scholarly journals Who Is Willing to Share Their AV? Insights about Gender Differences among Seven Countries

2021 ◽  
Vol 13 (9) ◽  
pp. 4769
Author(s):  
Amalia Polydoropoulou ◽  
Ioannis Tsouros ◽  
Nikolas Thomopoulos ◽  
Cristina Pronello ◽  
Arnór Elvarsson ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Autonomous Vehicles ◽  
Stated Preference ◽  
Autonomous Vehicle ◽  
Mixed Logit ◽  
Travel Behaviour ◽  
User Preferences ◽  
Traffic Conditions ◽  
Study Results ◽  
Willingness To Adopt

The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services.

scholarly journals Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan

Smart Cities ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 230-244 ◽  
Author(s):  
Mingyang Hao ◽  
Yanyan Li ◽  
Toshiyuki Yamamoto
Keyword(s):  
Willingness To Pay ◽  
Autonomous Vehicles ◽  
Stated Preference ◽  
Autonomous Vehicle ◽  
Transportation Mode ◽  
Public Preferences ◽  
Socioeconomic Characteristics ◽  
On Demand ◽  
Vehicle Systems ◽  
The Relationship

Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole.

scholarly journals Post-COVID-19 travel behaviour patterns: impact on the willingness to pay of users of public transport and shared mobility services in Spain

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Samir Awad-Núñez ◽  
Raky Julio ◽  
Juan Gomez ◽  
Borja Moya-Gómez ◽  
Julián Sastre González
Keyword(s):  
Willingness To Pay ◽  
Public Transport ◽  
Nationwide Survey ◽  
Travel Behaviour ◽  
Revealed Preferences ◽  
Trip Purpose ◽  
Willingness To Adopt ◽  
Shared Mobility ◽  
Considerable Impact ◽  
Willingness To Use

Abstract Background The COVID-19 crisis has meant a significant change in the lifestyle of millions of people worldwide. With a lockdown that lasted almost three months and an impulse to new normality, transport demand has suffered a considerable impact in the Spanish case. It is mandatory to explore the effect of the pandemic on changes in travel behaviour in post-COVID-19 times. Methodology A nationwide survey was carried out during the lockdown in Spring 2020 to overview the recent changes. The survey collected both stated preferences (socio-demographic characteristics and mobility-related attributes), and revealed preferences (individuals’ habits, especially in the frequency of the trips according to the trip purpose, and opinions regarding the willingness and acceptability of these changes, and which actors would have to drive them, and how) of individuals. This paper aims to study and understand the willingness to adopt a set of measures to improve the safety conditions of public transport and shared mobility services against possible contagion from COVID-19 and the willingness to pay for them. Results The results obtained show that some measures, such as the increase of supply and vehicle disinfection, result in a greater willingness to use public transport in post-COVID-19 times. Similarly, the provision of covers for handlebars and steering wheels also significantly increases individuals’ willingness to use sharing services. However, respondents expect that these measures and improvements would be implemented but maintaining the same pre-COVID-19 prices. The results of this research might help operators deploy strategies to adopt their services and retain users.

scholarly journals Model Contrast of Autonomous Vehicle Impacts on Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Sociodemographic Factors ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Traffic Conditions ◽  
Demand Models ◽  
Wide Range ◽  
Potential Benefits

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

Incorporating Attitudinal Factors to Examine Adoption of and Willingness to Pay for Autonomous Vehicles

2019 ◽  
Vol 2673 (8) ◽  
pp. 418-429 ◽  
Author(s):  
Hamidreza Asgari ◽  
Xia Jin
Keyword(s):  
Willingness To Pay ◽  
Stress Reduction ◽  
Latent Variables ◽  
Autonomous Vehicles ◽  
Mode Choice ◽  
Autonomous Vehicle ◽  
Cost Savings ◽  
Structural Equations ◽  
Driving Mode ◽  
Attitudinal Factors

Results from a recent consumer survey were thoroughly analyzed in relation to willingness to adopt and willingness to pay (WTP) for different autonomous vehicle (AV) features. Four different levels of automation were considered including basic vehicles, adding advanced features, partial automation, and full automation. A structural equations model with latent variables was employed, which simultaneously regressed adoption and WTP levels against a variety of available variables including socioeconomic and demographic attributes, private car usage habits, and attitudinal preferences/personal opinions. To address the endogeneity in personal attitudes, these variables were added to the model as latent factors. Accordingly, the analysis revealed four major latent attitudinal factors, respectively labeled as “joy of driving,”“mode choice reasoning,”“trust,” and “technology savviness.” Model results indicated that those who enjoy driving were the hardest to persuade towards AV adoption or to pay for automated features. On the other hand, technology savvy people showed higher tendency towards AV adoption. When it comes to factors affecting mode choice including travel time, travel cost, and functionality, people are willing to pay more for automated features when they believe that these features and services will provide them better utility, in relation to time and cost savings, convenience, stress reduction, and quality of life, and so forth. Interestingly, individuals with trust concerns showed higher WTP values, which may indicate that the market believes autonomous vehicles will bring more privacy and protection, at least compared with existing shared mobility or public transit options.

Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection?

2021 ◽  
pp. 036119812110252
Author(s):  
Moein Khaloei ◽  
Andisheh Ranjbari ◽  
Ken Laberteaux ◽  
Don MacKenzie
Keyword(s):  
Stated Preference ◽  
Autonomous Vehicle ◽  
Mixed Logit ◽  
Revealed Preference ◽  
Current Mode ◽  
Mixed Logit Model ◽  
Transit Ridership ◽  
Last Mile ◽  
Autonomous Mode ◽  
The Us

Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle. To that end, a stated preference survey was launched around the US to ask 1,500 commuters how they would choose their commute mode from among choices including their current mode and other conventional modes as well as asking them to choose between their current mode and an autonomous mode. Using a joint stated and revealed preference dataset, a mixed logit model was developed and analyzed. The results show that ridehailing per se might not be a significant competitor to transit, especially if it is integrated with transit as a first-/last-mile service. The total share of transit (transit-only riders plus those who use transit in connection with first-/last-mile ridehailing) remains substantially flat as set against conventional ridehailing services, even if ridehailing fares decrease. On the other hand, when the ridehailing price is significantly reduced by automation, our analysis suggests a decline in total transit ridership and an increase in ridehailing, especially for solo ridehailing. Also, it was found that autonomous pooled ridehailing might not be as appealing to commuters as autonomous solo ridehailing.

scholarly journals Examining Willingness-to-Pay and Zero Valuations for a Health Improvement with Logistic Regression

2021 ◽  
Vol 58 ◽  
pp. 004695802110281
Author(s):  
Afentoula G. Mavrodi ◽  
Stavros A. Chatzopoulos ◽  
Vassilis H. Aletras
Keyword(s):  
Logistic Regression ◽  
Willingness To Pay ◽  
Stated Preference ◽  
Intraclass Correlation ◽  
Healthcare Services ◽  
Health Improvement ◽  
Public Healthcare ◽  
Computer Assisted ◽  
Good Reliability ◽  
Study Results

Study aim was to elicit the Greek general population’s willingness-to-pay (WTP) for a health improvement (recovery to perfect health), examine attitudinal differences between willing- and unwilling-to-pay individuals regarding healthcare services provision, and investigate —using a logistic regression model—demographic/socioeconomic factors impact on their intention to pay for a health improvement. A research tool was developed to conduct a cross-sectional stated-preference telephone-based survey (January-February 2019) and a representative sample (n = 1342) of the Greek general population was queried. The computer-assisted telephone-interview (CATI) method was used to ensure random sampling. WTP was elicited using the iterative bidding technique. Participants’ attitudes toward healthcare services provision were assessed through pre-defined statements. Test-retest reliability of these statements was assessed using intraclass correlation coefficients (ICC). Logistic regression was employed to identify sociodemographic factors’ effect on WTP intention. Differences among individuals’ attitudes were assessed using the chi-square test. All analyses were conducted using the IBM SPSS Software v.25.0. Analysis showed acceptable reliability for WTP estimates (ICC = .67) and good reliability for healthcare services assessment statements (ICC = .83-.94). Mean WTP was estimated at €439.8. Respondents with higher educational level and higher household income were more likely to be willing to pay for a health improvement. On the contrary, older participants were less likely to be willing to pay. Most participants who considered public healthcare services to be of high quality were unwilling to pay. Logistic regression analysis led to the development of an effective predictive model regarding factors affecting individuals’ WTP intention for a health improvement. Further classification of unwilling-to-pay individuals into protest responders and “true” zero valuators showed that protest responders are unlikely to be representative of the population. Hence, study results can be used for debiasing WTP responses, leading to a more accurate use of WTP estimates by policy makers, exploiting WTP values in medical interventions cost-benefit analysis within reimbursement decisions framework.

A Connected Autonomous Vehicle Emulator (CAVE) for Testing Multi-agent, Conventional–Autonomous Mixed Vehicle Traffic Scenarios

2021 ◽  
pp. 339-358
Author(s):  
Dan Negrut ◽  
Asher Elmquist ◽  
Radu Serban ◽  
Dylan Hatch ◽  
Parmesh Ramanathan
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Computer Programs ◽  
Software Infrastructure ◽  
Vehicle Traffic ◽  
Traffic Conditions ◽  
Multi Agent

We discuss a software infrastructure that provides a virtual proving ground for designing, training, and auditing the computer programs used to pilot connected autonomous vehicles (CAVs). This effort does not concentrate on developing the piloting computer programs (PCPs) responsible for path planning in autonomous vehicles (AVs). Instead, we have established a first version of an emulation platform that changes the PCP design/test/improve process, which is often times carried out covertly [46], or in actual traffic conditions with potentially fatal consequences [45, 47].

scholarly journals Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ana T. Moreno ◽  
Andrzej Michalski ◽  
Carlos Llorca ◽  
Rolf Moeckel
Keyword(s):  
Metropolitan Area ◽  
Autonomous Vehicles ◽  
Online Survey ◽  
Travel Demand ◽  
Stated Preference ◽  
Autonomous Vehicle ◽  
Trip Duration ◽  
Conventional Vehicle ◽  
Willingness To Use ◽  
The Impact

Intermediate modes of transport, such as shared vehicles or ride sharing, are starting to increase their market share at the expense of traditional modes of car, public transport, and taxi. In the advent of autonomous vehicles, single occupancy shared vehicles are expected to substitute at least in part private conventional vehicle trips. The objective of this paper is to estimate the impact of shared autonomous vehicles on average trip duration and vehicle-km traveled in a large metropolitan area. A stated preference online survey was designed to gather data on the willingness to use shared autonomous vehicles. Then, commute trips and home-based other trips were generated microscopically for a synthetic population in the greater Munich metropolitan area. Individuals who traveled by auto were selected to switch from a conventional vehicle to a shared autonomous vehicle subject to their willingness to use them. The effect of shared autonomous vehicles on urban mobility was assessed through traffic simulations in MATSim with a varying autonomous taxi fleet size. The results indicated that the total traveled distance increased by up to 8% after autonomous fleets were introduced. Current travel demand can still be satisfied with an acceptable waiting time when 10 conventional vehicles are replaced with 4 shared autonomous vehicles.

Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine

2019 ◽  
Vol 2673 (11) ◽  
pp. 865-878 ◽  
Author(s):  
Dongwoo Lee ◽  
John Mulrow ◽  
Chana Joanne Haboucha ◽  
Sybil Derrible ◽  
Yoram Shiftan
Keyword(s):  
Panel Data ◽  
Autonomous Vehicles ◽  
Choice Model ◽  
Environmental Concern ◽  
Stated Preference ◽  
Autonomous Vehicle ◽  
Predictive Performance ◽  
Gradient Boosting ◽  
Purchase Cost ◽  
Gradient Boosting Machine

This article applies machine learning (ML) to develop a choice model on three choice alternatives related to autonomous vehicles (AV): regular vehicle (REG), private AV (PAV), and shared AV (SAV). The learned model is used to examine users’ preferences and behaviors on AV uptake by car commuters. Specifically, this study applies gradient boosting machine (GBM) to stated preference (SP) survey data (i.e., panel data). GBM notably possesses more interpretable features than other ML methods as well as high predictive performance for panel data. The prediction performance of GBM is evaluated by conducting a 5-fold cross-validation and shows around 80% accuracy. To interpret users’ behaviors, variable importance (VI) and partial dependence (PD) were measured. The results of VI indicate that trip cost, purchase cost, and subscription cost are the most influential variables in selecting an alternative. Moreover, the attitudinal variables Pro-AV Sentiment and Environmental Concern are also shown to be significant. The article also examines the sensitivity of choice by using the PD of the log-odds on selected important factors. The results inform both the modeling of transportation technology uptake and the configuration and interpretation of GBM that can be applied for policy analysis.

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.

Sign in / Sign up

Export Citation Format

Share Document