Destination Choice Behavior Research Articles

Improved Trip Distribution Models Using the Empirical Bayes Method

The destination choice model considers each individual as a fundamental analytical unit, thereby providing increased flexibility in accommodating a wide range of factors that influence destination choice behaviors compared to aggregate trip distribution models. However, in practical applications, practitioners have found that there are still influencing factors that cannot be represented in the model, resulting in a huge deviation between the predicted trip distribution and the actual trip distribution. To enhance the predictive accuracy of the destination choice model, this paper proposes a new computational method based on a combination of the traditional destination choice model and the empirical Bayes (EB) method. It can integrate additional information from trip frequencies in the survey data with the information from various influencing factors in the traditional destination choice model. The proposed EB model is developed for commuting trips based on 2019 Shanghai Household Travel Survey data, and the model is validated using the survey data and mobile signaling data. For comparisons, both the traditional destination choice model and the proposed EB method are applied to distribute trips. The analytical results indicate that the EB method outperforms the traditional model in all the goodness-of-fit indicators, and the prediction errors are reduced in all the validation procedures. It is therefore expected that the proposed EB method based on Bayesian statistics may improve the predictive capability of the destination choice model for trip distribution.

Deep Learning Model for Short-Term Origin–Destination Distribution Prediction in Urban Rail Transit Network Considering Destination Choice Behavior

Urban rail transit (URT) has emerged as a crucial mode of transportation in metropolitan areas. For the effective operation of expanding URT networks, accurate short-term origin–destination (OD) demand distribution predictions are essential. This study introduces a novel deep-learning-based model for predicting short-term OD distribution in extensive networks, taking destination choice behaviors into account. First, we perform a comprehensive analysis of station passenger flows and OD flows from both temporal and spatial dimensions. Then, we develop the origin–destination distribution prediction (ODDP) model, combining the destination choice model (DCM) with the deep learning model (DLM). The DCM aims to understand OD distribution patterns from a behavioral perspective by transforming real-time inflows into OD distributions. Meanwhile, the DLM, employing attention and convolution layers, effectively captures the intricate temporal and spatial dynamics of passenger flows. Our model is evaluated using data from the Guangzhou Metro network in China, showing significant enhancements in prediction accuracy, model interpretability, and overall robustness. The implementation of our model promises substantial benefits for the operational efficiency of URT systems.

A Study on the Formation and Distribution Mechanisms of the Demand for Shared Electric Vehicles

With the decarbonization of the transportation sector and the diversification of travel demand, the development of shared electric vehicles has become crucial. Based on survey data of travel mode and destination of shared electric vehicles in Beijing, this paper aims to explore the formation and distribution mechanisms of the demand for shared electric vehicles. First of all, a multi-index and multi-cause (MIMIC) model was established to quantify the psychological latent variables that cannot be directly observed and to analyze the mechanisms between individual socio-demographic attributes and latent variables. Secondly, these psychological latent variables were added to mixed logit (ML) models as explanatory variables to form hybrid choice models to explore the travel mode choice behavior and travel destination choice behavior, respectively, when using shared electric vehicles for leisure travel. The results show that potential users of shared electric vehicles are characterized by higher education, employees of enterprises, no car availability and high driving years, and most of them travel for the purpose of connecting to transport hubs. Latent variables such as individual carbon trading, subjective norms, risks and behavioral intentions all affect the demand for shared electric vehicles; in-car time, out-of-car time, travel cost and the number of subway stations have negative impacts on the demand, while mall properties and the number of parking lots have positive impacts on the demand. Furthermore, the use of shared electric vehicles is highly correlated with the use of cars and subways, and part of the travel demand could be transferred to shared electric vehicles by taking certain measures.

Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

In this study, we examine the factors affecting Chicago, U.S., transportation network companies (TNCs) users’ trip fare and destination choice behavior. While trip fare has been examined from various perspectives, earlier fare models have not considered an exhaustive set of independent variables. Further, trip fare decisions are significantly influenced by trip destination. Therefore, in our study a joint model of trip fare and destination choice is proposed. The joint model system—linear regression for fare and multinomial logit model for destination—is developed based on Chicago TNC weekday trip data from January 2019 to December 2019. A wide range of origin- and destination-specific land use and built environment factors, transportation infrastructure attributes, and weather attributes were found to be significant in the model system. Based on log-likelihood and Bayesian information criterion measures, the model performance of the proposed joint model is found to be superior compared with independent fare and destination models. The applicability of our proposed fare and destination choice model is illustrated through fare prediction and destination elasticity analysis. The framework can potentially be employed to generate TNC fare for inclusion in level of service measures for TNC models in the mode choice model.

An exploration of heterogeneity in Latent Psychological Variables on Travelers’ destination choice

Discrete choice models have been widely used to model individuals’ decision-making processes in numerous contexts. Given that people’s tastes and preferences over alternative shopping destinations vary for reasons not necessarily observed by demographic characteristics, more intricate modeling techniques are required to capture taste variations. Latent factors, such as the psychological impact on the destination choice process, can explain heterogeneity among individuals. This study considers the latent class difference in shopping destination choices by using empirical data collected from 812 individuals for two types of grocery and clothing in Tehran, Iran. The heterogeneity in destination choice behavior is captured by a Mixed Logit (ML) model and a Latent Class Variable (LCV) model formulation. A simple multinomial logit model has also been developed to benchmark the two techniques. Our findings suggest that both the ML and the LCV can capture taste variation in travelers’ behavior, where the ML expectedly provides a better fit to the data.

Capturing positive network attributes during the estimation of recursive logit models: A prism-based approach

Although the recursive logit (RL) model has been recently popular and has led to many applications and extensions, an important numerical issue with respect to the computation of value functions remains unsolved. This issue is particularly significant for model estimation, during which the parameters are updated every iteration and may violate the feasibility condition of the value function. To solve this numerical issue of the value function in the model estimation, this study performs an extensive analysis of a prism-constrained RL (Prism-RL) model proposed by Oyama and Hato (2019), which has a path set constrained by the prism defined based upon a state-extended network representation. The numerical experiments have shown two important properties of the Prism-RL model for parameter estimation. First, the prism-based approach enables estimation regardless of the initial and true parameter values, even in cases where the original RL model cannot be estimated due to the numerical problem. We also successfully captured a positive effect of the presence of street green on pedestrian route choice in a real application. Second, the Prism-RL model achieved better fit and prediction performance than the RL model, by implicitly restricting paths with large detour or many loops. Defining the prism-based path set in a data-oriented manner, we demonstrated the possibility of the Prism-RL model describing more realistic route choice behavior. The capture of positive network attributes while retaining the diversity of path alternatives is important in many applications such as pedestrian route choice and sequential destination choice behavior, and thus the prism-based approach significantly extends the practical applicability of the RL model.

A virtual experiment on pedestrian destination choice: the role of schedules, the environment and behavioural categories.

Which locations pedestrians decide to visit and in what order drives circulation patterns in pedestrian infrastructure. Destination choice is understood to arise from individuals trading off different factors, such as the proximity and busyness of destinations. Here, a virtual experiment is used to investigate whether this behaviour depends on the layout of buildings, whether planned or imposed destination schedules influence decisions and whether it is possible to distinguish different choice behaviour strategies in pedestrian populations. Findings suggest that virtual experiments can consistently elicit a range of destination choice behaviours indicating the flexibility of this experimental paradigm. The experimental approach facilitates changing the environment layout while controlling for other factors and illustrates this in itself can be important in determining destination choice. Destination schedules are found to be relevant both when imposed or generated by individuals, but adherence to them varies across individuals and depends on prevailing environmental conditions, such as destination busyness. Different destination choice behaviour strategies can be identified, but their properties are sensitive to the detection methods used, and it is suggested such behaviour classification should be informed by specific use-cases. It is suggested that these contributions present useful starting points for future research into pedestrian destination choice.

Do first impressions matter? Effect of first impressions on tourists’ destination choice: an event-related potential study

ABSTRACT Traditional methods rarely give a fully understanding of tourists’ first impression of destination images and how it affects their subsequent destination choice behaviour. In addition, the first impression response and subsequent destination choice behaviour of tourists by tourist images have not yet been investigated electrophysiologically. Hence event-related potentials (ERPs) were applied to explore the study of the brain activity induced by tourists’ first impression and destination choice behaviour when viewing images of different types of tourist destinations. The results found that (1) Tourists’ first impression formation is associated with N1, N2 and P2 components produced by brain activity. (2) There are significant differences in the neural mechanism of tourists’ first impressions of the aesthetic emotional response to both images of the ‘natural’ and ‘built’ environment and images that they ‘like’ and ‘dislike’. (3) P2 and P3 components are associated with tourists’ destination choice, with destination ‘choice’ photos having a higher amplitude than destination ‘reject’ photos in the P3 component. (4) Good first impressions are positively correlated with destination choice behaviour. This study explores the electrophysiological activity of destination image perception and choice behaviour, and the results can act as an evaluating indicator of destination marketing.

Forecasting Passenger Flow Distribution on Holidays for Urban Rail Transit Based on Destination Choice Behavior Analysis

Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers’ destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers’ travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes.

Studying the Role of Behavioral Characteristics in Individuals Travel Choice Behavior in Response to Earthquake Disaster Using Discrete Choice Models

The study of decision-making in response to earthquake disaster including evacuation and destination choice behavior is necessary for emergency planners in order to provide emergency traffic management and solve evacuation problems in transportation network. In this research, it is attempted to study the pattern of choice behavior in Qazvin transport network by considering physical and behavioral factors simultaneously. The required data were collected through a questionnaire from 546 inhabitants of Qazvin based on the stated preferences (SP) survey for six designated earthquake scenarios at different severities and times. The effects of socioeconomic factors, trip daily characteristics, and behavioral factors on decisions to evacuate and choose destinations are studied in this research. The binary logit model is applied for analyzing the evacuation decision, and the multinomial logit model is applied for the destination choice in response to the earthquake disaster. The binary logit model analysis with 73% correct prediction indicates that socioeconomic characteristics including age, marital status, family size, and car ownership have positive effects on evacuation choice. Furthermore, the analysis of behavioral factors indicates that moderate religious belief decreases the likelihood of evacuation, while following previous experiences, trust in acquired trainings, and following decisions made by others moderately increase the likelihood of evacuation. Also, socioeconomic and behavioral factors are the determinants of the destination decision, and the results indicate that individuals following previous experiences highly are more likely to choose gas station and relative’s home destination versus going to urban open spaces.

Effect of Terrorism on the Performance of Tourism Industry. A Critical Literature Review

Purpose: Global Tourism destinations provide different products which satisfy tourists with different interests, needs and wants. The general objective of the study was to examine effect of terrorism on the performance of tourism industry.
 Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps.
 Findings: The study concludes relevant governments should face the fact that terrorist attacks provoke a substitution effect on destination choice behaviors. Tourists will replace destinations considered unsafe due to terrorist threats with others considered safer. The physical distance of the tourists' home countries and their cultural and socio-economic traits influence this replacement behavior. Given the randomness of terrorist attacks, relevant governments should be prepared to alter quickly their marketing strategy, namely their market targeting strategies and promotion campaigns to prevent substitution effect and lack low returns from the tourism industry
 Recommendations: The study recommends that relevant governments in dealing with the root causes of terrorism should also denounce the terrorists considering their acts as criminal acts just like others. The tourism stakeholders should increase efficiency in law enforcement, prosecution of terrorist suspects, by improving intelligence gathering and the police should be effectively trained to deal with terrorism incidences. The covert nature of terrorism often precludes an easy analysis of their capabilities or intent, therefore, giving them an advantage of lying in wait and striking when least expected; thus, the government counterterrorism should be proactive

A seemingly unrelated regression model of the impact of COVID-19 risk perception on urban leisure place choices

Due to the prolonged COVID-19 pandemic and various restrictions, peoples’ leisure activity patterns significantly change. Thus, it is necessary to understand how people’s travel and leisure behaviors have changed during the COVID-19 pandemic. However, there is still a lack of empirical evidence on how individuals’ COVID-19 risk perception influences their leisure destination choice behavior. This empirical study aims to confirm the relationship between risk perception of COVID-19 and choice of leisure destination and to explore any differences between them related to demographic characteristics. A total of 537 valid samples were used for SUR model analysis by conducting an online survey targeting citizens of the Seoul metropolitan area, Korea. Our findings show that the risk perception of COVID-19 has a significant effect on the choice of leisure places. In particular, the risk perception of COVID-19 has a positive effect on the choice of natural places, disinfected areas, and socially distanced spaces while negatively influencing the choice of crowded leisure places. In addition, age and gender are more effective factors than other control variables in COVID-19 risk perception and leisure destination choices. Furthermore, this study also provides several implications for urban leisure place planners and service providers to respond to the changing leisure activity patterns caused by COVID-19.

Evacuation destination choice behavior of households in Eastern Samar, Philippines during the 2013 Typhoon Haiyan

Understanding evacuee behavior is important to minimize the loss of lives and manage evacuation in the future. Evacuation destination choice entails the decision of affected people regarding where to stay, away from an impending hazard. This study investigates the factors considered by households when choosing where to go during an evacuation period. Data was gathered from households in one of the most affected barangays in Borongan City, Eastern Samar during Typhoon Haiyan. A binary logit was used to determine the significance of variables in evacuation destination choice. Destination categories are public shelter/evacuation center and family/friends’ house. Results show that gender, income, marital status, and the number of household members that evacuated are significant factors in the choice of destination. The results from the study give relevant insights for government agencies and research institutions towards understanding destination behavior. Consideration of simultaneously modeling evacuation destination with evacuation decision is an insight taken from this study.

A framework for estimating bikeshare origin destination flows using a multiple discrete continuous system

Given the burgeoning growth in bikeshare system installations and their growing adoption for trip making, it is important to develop modeling frameworks to understand bikeshare demand flows in the system. The current study examines two choice dimensions for capturing the system level bikeshare system demand: (1) total station level demand and (2) distribution of bike flows from an origin station across the network. A linear mixed model is used to estimate the first choice and Multiple Discrete Continuous Extreme Value (MDCEV) model is used to analyze the latter. The data is drawn from the New York City bikeshare system (CitiBike) for six months (January through June 2017). For our analysis, we examine demand and distribution patterns on a weekly basis controlling for a host of independent variables (trip, socio-demographics, bicycle infrastructure, land use and built environment, temporal and weather). Model validation exercise results revealed that the proposed model performs well for low demand destinations. A policy exercise evaluating destination choice behavior demonstrated how the impact of distance is compensated by additional bicycling infrastructure in the farther locations. The results from the study help bikesharing system planners and operators to better evaluate and improve bikeshare systems.

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.

Modeling Destination Choice Behavior of the Dockless Bike Sharing Service Users

This study investigates trip-level destination choice behavior of users of the dockless bike sharing service (DBS). A random parameter latent segmentation-based logit (RPLSL) model is developed utilizing the DBS users’ trip itinerary data for Kelowna, Canada. The RPLSL model captures multi-dimensional heterogeneity such as inter-segment and intra-segment heterogeneity. The model is developed at a micro-spatial resolution which is defined as the bicycle analysis zone. One of the key features of this study is to test the interdependencies between the origin and destination of a trip using their built environment attributes. Model results suggest that segment 1 is more likely to include trips originating from the urban neighborhoods; whereas, segment 2 includes trips originating from the suburban neighborhoods. The parameter estimation results reveal that DBS trips are more likely to be destined to locations with longer length of cycle tracks, higher employment density, and that are closer to the Central Business District and bus stops (i.e., within 500 m). The model confirms multi-layer heterogeneity. For instance, trips originating from the urban areas in segment 1 are more likely to be destined to destinations within 500 m of the designated bike return sites (i.e., havens). In contrast, shorter trips originating in suburban areas in segment 2 show a negative relationship. Interestingly, a bike-friendly environment might increase the attractiveness of destinations closer to havens, even for the trips originating in suburban areas. The findings of this study will assist in developing policies and infrastructure investment decision making at the destination locations to promote DBS usage.

Exploring the Effect of Sequentially Receiving Airline and Destination Information on the Choice Behavior of Tourism Destinations

This article reports the results of a holiday destination choice model of domestic travelers in Australia. Although destination choices have been studied before, travelers’ behavior when choosing an airline ticket is less well investigated, in particular the effect of the choice of airline ticket and tourism features on each other and on the final destination choice. Multinomial logit (MNL) models were estimated using data from a Stated Preference (SP) choice experiment based on a D-Efficient design. Following the leader-driven primacy phenomenon, the article also tests whether destination choices are influenced by sequentially receiving information about airline tickets and tourism features. Results show that when airline ticket information is presented first, the destination choice behavior could be affected. In this context, the information sequencing effect is clear. However, the influence of tourism features is not as clear on the final choice when travelers are first exposed to tourism features and then airline tickets.

Holiday travel behavior analysis and empirical study with Integrated Travel Reservation Information usage

Abstract The efficient travel demand management is more important than traffic congestion control. Currently, tour reservation system (TRS) based on demand response has become a significant part of travel demand management. The Integrated Travel Reservation Information (ITRI) released by TRS plays an important role in the decision-making process of tourist’s holiday travel destination and departure time choice behaviors, which is seldom investigated. To fill this gap, this paper explored the feedback mechanism among the ITRI usage, tourism utility of various alternatives and tourist decision-making behavior based on the Engel-Kollat-Blackwell (EKB) consumer purchase decision-making model. To quantify the impact of ITRI on travel behavior, a revealed preference and stated preference survey were designed and carried out in China. The data show about 80% of tourists are concerned about the information of tickets sold ratio. Nested Logit (NL) model for the joint choice of destination and departure time was established, which accounted for the impact of TRS and ITRI on the utility of alternatives. The NL model results reveal that tourist’s age, education level, number of visits, ITRI content, ITRI query method and the tickets sold ratio have significant effects on destination and departure time choice. Especially, sensitivity analysis results reveal that when the tickets sold ratio reaches 65%, 75%, 85% and 95% respectively, every 1% growth of tickets sold ratio is accompanied by 2.858%, 2.877%, 3.015% and 3.362% increase in the probabilities of traveling during non-holiday periods. Therefore, tourists are encouraged to travel during non-holiday periods by tickets sold information release, which can achieve the rational allocation of tourism resources and enable to make maximum use of tourism resources. Further, the results can provide a basic data for development of the tourism demand management and equilibrium of the tourist’s spatial and temporal distribution.

Consideration of Both Latent Variables and Taste Variation in Modeling Destination Choice for Student’s Non-mandatory Activities

Extensive studies have been conducted on urban travel behavior, but rarely have addressed the travel behavior of special trip generators such as dormitory students. One of the most important student travel activities is non-mandatory (shopping or recreation) ones. The purpose of this study is to investigate the influence of the factors (both tangible and intangible) on the destination choice of such activities for future policy-making in line with appropriate distribution of demand in the city. Therefore, a questionnaire was designed and distributed among the students residing in Imam Khomeini International University dormitories. The results showed that taking individual perception in addition to their taste variation into account would enhance the ρ2(0) index of the mixed hybrid logit model by 0.175 than the standard logit model (with no latent variable and no consideration of taste variations). Furthermore, it was found that increasing the distance to a zone and the absence of direct accessibility to a particular zone via public transportation (buses or taxies) reduced the utility of traveling to that zone. Moreover, the presence of the central business district (CBD) of city in a particular zone and increasing the individuals’ perceived attractiveness of a zone (as the latent variable) increased the utility of traveling to that zone. The results of the mean marginal effect of “Attractiveness” variable show that the probability of five investigated zones increases by 12.2–49.3%. Therefore, this study concludes that both tangible and intangible factors are needed to better understand students' destination choice behavior for using in future policy-making.

A two-stage recreational destination choice study incorporating fuzzy logic in discrete choice modelling

A major challenge in developing discrete choice models when the feasible choice set is extensively large is to find an appropriate choice set that is computationally manageable for being integrated into the choice modelling specification while it also reflects the behaviour of the decision-maker. This study aims to develop a new sampling method for choice set formation which is then used to improve the knowledge about recreational destination choice behaviour. The study proposes a two-stage framework where in the first stage a rule-based fuzzy logic model is used to form a latent choice set. The second stage incorporates the set in a discrete choice model to estimate destination selection behaviour. In the travel demand modelling literature, there has been little research into modelling recreational activity destination choices. In this area, the primary focus has been on other activities, such as work, study and shopping. In this context, the sampling methods used to form the choice set using the large set of possible options in the universal set are unconvincing. The proposed two-stage approach is examined using a household travel survey from Victoria, Australia, where nine different recreational destination choice models are developed. Compared to the conventional modelling framework, these models show impressive model fit and prediction potential.