Stated Preference Survey Data Research Articles

Examining Congestion Pricing Scheme Effectiveness Using the Travel Time Congestion Index

This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.

Investigation of the Policy Effect of High-Speed Metropolitan Rail Service in Seoul Through Latent Class Modeling Analysis of Stated Preference Survey

The sprawl of megacities is increasing the need to improve public transit services to cover longer distances in shorter times than current services. High-speed metropolitan rail services have been introduced to satisfy the needs of passengers and are expected to alleviate the transportation problems caused by urban sprawl. However, the effectiveness of public transit demand inducement policies, which have been employed in various ways in South Korea, has been questioned. Thus, agencies responsible for high-speed metropolitan rail services should also evaluate whether such policies will work properly. The achievement of the policy can be evaluated as sufficiently inducing the demand of the target group, and investigated through the residence information and the current travel behavior. This study investigates the current travel behavior of users to be replaced when a new high-speed metropolitan rail service is introduced and directly analyzes the travel mode shifting effect. While previous studies focused on increasing the applicability to general services by classifying groups based on personal attributes, this study focuses on evaluating detailed policy achievements for specific services. We apply a latent class modeling approach using stated preference survey data for group classification. In particular, by employing the user’s current travel behavior as a class membership attribute, the characteristics of potential passengers of the high-speed metropolitan rail service are analyzed. The estimation results suggest that current travel behavior significantly classifies high-speed rail passengers. In addition, the policy achievement was investigated in detail by region and operation time, and several additional policy directions are presented to satisfy the policy goals.

Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier.

What would it take for the people of Riyadh city to shift from their cars to the proposed metro?

Modal shift from car to metro has positive impacts on the society including congestion reduction, energy efficiency, and reduction in carbon emission and air pollution. However, people often choose car over public transport for many reasons, for example, comfort, freedom, and reliability among many others. Barriers to shift to public transport can play even stronger role in cities like Riyadh, where the urban infrastructure as well as fuel price and the culture favor car travel. To what extent certain factors may influence modal shift from car to public transport in Riyadh is a critical question. In this study, we investigate the underlying factors that drive people of Riyadh to use the metro instead of car. Using stated preference survey data and mixed logit models, we analyzed modal shift from both car and taxi to metro. The results show that travel times, travel costs, walk times and transfer requirements significantly and negatively affect people’s mode choices. Whereas young Saudis are more likely to shift to metro, the findings suggest that a very favorable walking situation is required for metro to be successful.

Household Adaptations to Infrastructure System Service Interruptions

When critical infrastructure system services are disrupted, households typically respond by reducing, delaying, or relocating their demand (e.g., delaying laundry), or augmenting supply (e.g., using a generator). While this phenomenon is well known, there has been little systematic empirical investigation of it. Focusing on electric power and water service interruptions and using revealed and stated preference survey data from Los Angeles County, California, we develop 24 mixed logit models, one each to predict the probability an individual undertakes a specified adaptation as a function of outage duration and characteristics of the individual. The analysis aims to determine: (1) how common different household adaptations are; (2) how adaptation implementation varies with infrastructure type, outage duration, and uses of the service; (3) what household characteristics are associated with implementation of different adaptations; and (4) how adaptations tend to occur together. The percentage of individuals who report doing an adaptation varies greatly across adaptations and outage durations, from 2% to 88%. In general, adaptations that require moving out of the home are the least common of those investigated. For electric power outages, adaptations that could be done at home are less likely as the outage duration increases, while those that require going somewhere are more likely as the duration increases. For water outages, all adaptations (except delaying consumption) are more likely as an outage lasts longer. Using electric power or water for medical devices and/or work and business has a large effect on the likelihood of implementing many adaptations. Preevent conservation habits are also associated with an increased likelihood of implementing adaptations. The influence of household characteristics varies greatly across adaptations. There is evidence that some adaptations tend to occur together (e.g., using water from lakes and the government) and others tend not to (e.g., delaying electricity use and going to a hotel).

A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles

Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the impact of such word-of-mouth (WOM) dissemination on the consumer's risk preferences. Moreover, even cutting-edge forecasting models rely on crude/synthetic consumer behavior models. We propose a general framework to forecast the adoption of novel products by developing a new consumer behavior model and integrating it into a population-level agent-based model. Specifically, we extend the hybrid choice model to estimate consumer behavior, which incorporates social network effects and interplay between WOM and risk aversion. The calibrated consumer behavior model and synthetic population are passed through the agent-based model for forecasting the product market share. We apply the proposed framework to forecast the adoption of autonomous vehicles (AVs) in Nashville, USA. The consumer behavior model is calibrated with a stated preference survey data of 1,495 Nashville residents. The output of the agent-based model provides the effect of the purchase price, post-purchase satisfaction, and safety measures/regulations on the forecasted AV market share. With an annual AV price reduction of 5% at the initial purchase price of $60,000 and 90% of satisfied adopters, AVs are forecasted to attain around 80% market share in thirty-one years. These findings are crucial for policymakers to develop infrastructure plans and manufacturers to conduct an after-sales cost-benefit analysis.

Public Transport Crowding Valuation: Evidence from College Students in Guangzhou

Overcrowding has grown to be an increasingly important issue for public transit in Guangzhou, China. To capture traveler benefits of reduced crowding from improved public transport, it is necessary to identify the relevant importance of crowding to travelers. This paper analyzes the disutility of crowding in metro car and bus transit with discrete choice models. Based on the stated preference survey data from college students in Guangzhou, the results show that there is non-negligible impact of crowding on passenger travel. The relationship between the disutility of crowding and standee density is not linear; that is, the disutility increases at a modest rate as the standee density increases when it is easy to move around in cars and increases rapidly when it is difficult to move around in cars. Also, there is only a slight difference between the effects of crowding on metro and bus transit.

Exploring Travel Mode Preference of External Trips for Smart City Transportation Planning: Sejong, Korea

In the face of growing concerns about urban problems, smart cities have emerged as a promising solution to address the challenges, for future sustainable societies in cities. Since the early 2000s, 67 local governments in Korea have been participating in smart city projects, as of 2019. The Sejong 5-1 Living Area smart city was selected as one of two pilot national demonstration smart cities. The main objectives of this study are to introduce the Sejong 5-1 Living Area smart city project that is currently in the planning stage, present travel and mode preferences focusing on external trips in a smart city context to be built, and analyze a mode choice model according to the socioeconomic characteristics of individual travelers. One of the distinguishing features of the Sejong smart city is its transportation design concept of designating a sharing car-only district within the city to limit private vehicle ownership to about one-third of residents, while bus rapid transit (BRT) plays a central role in mobility for external trips among four transport modes including private cars, BRT, carsharing, and ridesharing. This study was analyzed using the stated preference survey data under hypothetical conditions by reflecting the unique characteristics of the Sejong smart city transportation policy. Approximately two-thirds of respondents in the survey preferred to spend less than 1.25 USD, traveling less than 35 min on BRT trips. On the basis of the survey data, we developed a mixed logit mode choice model and found the overall model estimates to be statistically significant and reasonable. All people-specific variables examined in this study were associated with mode choices for external commuting trips, including age, income, household size, major mode, driving ability, and presence of preschoolers.

Empirical models of consumer and merchant behavior in the two-sided market of local currency

Local currency is one options for revitalization of local regions. In this study, we model consumer and merchant behavior in the two-sided market of local currency where, inter-side and intra-side interactions among consumers and merchants are considered. For the consumer side, we model whether they use local currency and if so, how much money they exchange. For the merchant side, we model whether they accept local currency payment. Empirical models were developed using stated preference survey data collected in four cities of Hiroshima Prefecture. Empirical results showed that inter-side interactions between consumers and merchants significantly affect the behaviors of both. We also found that consumers’ intra-side interaction and their level of place attachment significantly affects the behavior of consumers.

Do automated vehicle (AV) enthusiasts value travel time differently from cautious travelers? an exploration of travelers’ attitudes towards AV

ABSTRACT The anticipation of automated vehicles (AV) has resulted in diverse research topics, including understanding the adoption and usage of this emerging mode. Using attitudinal indicators, this paper identifies prevalent types of attitudes towards AV. It then explores the variation of those attitudes across different demographic groups and quantifies the influence of these attitudes on the preference for AV. The paper uses stated preference survey data collected among respondents of Chicago in 2019. The exploratory factor analysis of the attitudinal indicators identifies four factors, namely ‘Cautious Travelers’, ‘Hedonists’, ‘Utilitarians’, and ‘Safety-First’. Next, the paper develops an integrated choice and latent variable model to test the influence of these four types of attitudes on the preference for AV. The results indicate that the cautious travelers associate higher Value of Travel Time (VOTT) to AV and enthusiasts associate lower VOTT to AV than a base model without such attitudes.

An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

ABSTRACT The global electric vehicle (EV) market has been experiencing an impressive growth in recent times. Understanding consumer preferences on this cleaner, more eco-friendly mobility option could help guide public policy toward accelerating EV adoption and sustainable transportation systems. Previous studies suggest the strong influence of individual and external factors on EV adoption decisions. In this study, we apply machine learning techniques on EV stated preference survey data to predict EV adoption using attitudinal factors, ridesourcing factors (e.g., frequency of Uber/Lyft rides), as well as underlying sociodemographic and vehicle factors. To overcome machine learning models’ low interpretability, we adopt the innovative Local Interpretable Model-Agnostic Explanations (LIME) method to elaborate each factor’s contribution to the predicting outcomes. Besides what was found in previous EV preference literature, we find that the frequent usage of ridesourcing, knowledge about EVs, and awareness of environmental protection are important factors in explaining high willingness of adopting EVs.

Paving the way for autonomous Vehicles: Understanding autonomous vehicle adoption and vehicle fuel choice under user heterogeneity

Vehicle automation, along with vehicle electrification and shared mobility, may transform the existing transportation if they are handled properly. However, they may create unintended consequences if the current market dominance of fossil fuel and privately-owned vehicles persists, and travel patterns and transportation policies remain unchanged. The extent of these potential benefits and unintended consequences depends on the expected AV adoption process, people’s preferred vehicle powertrain, and AV-related policy and infrastructural support. This paper seeks to understand the impacts of attitudinal factors and roadway designs on people’s intention to use AVs and to purchase battery-electric AVs (EAVs) and gasoline-powered AVs (GAVs) under travel and user heterogeneity. Fourteen latent attitudinal factors related to the perceptions and attitudes towards AV and EV technologies, driving, the environment, and personal innovativeness were considered. An EAV-enabled urban design environments were created, featuring dedicated AV lanes, wireless charging for EAVs, and AV pick-up/drop-off zones. Using a stated preference survey data of over 1300 responses in the U.S., Multiple Indicators and Multiple Causes models are estimated to understand the relationship among various latent variables and capture heterogeneities within the population based on their sociodemographic and behavioral characteristics. The model estimation results show that the respondents’ perception of AVs and EAVs advantages, road safety improvement potential, compatibility with their lifestyles and travel needs, and their attitudes towards driving are key factors of their intention to use AVs and purchase EAVs. Furthermore, some segments of the population based on their sociodemographic and travel behavior characteristics are more likely to have a higher intention to use AVs and buy EAVs. The model estimation results and study insights can be used by policymakers to develop road network design guidelines and policies to nudge consumers towards more sustainable transportation options, minimize the unintended consequences of vehicle automation, and maximize its benefits.

Joint Estimation of Revealed Preference Site Selection and Stated Preference Choice Experiment Recreation Data Considering Attribute Non-Attendance

We estimate demand models with revealed preference (RP) site selection and stated preference (SP) discrete choice experiment marine recreational fishing data. We combine RP data from the Marine Recreational Information Program (MRIP) creel survey with SP survey data from 2003/04. RP and SP data combination is motivated by two factors. Catch rate information in the RP data are often thin, and use of SP scenarios for changes in catch can improve variation while minimizing multicollinearity. The SP data can suffer from hypothetical bias, which often manifests itself as bias in the cost coefficient. There are eight SP trip decisions and one RP trip decision for each of 1928 anglers who provided enough information to be analyzed. Joint RP-SP generalized multinomial logit models are estimated. We find that the SP travel cost coefficient is much lower than the RP travel cost coefficient in absolute value, suggesting hypothetical bias in the SP data. This difference is reflected in the willingness to pay estimates, where the SP estimates for improved catch are much higher than the RP estimates. Attribute non-attendance (ANA) arises when survey respondents ignore choice experiment attributes. We use inferred ANA methods to identify respondents who may be ignoring the SP cost variable. The generalized multinomial logit model accounting for ANA is statistically preferred. The SP cost coefficient accounting for ANA is much higher in absolute value than the SP coefficient from the model that does not account for ANA. The ANA model indicates much more consistency between the RP and SP data. The smaller difference in the travel cost coefficients is also reflected in the willingness to pay estimates. Key Words:

Stated Preference Data & Alogit

Stated Preference (SP) surveys are a form of experimental surveys in which the respondent states his/her preferences towards to an alternative out of a set of alternatives that they are presented with. The process of analysing the data collected and estimating the utility of the alternatives under investigation found through such surveys, depending on the nature of the survey design and its underlying details, can be time consuming and cumbersome. If the data is to be studied using logit models, the ALOGIT software can be used which is a powerful tool used for utility maximization and estimations of a SP survey data set. The software requires development and use of a special and often quite lengthy code. This paper presents the reader with a specific yet immensely useful computer program to be used in ALOGIT for estimations when working with SP data and logit models involving ranking and rating of alternatives.

Economic Values of Coastal Erosion Management: Joint Estimation of Use and Existence Values with recreation demand and contingent valuation data

Revealed and stated preference survey data from North Carolina households are utilized to estimate structural micro-econometric models of recreation demand and willingness-to-pay (WTP) for coastal erosion management among beach visitors and non-visitors. We test for and reject weak complementarity, implying existence values associated with management of North Carolina’s beaches. We find stronger preferences for shoreline retreat (median WTP = $22.20 per household, per year) as a management strategy relative to beach nourishment (WTP = $7.91), and we find substantially weaker preferences for shoreline armoring (WTP = $0.09). Shoreline retreat exhibits much larger estimates of existence values, whereas existence values for shoreline armoring are negative. Our data permit estimates of marginal value of incremental beach width accruing to beach users and non-users (which range from $0.23 and $0.48 per meter).

Using an integrated choice and latent variable model to understand the impact of “professional” respondents in a stated preference survey

Internet panels are increasingly used for stated preference research. Because members of such panels receive compensation for each completed survey, one concern is that over time this creates professional respondents who answer surveys solely for the monetary compensation. We identify professional respondents using data on panel tenure, survey response frequency, completion rate and total number of completed surveys. We find evidence of two types of professional respondents: “hyperactives” who answer surveys frequently and “experienced” who have long panel tenure and a large number of completed surveys. Using an integrated choice and latent variable model on stated preference survey data, we find that “hyperactive” respondents are less likely to choose the 'status quo’ and have a more stochastic choice process as seen from the econometrician's point of view, whereas “experienced” respondents have a relatively more deterministic choice process. Our results show that “hyperactive” respondents significantly impact estimated values.

Influence of Electronic-Docking Stations on China’s Dockless Bikesharing Programs: Evidence from Beijing

Electronic-docking stations have been proposed and tested by some Chinese cities to address the issue of disorderly parking of dockless bikesharing programs. We study the influence of electronic-docking stations on dockless bikesharing programs, and measure and explain riders’ preferences toward dockless bikesharing and electronic-docking stations. These research questions are addressed by a mixed logit model and regression models based on stated preference survey data collected in Beijing. We obtain some interesting empirical results which offer some guidance for governments and operators to facilitate the sustainable development of dockless bikesharing programs. For instance, we find that riders are sensitive to the density for electronic-docking stations, but somewhat indifferent to the rental fee of 1–2 yuan for each ride; the choice probability of riding a dockless shared bike drops by about 8–9% when access or egress time increases by 5 min; the fraction of riders interested in dockless bikesharing programs is close to 100%, while launching electronic-docking stations reduces this number to 88%; riders are willing to pay about 1.0–1.1 yuan to save access or egress time by 5 min; riders who live or work away from public transit stations or who often engage in physical exercises are interested in dockless bikesharing programs; and riders indifferent to the introduction of electronic-docking stations mainly include those who are above 26 years old and those with monthly income less than or equal to 8000 yuan.

Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models

Some recent studies have shown that machine learning can achieve higher predictive accuracy than logit models. However, existing studies rarely examine behavioral outputs (e.g., marginal effects and elasticities) that can be derived from machine-learning models and compare the results with those obtained from logit models. In other words, there has not been a comprehensive comparison between logit models and machine learning that covers both prediction and behavioral analysis, two equally important subjects in travel-behavior study. This paper addresses this gap by examining the key differences in model development, evaluation, and behavioral interpretation between logit and machine-learning models for mode-choice modeling. We empirically evaluate the two approaches using stated-preference survey data. Consistent with the literature, this paper finds that the best-performing machine-learning model, random forest, has significantly higher predictive accuracy than multinomial logit and mixed logit models. The random forest model and the two logit models largely agree on several aspects of the behavioral outputs, including variable importance and the direction of association between independent variables and mode choice. However, we find that the random forest model produces behaviorally unreasonable arc elasticities and marginal effects when these behavioral outputs are computed from a standard approach. After the introduction of some modifications that overcome the limitations of tree-based models, the results are improved to some extent. There appears to be a tradeoff between predictive accuracy and behavioral soundness when choosing between machine learning and logit models in mode-choice modeling.

Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays

Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers’ route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers’ routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies.

Understanding stated neighborhood preferences: The roles of lifecycle stage, mobility style, and lifestyle aspirations

Stated neighborhood preference is informed by a host of factors describing an individual’s socioeconomic condition, current environmental context, recent transportation decisions, and future aspirations for certain housing and accessibility attributes. To date, travel applications and behavioral models investigating the residential location choice process have been parsimonious in differentiating decision-makers and the available neighborhood supply. This Portland, Oregon study used stated preference survey data and an integrated choice and latent variable modeling framework to provide a new awareness of the relationships between an individual’s lifecycle stage, mobility style, lifestyle aspirations, and underlying preference in residential environment. Specifically, we found that aspirations regarding housing and accessibility were strong predictors of stated neighborhood preferences, while mobility style was also closely associated with these lifestyle aspirations. Also, consistent with a narrative of gentrification, high income households preferred more urban environments with better multimodal access.