Revealed Preference Survey Research Articles

Public transport mode choice behavior of different-income passengers during large-scale public health incidents

Public transport ridership has been hit hard by the COVID-19 pandemic in many countries. Investigating passengers' public transport mode choice behavior during large-scale public health incidents can uncover the major influential factors and help propose policies and strategies to reduce the pandemic transmission and recover the public transport revenue. This study develops an integrated choice and latent variables (ICLV) model by income based on structural equation model to model passengers' public transport choice behavior during the normalized stage of the pandemic. The model considers passengers' socioeconomic attributes, travel attributes, and attitude-perception attributes, and can appropriately capture passengers' psychological latent attributes. Taking Beijing China as an example, we collect some revealed preference survey data online. The modeling results show that the risk perception as a mediator variable has a significant impact on mode preference. Moreover, the convenience of public transport has the largest influence on risk perception. These findings suggest that risk perception and the convenience of public transport play a major role in passengers' mode choice behavior. In addition, the impacts of the various influential factors on the public transport mode choices are significantly different across different income groups. Further, the ICLV model can achieve better performance and is superior to the traditional Multinomial Logit model. The modeling framework can help propose targeted and instructive strategies during the normalized stage of the pandemic by uncovering the major influential factors in passengers’ public transport mode choices, which is applicable to similar pandemics in the future.

A behavioral explanation of the activity-space segregation: Individuals’ preference of choosing an activity destination

The difference in the individuals’ preference of activity destination choice is a new explanation for the activity-space segregation. This study investigates individuals’ preference in the destination choice for their daily activities. It uses revealed preference survey for the choice of the activity destination, and mobile phone dataset for the ambient population at the activity destination in Guangzhou, China. It has found that (1) the activity-space segregation is strongly influenced by the residential segregation, but disadvantaged populations are more spatially constrained by the distance decay effect; (2) all individuals prefer a destination with high diversity of built environment; and (3) migrant people tend to be self-segregated at the activity space, but people with higher education status prefer to take activities at an integrated place.

Statistical modelling of travel mode choice of public university students in Freetown, Sierra Leone: the case of three campuses

ABSTRACT Though much research has been conducted on students’ travel behavior around the world, not much is known about university students in Sierra Leone. This study investigates the most common transport modes amongst public university students, the factors influencing their preference, and the impact of their choice on the environment. Through a Revealed Preference survey, using the traditional paper-and-pencil method combined with a smartphone-assisted interviewing technique, 632 students were sampled. Discrete travel choice models were developed and estimated, and the methodology was demonstrated for the three campuses under the University of Sierra Leone. The study found that more than one-third of students preferred the paratransit mode, ‘podapoda’, followed by taxi, two and three-wheelers. According to the mode choice model results and exploratory analysis, sociodemographics, travel time, cost, and trip distance are the main factors considered by students’ for selecting a transport mode. Students varied widely in their assessment of how their mode choice affects the environment. These findings highlight the need for a sustainable transport system for university trips. Acquiring appropriate transport modes, promoting active travel, and developing student residences with shorter commutes will ensure long-term land management. This will promote the sociocultural, economic, educational, and ecological aspects of campus growth.

Potential Demand for E-grocery Delivery Services: The Effect of Delivery Attributes on Consumers Preferences

Because of the COVID-19 pandemic, the e-grocery sector has experienced a boom in demand. Even now that the emergency is over, consumer demand continues to grow. Therefore, it is helpful to analyze what factors drive consumers' choices and what could be expectations for the near future. This work uses a discrete choice experiment to investigate the potential e-grocery demand (goods and services). For this purpose, a stated and revealed preference survey was developed to highlight the intention to switch from the traditional to the digital market, also considering the consumers' interest toward delivery and collection preference. The results indicate that even though most people are satisfied with their current behavior in the traditional market, the e-grocery market is seen as an opportunity for convenience and flexibility, low fares, and a precise delivery organization for both click and collect or home deliveries that meet the needs of consumers.

A framework for assessment of pedestrianization impacts on quality of life: Combining subjective and objective measures

Pedestrianization is a useful strategy to tackle the challenges resulting from increased vehicular movement on the road that adversely affects the Quality of Life (QOL). Evidence from developed economies shows that pedestrianization has an optimistic influence on cities by advocating liveability and QOL. However, more information is needed about the effect of pedestrianization on QOL in developing economies like India. This study examines the QOL consequences of the pedestrianized urban street by integrating subjective information obtained through revealed preference surveys and objective street environment conditions. The research involves data collected when the pedestrianization measure was implemented in an urban street in Bengaluru, India, as a testbed. The strategy was enforced on all weekends from November 2020 to February 2021. The association between perceived QOL and latent factors is analyzed using the ordered response model to estimate the subjective QOL indicator. The principal component analysis is applied to objective variables including pollution, mobility, and cleanliness, to determine the Objective QOL indicator. Then, the subjective and objective QOL indicators are linearly aggregated to formulate a composite Local QOL index. Results from subjective analysis indicate that satisfaction with health, safety, comfort, and security due to pedestrianization positively affects QOL. Males and married people exhibit positive QOL consequences compared to females and unmarried people. Pedestrian footfall and pollution levels hold high weightage in determining the Objective QOL indicator. The combined evaluation demonstrates that the composite index falls under the category “A” explaining a strong positive influence on QOL. The proposed composite index classification can serve as a unique tool to evaluate the outcomes of pedestrianization measure and support decision-makers in effective strategy enforcement.

Logistic Regression Application in Determining Mode Selection Factors of Musi Emas Feeder Transport, Palembang City

Mode of transportation is absolutely a basic human need, especially in urban areas which tend to have high mobility. One type of mode that an important role in the urban transportation system is public transportation. oday, public transportation continues to make updates, one of which is public transportation in Palembang City marked by the start of operation of the Musi Emas feeder city transport in July 2022 with 7 routes. This study aims to identify the factors that influence and become the parameters of the traveling community in choosing mode of Musi Emas feeder city transportation Palembang City on the route corridor 1 Talang Kelapa – Punti Kayu, and corridor 2 Asrama Haji – Sematang Borang. The research approach used was quantitative with primary data sources from a revealed preference survey of 208 respondents who had used the Musi Emas feeder transport, and 192 respondents who had never used it. Analysis of the results of the survey data was modeled by logistic regression in order to look for factors that have an influence on choosing the mode of transportation of the Musi Emas feeder. The results of the analysis show that the independent variable data has a significant effect on variable dependent and the results of logistic regression analysis showed variable gender, age, and travel costs are influential variables in determining the choice of travelers to use the Musi Emas feeder city transportation mode.

A STUDY ON INTERSTATE FREIGHT MODE CHOICE BETWEEN TRUCKS AND TRAINS USED TO TRANSPORT OIL PRODUCTS: A CASE STUDY OF IRAQ

Freight mode choice is an important stage in shipping demand modeling. Transporting oil products using trucks between cities has several issues, including increased risk of accidents, higher transportation costs, environmental impact, and traffic congestion. Due to the absence of local studies, in addition to the few global studies that deal with freight mode selection used to transport oil derivatives between cities, the aim of the study is to develop transport mode choice models for trucks and trains transporting oil products between the interstate capital of Baghdad and the government of Basra for export. The revealed preference survey was used for 277 goods transport data collected with the help of personal interviews, questionnaires, and government institutions. The collected data were processed using two statistical approaches: binary logistic regression and Firth logistic regression. The study’s findings assist decision-makers in selecting sustainable modes of transport.

On-demand transit user preference analysis using hybrid choice models

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT services and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

Empowering revealed preference survey with a supplementary stated preference survey: demonstration of willingness-to-pay estimation within a mode choice case

Mode choice models play a pivotal role in transport demand modelling and help transport planners, engineers and researchers with policy and infrastructure investment evaluation. Recent mode choice studies primarily use revealed preference (RP) data to reflect individuals’ true behaviour. However, this may not be the best practice, given the lack of information in RP data. This study uses a nonlinear utility specification for a multinomial logit mode choice model development using high-quality travel data collected by a GPS-based smartphone application complemented by stated preference (SP) data. The model results highlight the impact of sociodemographic variables on mode choice behaviour and individuals’ willingness-to-pay (WTP) when the model is jointly developed compared to stand-alone SP and RP models. The main message of this study is that in addition to collecting RP, which is a reliable and unbiased source of data, collecting complementary SP data is beneficial as it provides information that is not otherwise available in RP data. This may include a proper variation in the public transport cost variable as demonstrated in this study. Moreover, to better understand the travellers' behaviour regarding the trade-off between time and cost a mixed multinomial logit (MMNL) model in the willingness to pay space is developed on the SP data. capturing the unobserved heterogeneity within the estimated WTPs, the MMNL model outputs reveal a higher variation in WTP of car in-vehicle travel time compared to bus in-vehicle travel time.

Determining Residential Location Choice along the Coastline in Victoria Island, Nigeria Using a Factor Analytical Approach

This paper evaluates how households consider environmental attributes alongside other housing attributes in their residential location decisions along the coastline in Victoria Island, Nigeria. The data were obtained from tenants’ revealed preference surveys where 204 respondents rated 15 identified and most common key factors in the order by which they influence their residential location choices. The factor analytical approach was then applied to understand how these factors influence such decisions. Thus, this study also gives bearing to the factors considered in making policy and/or investment decisions around residential location choice (RLC). The results revealed the presence of four key components with a total variance of 70.76%. Among the components, neighbourhood, critical dwelling cum socio-economic attributes are found to have a significant influence in explaining 39.78% of the variation in the factors influencing the study area’s selection as the residential location choice (RLC) for households. The findings have implications for households’ residential location choices. For residential locations to be equally attractive to tenants, policymakers and urban planners should pay attention to addressing the menace of the neighbourhoods including crime and traffic congestion. Moreover, property owners should respond to tenants’ needs by paying attention to the provision of utility facilities such as portable water and an appropriate mix of toilet/bathrooms to available bedrooms.

Understanding factors that impact ridesourcing service usage frequency: a case study in Shanghai

ABSTRACT This study investigates the influencing factors that impact ridesourcing service usage frequency and explore the potential similarities and differences among groups of population based on their primary usage purposes. A revealed-preference survey developed for this study was conducted among 783 ridesourcing service users from Shanghai, China in September 2020. Separate random parameters ordered probit models were estimated for users with different primary purposes of usage to capture unobserved heterogeneity. The identified influencing factors include travelers’ sociodemographic characteristics, reasons to choose ridesourcing services, and other behavioral characteristics. In addition, the impacts of these contributing factors were different based on their primary usage purpose. The model estimation and descriptive statistics findings suggest that groups of ridesourcing service users may respond differently to various types of promotional strategies. The study insights may be used to design future strategies that can potentially improve the service usage frequency of existing users and attract new users.

Intercity Mobility Assessment Facing the Demographic Challenge: A Survey-Based Research.

The key factor in moving towards a more sustainable travel model is based on improving mobility, especially in rural areas that share territorial dynamics with urban areas and are connected by a daily flow of inhabitants. The purpose of this article is to carry out a diagnosis of the daily mobility patterns of the inhabitants of a number of rural municipalities, with the aim of promoting sustainability and mitigating the phenomenon of territorial depopulation in future local planning policies. The research methodology is based on the use of revealed preference surveys together with accessibility analysis using GIS tools, allowing for an in-depth knowledge of the mobility patterns of the municipalities in the area under analysis. In this respect, the reference parameters in terms of territorial accessibility are determined by applying the network analysis procedure to basic public services. The results reflect the existence of an unbalanced modal split with a preponderance of private vehicle use (regardless of the destination or the reason for the journey). In addition, a very weak inter-municipal connection dynamic is observed. There is a knowledge gap in the verification of the long-term suitability of sustainable measures in rural areas implemented after the development of mobility plans (in order to assess their effectiveness).

Analysis of gender-specific bicycle route choices using revealed preference surveys based on GPS traces

Bike facility features in urban transport systems are one of the most important elements for encouraging user choices regarding sustainable transport modes. The process of designing the bikeway does involve biker perception but the act of designing does not often rely on this perception. In order to identify whether gender differences exist for bike route choices, the actual choices made by bikers - both male and female - have been detected by means of GPS data, with the pathways characteristics being known. Detected route choices have been analyzed using the Oaxaca-Blinder decomposition method (Blinder, 1973; Oaxaca, 1973), which provides a possible explanation for differences in gender-specific route attributes that male and female cyclists experience under similar conditions. The results show that differences between female and male cyclists exist in terms of the ease of use of the pathways and related choices. Some analyses regarding age classes have also shown that gender differences tend to be less relevant with increasing age, thus suggesting that more-experienced female cyclists make choices similar to those of their male counterparts.

In general terms, discrete choice models are calibrated using data obtained from Revealed Preference (RP) and Stated Preference (SP) surveys. In transportation planning, one of the main sources of data is the Origin/Destination (O/D) Survey, which is an R

In general terms, discrete choice models are calibrated using data obtained from Revealed Preference (RP) and Stated Preference (SP) surveys. In transportation planning, one of the main sources of data is the Origin/Destination (O/D) Survey, which is an RP survey and describes the actual choices and behaviors of individuals. However, it is not possible, through this source, to characterize the alternatives not chosen. This study has two related aims: (1) to propose a criterion to characterize the travel mode alternatives using RP data, and (2) to test the improvement of travel mode choice estimates based on including characteristics of alternatives. First, the CART (Classification and Regression Tree) algorithm was used to characterize the travel times of the travel modes available in the study area (city of São Paulo, Brazil). The trips were classified according to independent variables selected by the algorithm, and average travel time values were obtained for five travel mode alternatives – information not previously available in the RP survey. Finally, the improvement of discrete choice modeling, based on including average travel times, was tested using a validation sample and performance metrics, such as Hit rates and LogLikelihood values. An increase in estimates was observed from including travel duration, and the proposed method is an academic contribution to the modeling based on RP data.

Estimation of Cross Classified Value of Travel Time Using Binary Logit Model on Egyptian Roads

Objective: In this study, cross-classified categories of Value of Travel Time (VOT) are estimated using the binary logit model for the users of both public and private vehicles who have diverse income levels and go on different trips. The travelers’ income level has a large impact on their VOT and on their willingness to pay regarding other factors that impact the travelers’ choice of the most suitable means of transport for them. Methods: The research uses the combined stated and the revealed preference survey and applies the logistic regression analysis to estimate the categorized VOT on Egyptian roads. The data sets consist of 1123 respondents who were approached to collect information related to the socio-economic factors and the trips’ characteristics. Each respondent was requested to choose an alternative to each of the choice sets’ levels. All data sets were collected in light of the Binary Logit analysis and recorded concerning the socio-economic factors. A total of 33 models were developed to predict the VOT. Results: According to the given analysis, VOT for private car owners (32.5 LE/HR about 2.06 $/ HR) is higher than that for public transport users (18.3 LE/HR about 1.16 $/ HR) in Egypt. As for the diverse income level groups, a positive correlation is found between each traveler and his/her income. To clarify, the travelers’ VOT increases when their income levels improve. Conclusion: Concerning the purpose of the trip, the VOT reaches its peak for travelers who travel for the sake of work as their VOT is approximately 38.82 LE/HR, about 2.47$/HR compared to the other travelers who have different traveling purposes other than doing business.

Ridesourcing mode choice: A latent class choice model for UberX in Chile

As shared mobility expands, ridesourcing has become its most popular manifestation. However, users’ mode choice has not yet been sufficiently explored. Thus, this study aims to model ridesourcing mode choice across different latent classes to ascertain who chose ridesourcing and why.We conducted a mode choice study by collecting revealed preference surveys from UberX users in Viña del Mar, Chile, in 2017. We then determined the existence of two latent classes and modeled the mode choice using a latent class choice model. Ultimately, we characterized individuals belonging to each latent class and calculated the subjective value of time (SVT).Most UberX users were highly educated and aged 20–35 years. Further, UberX gained users principally from public transport (80%). Likewise, the two latent classes differed by socioeconomic characteristics and SVTs. A latent class grouped the highest-educated and highest-earning users, who also offered the highest SVT.In summary, two latent classes, differentiated by educational level and income, formed the ridesourcing market. Besides, they offered distinct ridesourcing choice behavior based on the widely dissimilar SVTs. There was also a strong substitution effect between ridesourcing and transit use. The results imply that policymakers and transportation planners could have increased the competitiveness of the public transit system by improving rapidity and safety, having room to increase the fares to defray the improvements. Further, they could have used information related to the latent classes to customize relevant policies and marketing strategies (routes, frequency, fares, etc.) for every latent class.

Deriving transport appraisal values from emerging revealed preference data

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from demand models is the Value of Travel Time (VTT), which provides insights on the trade-offs that travellers are willing to make in terms of travel time and travel cost. VTT estimates are a critical input to cost-benefit analyses and feasibility assessments of potential projects and thus play a crucial role in transport planning and policy decisions. While much of the early work on VTT made use of revealed preference (RP) data, their use decreased due to growing concerns about reporting errors that may result in omitted observations and measurement errors in the model inputs. As a consequence, VTT measures have, for the last two decades, primarily been estimated using state-preference (SP) surveys. While SP methods can assess the individual trade-offs in a controlled manner, they are prone to behavioural incongruence. More recently, RP data from passively-collected data sources have raised the promise of accounting for some of the limitations of traditional RP surveys due to the minimal (or even no) active input from the respondent. The present study utilises such a dataset that combined a 2-week trip diary captured through smartphone GPS tracking with a household survey containing individual socio-demographic information. A mixed Logit model for mode choice was specified and the estimated parameters were then applied on the National Travel Survey to calculate the VTT estimates. Those estimates were further adjusted based on trip distances to get more representative national VTT values. This process resulted in estimates similar to the official UK guidelines used in transport appraisal that were obtained from SP data, where our results are not affected by concerns about response quality or survey artefacts. The findings hence strengthen the case for shifting towards passively generated RP data sources and are important for transport practitioners.

Modelling parking type choice behavior

• An econometric parking choice model is developed to investigate parking choice behavior. • We derive cruising and walking time valuations. • Drivers are willing to pay more for reducing cruising and walking time. • Trip purpose and turnover policy affects the choice of the type of parking. • Research on the Tunisian drivers’ behavior. Cruising for a parking space represents one of the major sources of urban congestion. Search for Parking generates additional urban traffic, energy consumption, vehicle emissions and time waste. Thus, it is essential to understand how drivers make decisions to select their parking lots. The estimation of models that reproduce parking driver's attitudes is crucial to improve the performance of parking facilities and transport systems. This study aims to understand the different behavioral considerations that rule the choice of drivers to choose one parking type among three alternatives: on-street parking, off-street parking and underground parking. For this purpose, we propose a multinomial logit model by using data obtained with a revealed preference surveys of drivers' parking type choices and associated factors in the city. Our findings provide authorities with additional perspectives for creating an efficient parking policy and associated land use planning. Indeed, to tackle the parking issue we had to pay attention to driver’s preferences regarding future parking supply options.

Pro-environmental Attitude and Adherence to a Travel Mode in an Integrated Choice and Latent Variable (ICLV) Model: Results from a Revealed Preference Survey

Pro-environmental Attitude and Adherence to a Travel Mode in an Integrated Choice and Latent Variable (ICLV) Model: Results from a Revealed Preference Survey

Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets

China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers including income. Furthermore, we estimate the potential for rebound effect and the effectiveness of a feebate policy. To accomplish this, we developed a joint discrete-continuous model of car choice and usage based on revealed preference survey data from approximately 8000 new car buyers from India and China who purchased cars in 2016–17. Conditional on buying a new car, the fuel consumption in both markets is found to be relatively unresponsive to fuel price and income, with magnitudes of elasticity estimates ranging from 0.12 to 0.15. For both markets, the mean segment-level direct elasticities of fuel consumption relative to car price and fuel economy range from 0.57 to 0.65. The rebound effect on fuel savings due to cost-free fuel economy improvement is found to be 17.1% for India and 18.8% for China. A revenue-neutral feebate policy, with average rebates and fees of up to around 15% of the retail price, resulted in fuel savings of around 0.7% for both markets. While the feebate policy's rebound effect is low – 7.3% for India and 1.6% for China– it does not appear to be an effective fuel conservation policy.