Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou,China

The social and economic growth as result of promoting the rapid development of tourism in China has brought tremendous pressure on the urban transportation systems. Research of travel behavior concerning the characteristics of tourists has provided effective information for transportation planning. Due to different city plans, public transportation system design, car parking design and management, etc., the local situation in developed countries differs from the counterpart in China. However, little research has studied the factors influencing the choice of travel destinations in tourism. The research aims to study the tourism destination and mode choice behavior of tourists and provides suggestions to improve tourism transportation service system. An online questionnaire survey is used to collect data including the travel characteristics and personal attributes of local tourists in different holidays in Hangzhou, China. A multinomial logit model is constructed with the trip destination set as the dependent variable. Results show that age, residential type, car ownership, companion type and holiday length have a significant impact on destination choice. To determine what influences modal choice for such trips, a second logit model is established with travel mode set as the dependent variable with the explanatory variables of age, gender, companion type, car ownership, holiday length and travel destination found to be significant. The results demonstrated that people aged 26 to 44 prefer suburban areas, and they are the main group driving to their travel destination. Public transport use frequency decreases when the destination is located outside of the main tourist area. Finally, suggestions have been proposed to mitigate the congestion and parking problem based on model analysis from the perspective of the bus line setting, transfer improvements, and the policy to limit cars, respectively.     

Travel using managed lanes: An application of a stated choice model for Houston,Texas

Managed lane (ML) travel adds flexibility, but also complexity, to travel choices. Stated choice models (SCMs) are often used for modeling complex transportation choices such as these in an effort to predict demand for these travel options. The design methods for SCMs have evolved from simple orthogonal designs to more sophisticated designs such as D-efficient design that can increase efficiency in estimation. We used three different survey design strategies to produce the stated preference portion of surveys, which were used to elicit travel choices for a sample of Houston travelers. Apart from the D-efficient design we also used random and adaptive random designs to generate attribute levels. There were observable differences in choice behavior depending on what design strategy was used. These differences appear to influence estimates of the value of travel time savings (VTTS) obtained from the random parameter logit (RPL) models estimated using these data. This, in turn, would greatly impact the percentage of travelers predicted to use the MLs.The adaptive random strategy was superior to the other design methods in several categories, and it had similar efficiency to the D-efficient design. However, the mean of VTTS estimate obtained from a D-efficient design was closer to what is typically found in the literature. The difference was considerable and could greatly influence traffic and revenue estimates for the MLs, illustrating the importance of the survey design strategy.     

Latent class models of international air carrier choice

This paper uses a market segmentation approach to identify airline passengers’ potential segments and preferences toward international air carriers. The modeling approaches consist of the stated preference method and the latent class model. The stated choice experiments were designed based on service attributes in the international airline choice context. Empirical data was collected from airline passengers who have flown from Taipei to Tokyo or from Taipei to Hong Kong. The latent class model accommodating preference heterogeneity outperforms the multinomial logit model as indication of a better approach to analyze airline choice behavior. The latent class model with individual socioeconomic and trip characteristics in segment membership functions also improved model fit relative to the corresponding latent class model without individual characteristics. The values of willingness to pay for service attribute improvements vary across international air routes and segments. Passengers are willing to pay more for better service quality. In order to develop effective marketing and operational strategies for the international air travel market, this study highlights the importance of exploring airline choice behaviors by routes and segments.     

Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey

This study examines transit mode choice preferences of New Jersey commuters traveling to New York City (NYC) with the objective of assisting trans-Hudson ferry service planning on the basis of variables significantly affecting mode choice. A stated preference survey was conducted to collect data from a total of 2134 respondents who commute from northern New Jersey to NYC. A mixed multinomial logit model (MMLM) with random coefficients was used to analyze the data. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey to NYC. Essential model results are presented for all four modes, but detailed results are discussed for only the ferry, PATH, and bus modes. Model results indicate that commuters' choices are affected not only by travel time and cost for the segment of the journey that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes required by the journey, and travel time and cost of trips from home to stations/terminals before crossing the river. The results also show that commuters' attachment to the modes they currently use serves as a barrier to switching to other modes. The most important takeaway from this research for transit service planning is that there is a need to consider the attributes of both individual trip segments as well as entire journeys between commuters' home and work when exploring changes to transit service characteristics.     

Assessing intercity multimodal choice behavior in a Touristy City: A factor analysis

This paper investigates influential factors in passengers' intercity multimodal choice behaviors in a touristy city. By collecting large individual-level data through a comprehensive field survey that was carried out at the major transportation hubs in Xi'an, China, we studied four travel modes of the surveyed travelers in this touristy city, including air, high-speed rail, traditional passenger train, and express bus. For factor analysis, 12 variables, including not only individual-related attributes but also ticketing methods and mental perceptions, were used as the independent factors after the correlation analysis and collinearity test. The regression relationships between the travel mode choice and the independent variables were studied using Bayesian multinomial logistic regression. The results indicate that those 12 factors have significant and various influences on passengers' mode choices. In particular, travel distance, fare rate, intercity travel time per hundred kilometers, quality of service, accessibility of transportation hubs, and ticketing methods have influential contributions for explaining the choice decision-makings. The findings demystify the effects of several unexplored factors in intercity multimodal travel choice behaviors and shed new light on formulating traffic management strategies for service providers and decision-makers in practice.     

Mitigating the impact of errors in travel time reporting on mode choice modelling

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper focuses on the mitigation of their impact on choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities. Employing these multiple – sometimes incongruent – sources of information in the choice model appears to be beneficial. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impacts on the generated policy scenarios. The model results also contributed to identifying the main travel activity features that affect travel time reporting, providing indications that can assist in understanding and mitigating the impact of imprecise measurements.     

A latent class choice based model system for railway optimal pricing and seat allocation

In this paper, discrete choice methods in the form of multinomial logit and latent class models are proposed to explain ticket purchase timing of passenger railway. The choice model and demand functions are incorporated into a revenue optimization problem which jointly considers pricing and seat allocation. The framework provides insightful policy implications in term of fare and capacity distribution derived from actual passenger behavior. It shows that accepting short-haul demand provides greater revenue than long-haul demand using the same capacity. Revenue improvement ranges from 16.24% to 24.96% in multinomial logit models and from 13.82% to 21.39% in latent class models respectively.     

Analysis of Iranian passengers' behavior in choosing type of carrier in international air travel to East Asia

Increased demand for East Asia flights from Tehran has intensified competition between Iranian and foreign carriers. In addition, the entrance of low-cost carriers (LCC) will make this competitive market even more intense. In this condition, carriers are striving to gain more market share by improving services and discounting their fares. Therefore, analyzing air travel demand is valuable for carriers' long term and short term planning. This study empirically investigates passengers' behavior in choosing five types of carriers in many groups of passengers using multinomial logit (MNL) and nested logit (NL) models. The data were collected through the stated preference (SP) questionnaire designed based on orthogonal main-effect. The main survey was conducted in the Imam Khomeini International (IKI) airport in July 2013 where 480 questionnaires were collected through face-to-face interviews. The results show that the ticket fare, the possibility of travel on desired date and time, international air travel experience, Frequent-flyer program (FFP) membership status, marital status, and gender are statistically significant contributors in explaining carrier choice. Interaction effects between trip purpose and ticket fare are statistically significant in choosing carriers. The results further indicate that business travelers are more willing to pay than non-business travelers for flights on desired date and time, although varying by type of carrier.     

Modeling the joint choice of access modes and flight routes with parallel structure and random heterogeneity

This study focuses on the influence of ground access transportation on air travel. The error components logit model with parallel structure is first adopted to formulate the joint choice behavior of access, airports, and flights. The proposed model proves to be effective in exploring interdependence between choice dimensions and travelers heterogeneity. The empirical results indicate that fare discounting should be introduced at the city airport and the frequency of existing flight routes should be increased. To benefit the hub airport, authorities of the new airport mass rapid transit system should reconsider the tradeoff between time and cost for express trains.     

Analysis of ground access modes choice King Khaled International Airport, Riyadh, Saudi Arabia

Airports are vital to the transportation system because air travel is the fastest means of transporting people and goods around the world. Airports also connect aviation systems with other modes of transportation.As a result, access modes to airports are critical to the aviation system, and studying air passenger decisions regarding mode choice is an essential part of airport management and system planning.Though there has been systematic analysis of car use to access airports, there has been less systematic attention to the use of the public transport system, especially the taxi service. The aim of this study is to identify King Khaled International Airport (KKIA) access mode characteristics and users, which has not been investigated previously concentrating on the mix between car and taxi using data collected specifically for this research.More than half (55.5%) of the travelers surveyed arrived at KKIA by private car; taxi and limousine passengers accounted for 42.2% of the sample. The Saudi Public Transit Company (SAPTCO), transported only 2.3% of the air passengers surveyed. These results are similar to findings for an airport in the United States and very different from findings for an airports in the East (e.g., Hong Kong).A binary logit model was developed to evaluate the access mode choice. It was found that income, luggage, travel access time, and nationality significantly affect mode choice.Results provide insight for improved planning of access to KKIA.     

Determinants and spatial variability of after-school travel by teenagers

This research work targeted teenagers to investigate after-school travel patterns and factors affecting their modal choice characteristics in Okinawa, Japan, where teenagers are not allowed to drive. The analysis is based on the cross-sectional data collected for discrete choice modeling of students’ travel in the prefecture. In this study, high school students’ trips from school to home have been analysed using a multinomial logit model. This has revealed the impact of individual, modal, and spatial variables on the mode choice decisions and return trip patterns. A comparative analysis made of downtown and suburb schools exposed differences in travel patterns induced by spatial factors and some existing constraints in modal choice options.     

Modeling air travelers’ choice of flight departure and return dates on long holiday weekends

Air travel demand is typically high on long holidays. Understanding factors that influence the choice of air travelers with respect to their departure and return dates on long holidays can help airlines make effective decisions on pricing, ticket sales, and scheduling. We conduct a stated preference survey to examine the preferences of low-cost airline travelers on a particular holiday weekend. A temporally correlated logit model is developed to account for the temporal correlation of day-of-the-week alternatives. The results indicate that airfare is the key variable affecting air travel date choices. The utility of day alternatives decreases when more leave days are required before the holiday begins. Departure dates before the beginning of the holiday weekend and return dates after the end of the holiday are highly substitutable. The low-fare strategy comprising early departures and late returns can effectively increase the load factor of off-peak flights on long holiday weekends.     

Influences of rural built environment on travel mode choice of rural residents: The case of rural Sichuan

China has entered a stage in which new rural construction and urbanization are rapidly developing. Considerable changes are occurring in rural China, and the built environment is different from that in the past; such difference directly influences the travel mode choice of rural residents. However, our knowledge on how the rural built environment influences the travel mode choice of rural residents in China remains limited. To fill this gap, this study combines on-site measurement methods, geographic information system (GIS) technology, and activity diary survey to obtain basic data regarding the built environment and the daily activities of rural residents. The multinomial logit (MNL) model is used to explore the relationship between the rural built environment and the travel mode choice of rural residents. Results show that building density significantly positively affects private car trips. This finding challenges earlier urban built environment research due to the considerable gap between rural and urban areas. An increase in road density increases the travel frequency of electric bicycles and motorcycles. Accessibility perception and preferences positively affect the probability of choosing to walk. Safety and neighborhood harmony perception positively affect the travel frequency of motorcycles and private cars. Rural residents who prefer a safe living environment are likely to choose walking for their daily travel. Despite the considerable achievements in the construction of rural roads, the frequency of public transportation remains low for rural residents. Therefore, additional attention should be given to the investment and construction of public transport facilities during rural urbanization.     

Determinants of travel mode choice for seeking healthcare: A comparison between elderly and non-elderly patients

People's travel mode choices can vary significantly by age group due to different influencing factors, but relevant research on health-related travel is scarce. In this study, we explored and compared the determinants of travel mode choice for healthcare-seeking non-elderly and elderly patients in Beijing, China. A multinomial logit model was used to analyze data from a recent healthcare-seeking behavior survey. The results show that increased age discourages car use and slightly prompts non-motorized travel in the non-elderly, but has the opposite effect in the elderly group. Household income has a negative effect on non-motorized travel in the non-elderly, but a positive effect in elderly patients. Highly mixed land use, intensive urban development, and difficulty in parking discourage non-elderly patients from traveling by car, but none of these factors have a significant effect on the elderly. Our findings shed light on the unique transportation demands for different age cohorts and inform the creation of age-specific interventions to improve overall access to health.     

Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior

This paper presents a modeling methodology capable of accounting for spatial correlation across choice alternatives in discrete choice modeling applications. Many location choice (e.g., residential location, workplace location, destination location) modeling contexts involve choice sets where alternatives are spatially correlated with one another due to unobserved factors. In the presence of such spatial correlation, traditional discrete choice modeling methods that are often based on the assumption of independence among choice alternatives are not appropriate. In this paper, a Generalized Spatially Correlated Logit (GSCL) model that allows one to represent the degree of spatial correlation as a function of a multi-dimensional vector of attributes characterizing each pair of location choice alternatives is formulated and presented. The formulation of the GSCL model allows one to accommodate alternative correlation mechanisms rather than pre-imposing restrictive correlation assumptions on the location choice alternatives. The model is applied to the analysis of residential location choice behavior using a sample of households drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) data set. Model estimation results obtained from the GSCL are compared against those obtained using the standard multinomial logit (MNL) model and the spatially correlated logit (SCL) model where only correlations across neighboring (or adjacent) alternatives are accommodated. Model findings suggest that there is significant spatial correlation across alternatives that do not share a common boundary, and that the GSCL offers the ability to more accurately capture spatial location choice behavior.     

A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route

This study is to analyze passengers' choice of the mode of transportation when air transportation is in competition with high speed rail (HSR). The Seoul-Jeju route analyzed as an empirical case study, in which the construction of an undersea tunnel to connect Seoul and Jeju city by HSR has been considered. The study also included two new variables, ‘safety of transportation’ and ‘availability of duty free shopping’ in addition to traditional transport choice variables such as travel time, travel costs and frequency of service to reflect special characteristics of the market. As data gathering tools, SP techniques and mixed logit model, for analytical methodology, were utilized. The authors found that the goodness of fit of the models was improved with new variables. The models also showed that the characteristics of business passengers and leisure passengers in choosing the mode of transportation were different. Business passengers were apt to choose a safety secured mode of transportation regardless of fare while leisure passengers preferred to use duty free shops more than business passengers.     

Children's school commuting in the Netherlands: Does it matter how urban form is incorporated in mode choice models?

Walking and cycling to school is environmentally sustainable and increases children's physical activity. Although it is known that the built environment influences children's travel behavior, there is scant knowledge of how urban form along children's commuting routes affects walking and cycling separately, or of how to incorporate urban form exposures in transport mode choice models. This research investigated (a) the associations between children's transport mode choice and urban form correlates en route, and (b) the consequences of different urban form operationalizations (i.e., individual variables, composite measure, and principal components [PCs]). Global Positioning System devices were used to track 623 trips to and from school made by Dutch children aged 6–11 years. Urban form exposures were derived with geographic information systems, and their relationships with mode choice were tested with mixed multinomial logit models in a cross-sectional research design. Differences between the number of associated urban form variables, their magnitudes, and their significance levels were found for both walking and cycling, independent of the operationalization. Urban form was most influential for walking, whereas distance-related effects were absorbed when modeled as PCs. The highest model fit was achieved through PCs; the composite measure resulted in the lowest fit. To maximize the effectiveness of planning and health interventions, walking and cycling must be targeted separately. Policymakers should avoid “one-fits-all policies,” which are deemed to be inefficient. Because urban form affects model interpretation and fit, careful attention should be paid to how urban form is modeled, and sensitivity analyses should be performed.     

Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system

In recent years, there has been increasing attention on bicycle-sharing systems (BSS) as a viable and sustainable mode of transportation for short trips. However, due to the relatively recent adoption of BSS, there is very little research exploring how people consider these systems within existing transportation options. Given recent BSS growth around the world, there is substantial interest in identifying contributing factors that encourage individuals to use these systems. The current study contributes to this growing literature by examining BSS behavior at the trip level to analyze bicyclists’ destination preferences. Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL). The quantitative frameworks developed have been estimated using 2013 data from the Chicago’s Divvy system. In our modeling effort, we distinguish between BSS users with annual membership and short-term customers with daily passes. The developed model should allow bicycle-sharing system operators to plan services more effectively by examining the impact of travel distance, land use, built environment, and access to public transportation infrastructure on users’ destination preferences. Using the estimated model, we generated utility profiles as a function of distance and various other attributes, allowing us to represent visually the trade-offs that individuals make in the decision process. To illustrate further the applicability of the proposed framework for planning purposes, destination station-choice probability prediction is undertaken.     

Latent class nested logit model for analyzing high-speed rail access mode choice

This paper explores access mode choice behavior, using a survey data collected in Taiwan. The latent class nested logit model is used to capture flexible substitution patterns among alternatives and preference heterogeneity across individuals while simultaneously identifying the number, sizes, and characteristics of market segments. The results indicate that a four-segment latent class nested logit model with individual characteristics in segment membership functions is the most preferred specification. Most high-speed rail travelers were cost-sensitive to access modes, and thus strategies that reduce the access costs can be more effective than reducing the access times.     

What drives sustainable student travel? Mode choice determinants in the Greater Toronto Area

Examination of mode choice behavior is an important step in accurately predicting future travel demand. Despite having somewhat unique travel needs and challenges, there is a lack of knowledge in understanding the mode use behavior of university student population. The existing studies on university populations relied on a relatively smaller sample in investigating the behavior. Therefore, using world's largest university student's travel database, this study examines the factors affecting the mode choice behavior of a diverse university student population with student samples from four universities and their seven campuses located across the Greater Toronto Area (GTA) in Canada. Additionally, stratifying this diverse population using their attitudinal responses towards numerous travel modes, this study also estimates three additional mode choice models to obtain a more comprehensive understanding of how students in different markets, with different latent attitudes towards transportation, vary in terms of sustainable mode choice. A cluster analysis based on fourteen attitudinal responses, was conducted to stratify the sample whereas the popular multinomial logit approach was used to estimate the mode choice models. This study finds transit pass and bike ownership as important determinants that govern sustainable mode choice among the students in the region. The findings of this study could facilitate the sustainability offices at the four universities in making an informed policy decision in shifting the mode use behavior of students towards sustainable modes.