choice decisions towards PHEVs in a typical Indian context. A stated preference survey was designed to collect responses from the current owners of conventional vehicles (CVs) in Delhi, India, and Mixed Logit (ML) models were developed to estimate commuters’ Willingness To Pay (WTP) for a set of key PHEV-specific attributes. The decomposition effect of prospective owners’ sociodemographic characteristics and trip characteristics on the mean estimates of random parameters was investigated by developing ML models with heterogeneity. Subsequently, the influence of improvement of each PHEV-specific attribute on prospective owners’ choice probability was investigated by calculating marginal effects. Among the various PHEV-specific attributes considered in the present study, high WTPs are observed for decrease in battery recharging time, reduction in tailpipe emission and increase in electric range. Therefore, an added emphasis on these attributes by vehicle manufacturers is likely to enhance the attractiveness of PHEVs to Indian commuters. The results also highlight the importance of government subsidy for promoting PHEVs in the Indian market. Prospective owners’ income, availability of home-based parking space, and average daily trip length are found to significantly influence the choice decision of Indian commuters towards PHEVs.

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