Stated Preference Data Research Articles

Sea‐Level Rise, Drinking Water Quality and the Economic Value of Coastal Tourism in North Carolina

AbstractWe estimate the economic benefits of avoiding reductions in drinking water quality due to sea level rise accruing to North Carolina (NC) coastal tourists. Using stated preference methods and responses from recent coastal visitors, we find that tourists are 2%, 8%, and 11% less likely to take an overnight trip if drinking water tastes slightly, moderately, or very salty at their chosen destination. The majority of those who decline a trip would take a trip to another NC beach without water quality issues, others would take another type of recreational trip, with a minority opting to stay home. Willingness to pay for an overnight beach trip declines with the salty taste of drinking water. We find evidence of attribute non‐attendance in the stated preference data, which impacts the regression model and estimates of the willingness to pay for trips. Combining economic and hydrological models, annual aggregate benefit losses due to low drinking water quality could be as high as $232 million by 2040.

Urban Air Mobility Passengers’ Profiling: Evidence from Milan Airports, Italy

Urban air mobility (UAM) is expected to offer new travel options for passengers to and from airports in the near future, despite uncertainties associated with regulatory issues, environmental concerns, and societal impacts. This paper analyzes the socioeconomic and behavioral factors that could influence users’ modal choices (including UAM services) for accessing and egressing airports. Using revealed and stated preference data collected at Milan airports (Italy), mixed logit and hybrid choice modeling specifications are estimated and compared with profile potential UAM passengers. Our findings suggest that the level of service, socioeconomic factors, and trip-related variables explain passengers’ choices better than latent traits such as fear of flying, propensity for technological advances, and expectations about the safety of UAM services. In other words, the additional complexity of hybrid choice modeling is not justified by the slight gain in likelihood compared with the estimated mixed logit model. The results also indicate that high-income individuals traveling for business purposes are the most likely demand segment to use UAM services, at least initially. Moreover, highly educated individuals and employees who have their travel expenses reimbursed for work trips are less likely to choose UAM services for airport access or egress, preferring traditional ground taxis.

Investigating work commuters mode choice behavior in Dalian city during pandemic: a hybrid choice model approach

ABSTRACT This study examined commuters' mode choice behavior during the COVID-19 pandemic, focusing on the impact of both tangibles and intangibles factors. To do so, the authors developed a Hybrid Choice Model (HCM) which investigated the effects of sociodemographic characteristics, travel attributes, and intangible factors such as satisfaction, safety, and discomfort, on travel behavior. The model was tested using Stated Preference data collected from commuters in Dalian City (China) via wenjuanxing, an online survey platform. The results indicate that individual characteristics played a significant role in shaping people's perceptions and mode choice behavior was influenced by sociodemographic factors, travel characteristics, and the aforementioned intangible factors. More specifically, individuals, who perceived a higher level of safety, measured in terms of worries about robbery, accidents, and pandemic concerns, were less likely to choose cars. People with a stronger perception of discomfort favored metro over bus. Private car users reported higher satisfaction. The findings will be useful to policymakers for establishing more resilient transport solutions.

Impact of Student Backgrounds on the Outcomes of Maritime Postgraduate Professional Education

Maritime taught postgraduate education plays a vital role in cultivating expertise within the ever-evolving global logistics industry. Notably, the diverse backgrounds of students are of great importance in this context, given the scarcity of undergraduate maritime programs and the industry’s demand for professionals from various disciplines. However, this diversity also poses a challenge to educators in terms of effectively training and evaluating these students. This study focuses on a maritime taught postgraduate professional program and investigates how student background characteristics affect their learning outcomes and academic performance. By utilizing both stated preference data (SP) and revealed preference data (RP) collected from a postgraduate maritime professional program in Hong Kong, the study aims to mitigate hypothetical bias and gain a comprehensive understanding of the factors influencing student perceptions and experiences in maritime industry. The study measures five dimensions of student learning outcomes, including professionalism, strategic thinking, lifelong learning capability, career development, and academic performance. The findings suggest that student background characteristics have minimal impact on maritime learning outcomes, with only gender and undergraduate programs showing some influence. The study highlights the inclusive nature of maritime professional education, emphasizing that training quality, rather than personal background, is the key determinant of success.

Stated and Revealed Preferences Towards Bus Transport in Sri Lanka

This study aims to identify the attributes of public transportation buses (PTB) preferred by passengers, which will be perceived as the buses’ quality of service. PTB of Sri Lanka faces several issues impacting its quality, efficiency, and modal share. Understanding user perceptions is essential to enhancing service quality and addressing the challenges that deter bus usage. Thirteen shortlisted attributes were rated by 84 (61 Male and 23 Female) respondents, and the top five attributes were selected. The study gathered user preferences towards PTB using Stated Preference (SP) and Revealed Preference (RP) methods for the five chosen attributes. RP data were modelled using a multiple linear regression model with R language. The data set comprised 192 (127 Male and 65 Female) valid responses. SP data showed that the respondents favoured medium-speed buses (51.2%) that were clean and reliable. Time of arrival was the most crucial attribute (30.3%), followed by vehicle condition (24.3%). Segment analysis revealed that gender and vehicle ownership significantly impacted preferences. Both gender and vehicle ownership segments prioritised arrival time and least valued seat comfort. The study emphasises the importance of prioritising punctuality over comfort. The findings of this study could provide valuable insights for policymakers to design more user-centric strategies, potentially enhancing the efficiency and attractiveness of bus services in Sri Lanka.

Has the COVID-19 pandemic affected travellers’ willingness to wait with real-time crowding information?

Travel preferences in public transport (PT) have been substantially affected by the COVID-19 crisis, with rising emphasis on on-board safety and comfort aspects. Hence, real-time crowding information (RTCI) might have become even more instrumental in supporting travel decisions in congested urban PT systems. This study investigates the willingness to wait (WTW) to reduce (or avoid) overcrowding with RTCI in urban PT (bus and tram) journeys, analysing pre- vs. post-COVID travel behaviour attitudes. Stated-preference data and (subsequently estimated) choice models indicate that, while the pre-COVID WTW was primarily driven by mere possibility to avoid an overcrowded first departure, the post-COVID propensity to wait is strongly associated with expectations of seat availability in second departure as well. The ex-post WTW with RTCI seems to have become less-dependent on individual characteristics and more prominent for time-critical (obligatory) trips as well. Our findings underpin the rising relevance of passenger overcrowding in urban PT journeys. Moreover, they help better understand the potential of RTCI in post-pandemic recovery of PT ridership.

Exploring reference-dependency in route switching behavior on intercity travel: Endowment effect and disparities between willingness to pay and willingness to accept

This study explores asymmetric preferences in route switching behavior on intercity travel. A reference-dependent travel choice model reflecting the endowment effect is empirically compared with the classical symmetric travel choice model to investigate the significance of the reference-dependency. Based on the application to stated preference data, our results reveal that the reference-dependent travel choice model outperforms the classical symmetric travel choice model in route switching decision, indicating the significant disparities between WTA and WTP. It implies that policy making based on the results of the classical symmetric model assuming equivalent WTA and WTP could lead to the overestimation of switching demand. Furthermore, our results show that drivers are more heterogeneous in terms of WTA than WTP. Moreover, our applications of the latent class mixed logit structure indicate that most of the responded drivers show reference-dependency in route switching decision. Our findings in this study are essential in transport network policy making.

Preferences and welfare impacts of new metro serving airport access

The construction of a new rail infrastructureto connectairportswith cities plays a vital role in enhancing the accessibility of airport ground access and mitigating negative externalities resulting fromautomobiles. To identify air travelers’ preferences for airport ground access by considering a new metro line in Taiwan, we develop nested logit models, which combine revealed and stated preference data. The results reveal significant effects of travel time, travel cost, and individual characteristics; the metro and taxis are highly substituted for airport access. Consumer surplus or accessibility is not equally distributed across air travelers. Travelers who are female, young adults, lower income, and without vehicles in the household obtain considerable consumer benefits from using the new metro. Policy incentives to time and cost have a small impact on the increase in metro share, but consumer surplus surges considerably.

Analysis of consumer preferences for new electric vehicle technologies: Can future vehicle steering system steer consumer's purchase intention?

As the mainstream of vehicle industry is moving from internal combustion engine vehicles to electric vehicles (EVs), a multi-perspective analysis of consumer preference for the new EV technology is required. Therefore, this study proposes a new framework for consumer preference analysis—a two-stage choice experiment—and conducts an empirical analysis of the four-wheel independent steering system (4-WISS). In the first stage, we analyzed consumer preference for the availability of 4-WISS in EVs. In the second stage, we investigated consumer preference for the specific technology of 4-WISS in EVs. Stated preference data were collected through a choice experiment survey, and a mixed logit model was used to quantitatively analyze consumer preference and willingness-to-pay. Results show that consumers are willing to pay KRW 19,045,098.6 (USD 13,513.9) to use 4-WISS in high-end EVs. Furthermore, consumers who are female, older, and have more knowledge of autonomous vehicles prefer 4-WISS more. We found that consumers prefer the parallel movement among 4-WISS modes, the touch display for selecting 4-WISS, the steering wheel for operating 4-WISS mode, and dashboard for displaying information. We finally discuss the new business model in the vehicle industry and suggest automakers to establish an effective strategy for the initial market for EVs with 4-WISS.

The role of linear green infrastructure for cycling: A literature review

The transport system, especially at the urban level, is dramatically unsustainable due to the predominant use of motor vehicles. One of the most promising solutions is to encourage a modal shift towards active mobility, but effectively promoting walking and cycling is a chal- lenge. Although there is an abundant literature on the importance of the built environment and cycle routes to foster cycling, very little is known about the role of linear green infrastructure. The aim of this literature review is to summarise the main empirical evidence on this topic and to detect the gaps in the existing literature. To this end, we selected 111 papers and ana- lysed them in terms of the methodology used to collect and analyse the data and the factors that condition the impact of linear green infrastructure on cycling. We found that the majority of the papers are based on stated preference data and that the description used to characterise the linear green infrastructure is highly heterogeneous, reducing the comparability of the re- sults obtained. Overall, the impact on modal shift is positive, but it is significantly conditioned by the co-existence of cycle lanes and a number of additional factors related to the built envi- ronment and the socio-demographic characteristics of the respondents.

Modeling choice determinants for bicycle-bus integration in developing countries: Case study in Fortaleza, Brazil

In the context of first/last mile, the bicycle-transit intermodality could lessen accessibility problems of stations and stops, reducing the need for feeder services. Although extensively addressed in developed countries, bicycle-transit is less studied in Global South metropolises, where distinct usage patterns are observed. Thus, this research aimed to model the choice behavior of the low-income population for bicycle-transit integration in Fortaleza - Brazil, a large Latin American city with a deployed bike-sharing system focused mainly on bike-bus integration. A Stated Preference (SP) survey was conducted addressing socioeconomic and trip characteristics, as well as policy variables such as bicycle parking and cycleways/lanes. SP data allowed the estimation of nested and mixed logit models representing choice behavior, and sample segmentation was used to identify heterogeneity among different groups of individuals. The results show that investing in bicycle infrastructure could stimulate bike-bus integration in Fortaleza; however, their relative importance depends on the analyzed individual strata. Findings also indicate the influence of public and road safety issues, as well as gender, income, and trip characteristics such as purpose and access distance.

Exploring Sustainable Urban Transportation: Insights from Shared Mobility Services and Their Environmental Impact

The transportation landscape is witnessing profound changes due to technological advancements, necessitating proactive policy responses to harness innovation and avert urban mobility disruption. The sharing economy has already transformed ridesharing, bicycle-sharing, and electric scooters, with shared autonomous vehicles (SAVs) poised to reshape car ownership. This study pursues two objectives: firstly, to establish a market segmentation for shared ride services and secondly, to evaluate the environmental impact of ridesharing in different contexts. To mitigate potential biases linked to stated preference data, we analysed the navette service, utilized by a research institute in Europe, closely resembling future SAVs. The market segmentation relied on hierarchical cluster analysis using employee survey responses, while the environmental analysis was grounded in the 2019 navette service data. Our analysis revealed four unique employee clusters: Cluster 1, emphasizing active transportation and environmental awareness; Cluster 2, showing openness towards SAVs given reliable alternatives are available; Cluster 3, the largest segment, highlighting a demand for policy support and superior service quality; and Cluster 4, which places a premium on time, suggesting a potential need for strategies to make the service more efficient and, consequently, discourage private car use. These findings highlight a general willingness to adopt shared transport modes, signalling a promising transition to shared vehicle ownership with significant environmental benefits achievable through service design and policy measures.

Impact of risk perception and trust in autonomous vehicles on pedestrian crossing decision: Navigating the social-technological intersection with the ICLV model

In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians’ tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians’ willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems.

Relax on the way to work or work on the way to relax? Influences of vehicle interior on travel time perceptions in autonomous vehicles

The impending arrival of autonomous vehicle (AV) technology has the potential to transform how individuals perceive time spent travelling. By removing the need to drive and pay attention to the road, AV users could perform other activities, including those for work or leisure. As a result, AVs are expected to lower the burden of travel and, therefore, the value of travel time (VOTT). Despite the significant impacts that AVs may have on individuals’ choices and the transportation system, few have studied their impacts on travel time perceptions, and even fewer have examined the extent to which these impacts will vary depending on the types of tasks that can be performed within an AV. This study uses stated preference data collected in Fall 2022 to develop mode choice models and subsequently quantify how the availability of three types of AV: privately-owned, exclusive, and pooled AV may shift perceived travel times in the Greater Toronto and Hamilton Area. The error-component mixed logit models highlight the cross-nesting between privately-owned AVs and driving. In addition, this study is the first in Canada to distinguish the VOTT reductions by AV type, trip purpose, and interior description (which caters to different tasks). VOTT reductions as large as 42% less than driving a conventional vehicle were estimated. The results of this study provide additional empirical evidence for AV VOTT reductions (particularly in the Canadian context) and can be used to help craft policies in preparation for the arrival of AVs.

Optimisation of trip end service for international container sea-rail intermodal transportation – a case of China–Korea container railway ferry

ABSTRACT An innovative transportation mode for exporting containers from Korea to Europe, involving a combination of port-access liner train, Korea–China train ferry, and CR-Express train, is presented with the objective of maximising transportation profits of the intermodal system, A bi-level model is built, in the upper level, the model determines the location and departure frequency of port-access liner train in Korea, as well as the departure frequency and vessel size of Korea–China train ferry, while in the lower level, the model calculates the choice probabilities of the paths taken by the containers and the corresponding link traffics. A survey is conducted, including both RP (revealed preference) and SP (stated preference) surveys on containers’ path choices, for calibrating the transport utility function by fusing the RP and SP data. The case study shows that for the port-access train, it is optimal to locate the terminals in Seoul and Busan, and to dispatch 11 and 1 trains per week, respectively.

Studying shared bike route choice behavior using a bike-sharing system in Germany

The objective of this research is to explore the determinants of shared bike route choice in both urban and rural settings in Germany. To this end, a stated preference (SP) survey on route choice was conducted across several German cities. Participants were drawn from the service area of a regional bike-sharing system (BSS) in Germany. This area included five major cities and several mid-sized and smaller municipalities. The survey includes responses from both users and non-users of the BSS. Mixed multinomial logit models were used in analysis of the SP data. The study also calculated the willingness to pay (WTP) values for selected attributes. Significant influences on route choice included access and egress time, ride time, and ride cost. Significant random heterogeneity was found, especially for ride cost. Further heterogeneity was reported for interactions with sociodemographic attributes. Street type, surface, and bike infrastructure also had significant effects on route choice. Linkages between infrastructural preferences and respondents’ ages were evident. Although WTP values were strongly influenced by random heterogeneity in ride cost perception, most WTP values were comparable to the values obtained in other studies.

Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future.

Towards Smart Parking Management: Econometric Analysis and Modeling of Public-Parking-Choice Behavior in Three Cities of Binh Duong, Vietnam

In developing cities, newly emerging cities have started facing the problem of insufficient public parking facilities and ineffective regulations. To support the planning, design and management of the public parking system towards a smart and sustainable city vision, it is necessary to study deeply parking behaviors. This paper presents an empirical study on parking-choice behaviors of motorcycle users and car users in the emerging cities of developing countries through a case study of three cities in Binh Duong, Vietnam. To explore the behavioral mechanisms and influential factors, the multinomial logit parking choice models are developed using revealed preference and stated preference data. The users’ overall satisfaction and perceived importance of parking lot design and service aspects are analyzed using order logistic regression. The revealed choices show no trade-off between parking fee and walking distance, as the users are not fully aware of parking locations and service features. However, the stated choice experiments prove a potential existence of the trade-off mechanism and differentiate significant factors in the decision of choices for the two user groups. The results bring insightful implications for the development of a smart public parking system.

Statistical inference of travelers’ route choice preferences with system-level data

Traditional network models encapsulate travel behavior among all origin–destination pairs based on a simplified and generic travelers’ utility function. Typically, the utility function consists of travel time solely, and its coefficients are equated to estimates obtained from discrete choice models and stated preference data. While this modeling strategy is reasonable, the inherent sampling bias in individual-level experimental data may be further amplified over network flow aggregation, leading to inaccurate flow estimates. In addition, individual-level data must be collected from surveys or travel diaries, which may be labor-intensive, costly, and limited to a small time horizon. To address these limitations, this study extends classical bi-level formulations to estimate travelers’ utility functions with multiple attributes using system-level data. This data tends to be less subject to sampling bias than individual-level data, it is cheaper to collect and it has become increasingly diverse and available. To leverage system-level data, we formulate a methodology grounded on non-linear least squares to statistically infer travelers’ utility function in the network context using traffic counts, traffic speeds, the number of traffic incidents, and sociodemographic information obtained from the US Census, among other attributes. The analysis of the mathematical properties of the optimization problem and its pseudo-convexity motivates the use of normalized gradient descent, an algorithm developed in the machine learning community that is suitable for pseudo-convex programs. More importantly, we develop a hypothesis test framework to examine the statistical properties of coefficients attached to utility terms and to perform attribute selection. Experiments on synthetic data show that the travelers’ utility function coefficients can be consistently recovered and that hypothesis tests are reliable statistics to identify which attributes are determinants of travelers’ route choices. Besides, a series of Monte-Carlo experiments showed that statistical inference is robust to various levels of sensor coverage and to noises in the Origin-Destination matrix and the traffic count measurements. The methodology is also deployed at a large scale using real-world multi-source data in Fresno, CA, collected before and during the COVID-19 outbreak.

Estimating the ex ante adoption determinants of novel cropping systems with nonpecuniary benefits

AbstractLandowners who value the nonpecuniary benefits of novel cropping systems may be more likely to adopt those systems in response to new market opportunities. We examine ex ante adoption decisions for bioenergy crops in Wisconsin using stated preference data from nonoperator landowners. Bioenergy crops produce biomass, a private good, and may jointly contribute to civic or environmental public goods. We find that pecuniary and nonpecuniary benefits influence landowner decisions, but the effects differ by annual and perennial crop types. Landowners may trade pecuniary for nonpecuniary benefits when equating the marginal utility of land uses, resulting in lower willingness‐to‐accept values for adoption.