Itinerary Choice Research Articles

Bus user itinerary choice: Can crowding information help shift riders?

Bus user itinerary choice: Can crowding information help shift riders?

The Importance of Direct Routes for Air Travel Itinerary Choices: Results from a Stated Preference Choice Experiment

The Importance of Direct Routes for Air Travel Itinerary Choices: Results from a Stated Preference Choice Experiment

An effective approach for bi-objective multi-period touristic itinerary planning

Planning effective itineraries for tourists is a major problem that has been gaining attention over the last years. This paper proposes a new bi-objective integer linear programming model for this problem. Decisions include the choice of the best itinerary to be performed considering multi-period routing, time windows for the visited attractions and the choice of restaurants and hotels. The conflicting objectives considered are: (i) maximizing the level of service offered by the itinerary, and (ii) minimizing the total distance traveled. The problem resolution, even for small instances by exact methods, is limited. This motivated the proposition of a new customized MIP-heuristic based on decomposition, fix-and-optimize and MIP-start, to produce good-quality solutions with moderate computational effort. Tchebycheff’s scalarization method was coupled to this heuristic and multiple compromise solutions were obtained. Extensive results with problem instances of different sizes and characteristics showed a good performance of this approach, capable of producing effective solutions within short runtimes. The analysis of the solutions indicated a strong conflict between the objectives, allowing the user to quantify the losses and gains when one criterion is prioritized over the other. A brief sensitivity analysis of some model parameters revealed interesting managerial insights. Some examples include quantifying the negative impacts in terms of the level of service offered by concentrating hotels and restaurants in the center of tourist attractions, increasing visit and transfer times between attractions and reducing the planning horizon for the entire itinerary. These aspects validate the potential of using this MIP model and applying this MIP-heuristic in real situations.

Interpolating Urban Tourists’ Itineraries Based on Low-Fidelity Positioning Data and Domain Knowledge

This paper proposed and tested an interpolation method that took advantage of low-fidelity passive positioning data, such as mobile phone (MP) data, to estimate urban tourist visits to tourism sites, which utilized the domain knowledge of regularities in tourist behavior and preferences. The central component of the approach was a model of tourists choosing itineraries. A dataset of 80 Shanghai (China) tourist trajectories was collected through simultaneous MP and global positioning system (GPS) tracking. The results showed that the itinerary choice models that used both types of tracking data were quite similar and behaviorally reasonable. The most important finding was that visits that were estimated with the MP data and model were as accurate as those that were estimated via the GPS data and model.

On Effects of Personality Traits on Travelers’ Heterogeneous Preferences: Insights from a Case Study in Urumqi, China

Personality is a psychological concept which reflects people’s characteristic patterns of thoughts, feelings, and behavior. In this sense, it is straightforward to state that people’s behavior is fundamentally affected by their personalities. However, the concept of personality is rarely considered in most travel behavior studies. Given this fact, this paper aims to investigate the effect of personality traits on travelers’ heterogeneous preferences in the context of air itinerary choice. To this end, travelers’ stated choices for air itinerary and information reflecting travelers’ personality traits were collected. After defining specific personality traits based on the collected data, a hybrid choice model with interaction effects between travelers’ personality traits/socio-demographic characteristics and alternative-specific attributes were established and estimated and next the contributions of personality traits and socio-demographic characteristics to travelers’ choice behavior were compared and analyzed. The results confirmed the effects of personality traits on travelers’ heterogeneous preferences. However, the results also revealed that the magnitudes of such effects are not as great as the effects of socio-demographic characteristics.

Public Transit Itinerary Choice Analysis Considering Various Incentives

Previous research on public transport user preferences has primarily focused on mode choice, transfer penalties, and preferences related to service qualities. Individuals are generally found to not like waiting, transfers, or overcrowding. However, from an operation perspective, it could be important to convince users to change to a different itinerary to reduce overcrowding, which can cause problems such as bunching. This would most likely require convincing the user to voluntarily accept a longer wait or to take a different route potentially with a transfer. One means of doing so might be through incentives or offering a shared taxi. Incentivizing transit users to switch to a less-crowded itinerary, which could potentially be a way to manage bus overcrowding, is not well-studied. Through a discrete choice experiment, this study thus filled this research gap by testing people’s willingness to change routes or to take a shared taxi in exchange for one of three distinct types of incentives. A mixed binary logit model and a mixed nested logit model were used to investigate how different factors influence public transit user itinerary choice behavior. The results suggested that some people would be willing to change to the proposed alternative. Heterogeneity within the responses was seen as transit users were found to have distinct preferences for different incentives, and the value of the incentives influenced users differently. Our models and results could be helpful for public transit agencies to improve efficiency and reduce costs by better balancing the travel demand.

A Schedule-Based Model for Passenger-Oriented Train Planning With Operating Cost and Capacity Constraints

In the planning stage, train operators design timetables to serve passenger trips and a train circulation plan to support these timetables. These designs consider not only operating costs but also passenger convenience. In this study, we developed an optimization model for a new problem that focuses on timetabling and train-unit scheduling while also considering passenger itinerary choices in a schedule-based train system. This optimization model minimizes passenger travel costs within the constraints of a limited budget available for operating costs. The model is solved by an iterative heuristic that simulates the interaction between train operations and passenger itinerary choices. The heuristic solves the timetabling and train-unit scheduling problem using a decomposition approach to increase computational efficiency, while passenger loading is solved by a user-equilibrium passenger assignment model. An example based on the high-speed railway network in southern China was used to demonstrate the effectiveness of the proposed model and method.

Dynamic offer generation in airline revenue management

The New Distribution Capability and new retailing platforms will enable airlines to respond to shopping requests with bundled offers of flights and ancillary services, representing an evolution from traditional, flight-focused optimization. Assembling an attractive set of offers to display to customers therefore represents a new joint pricing and assortment optimization problem in airline revenue management. In this paper, we introduce an initial optimization approach for the selection and pricing of a la carte and bundled flight and ancillary offers. First, we propose a customer choice model that captures the impact of ancillary bundles on flight itinerary choice. We then calculate prices for each offer from a continuous range of price points and display the offer set that maximizes expected revenue for a given customer segment. We illustrate the approach using a single-flight, single-ancillary base case and discuss extensions to more complex environments. Tests in the Passenger Origin–Destination Simulator (PODS) show that dynamic offer generation (DOG) can increase net revenue when used by one or more airlines in a competitive network, assuming that the airlines are able to accurately segment incoming requests and estimate the average willingness-to-pay of each segment. We find that the majority of the revenue gains of DOG are due to competitive effects from the dynamic pricing of the flight component of the offer. The bundling mechanism of DOG is a secondary source of revenue gain that can be realized when customers take bundled ancillary services into account when choosing the flight. Our results provide insight for practitioners that are implementing offer optimization systems and processes. For example, in line with the previous literature on bundle pricing, we find that in transparent distribution channels an ancillary service should be bundled with the flight when the valuation for the ancillary is high or when its marginal cost of provision is low. We close by discussing the strategic and managerial implications of a move from traditional distribution strategies to a next-generation, offer-focused approach.

Empirical analysis of scaled mixed itinerary-size weibit model for itinerary choice in a schedule-based railway network

The mixed itinerary-size weibit (MISW) model was recently developed for predicting passengers’ itinerary-choice behaviors in a schedule-based railway network. It considers passengers’ heterogeneous perceptions and relaxes the independently and identically distributed assumptions of random utility models. However, this model has not been verified using real-world data. Moreover, it is assumed that passengers hold a negative perception of overlapping, but this assumption may not be suitable for all situations. Thus, this study proposes a scaled MISW model which includes a scale parameter to address this issue. We collected passenger ticket-booking data from the South China High-Speed Railway network and conducted an empirical analysis in which we compared the performances of the scaled MISW model and other models (i.e. the multinomial logit, multinomial weibit, and MISW models). According to the results, the scaled MISW model outperformed the other models in describing passengers’ choice behaviors in the railway network.

Price elasticity estimation for deep learning-based choice models: an application to air itinerary choices

One of the most popular approaches to model choices in the airline industry is the multinomial logit (MNL) model and its variations because it has key properties for businesses: acceptable accuracy and high interpretability. On the other hand, recent research has proven the interest of considering choice models based on deep neural networks as these provide better out-of-sample predictive power. However, these models typically lack direct business interpretability. One useful way to get insights for consumer behavior is by estimating and studying the price elasticity in different choice situations. In this research, we present a new methodology to estimate price elasticity from Deep Learning-based choice models. The approach leverages the automatic differentiation capabilities of deep learning libraries. We test our approach on data extracted from a global distribution system (GDS) on European market data. The results show clear differences in price elasticity between leisure and business trips. Overall, the demand for trips is price elastic for leisure and inelastic for the business segment. Moreover, the approach is flexible enough to study elasticity on different dimensions, showing that the demand for business trips could become highly elastic in some contexts like departures during weekends, international destinations, or when the reservation is done with enough anticipation. All these insights are of a particular interest for travel providers (e.g., airlines) to better adapt their offer, not only to the segment but also to the context.

An integrated reinforced learning and network competition analysis for calibrating airline itinerary choice models with constrained demand

This paper presents a novel methodology to develop itinerary choice models (ICM) for air travelers that addresses the limitations of the traditional utility-maximization approach. The methodology integrates a reinforcement learning algorithm and an airline network competition analysis model. The reinforcement learning algorithm searches for the values of parameters of the itinerary choice model while considering maximizing a reward function. The reward function is measured as the negative of the difference between the estimated and observed system metrics. The airline network competition analysis model is used to calculate the estimated system metrics. It is a simulation model that represents passenger-itinerary assignment. It captures the demand–supply interactions at the network level while considering the competition among all airlines. An ICM system is calibrated using the developed framework considering the global airline network, which includes more than 500,000 airport pairs. Validating the model against ground truth data shows that the developed model adequately captures the travelers’ itinerary choice behavior and replicates the competition pattern among airlines.

Effects of Personality Traits on Travelers’ Heterogeneous Preferences Toward Air Itinerary Choice: Analysis from Urumqi, China

Effects of Personality Traits on Travelers’ Heterogeneous Preferences Toward Air Itinerary Choice: Analysis from Urumqi, China

Cruise trajectory network and seasonality: empirical evidence from Queen Elizabeth cruise

ABSTRACT Affected by climate and natural conditions, cruise activities have obvious seasonal characteristics. For a cruise ship, seasonality consists of the systematic, although not necessarily regular, movement of a cruise ship in a selected time. To examine more specifically the natural factors of the seasonal characteristics from a microscopic perspective, we construct month-temperature, quarter-season, and year-climate zone, a seasonal analysis framework in two dimensions of space and time and using a single world cruise ship named Queen Elizabeth (QE) as the case. Comparing the characteristics of the trajectory network with the seasonal factors, we find the cruise ship tend to voyage in a stable temperature range of 10°C–20°C, flee the winter and catch up with the summer and spring, and mainly stay in the temperature maritime climate zone, the subtropical monsoon humid climate zone, and the Mediterranean climate zone to form several branch network. Finally, the academic and practical implications of the research findings for seasonal management and itinerary choice are elaborated from the supply-side and demand-side.

Pedestrian itinerary choice: between multi-sensory, affective and syntactic aspects of the street pattern in the historic quarter of Bejaia, Algeria

Consideration of the five senses and emotions is not well explored in the urban environment. Different fields deal with the influence of the physical environment on human experience. However, the role of the multi-sensory experience and affectivity in selecting the itinerary for pedestrian mobility is not yet well studied. Several methods have been proposed to evaluate the correlation between pedestrian movement and the environmental configuration, where the visual parameter is the main determinant. Given that other senses could be used to select any itinerary, since the urban environment is perceived at a multisensory level, the aim of the present study is to investigate the relationship between people's multisensory experience and their navigation in an urban environment in terms of itinerary choice. This study is carried out on the streets linking the city centre to the seafront in Bejaia, Algeria. The investigation was based on qualitative and quantitative methods. The former consists of the organised walk with running commentary technique. The latter consisted of a syntactic analysis of the street pattern. The results show that people choose their itinerary not only on the visual aspect created by the spatial configuration, but also on several variables related to the affectivity and multi-sensory experiences of the urban environment. The findings will be discussed with regard to their usefulness for the design and development of urban publics spaces.

Stacked Hybrid Discrete Choice Models for Airline Itinerary Choice

This study develops a methodology to train and apply a hybrid stacked discrete choice model for airline itinerary choice. This stacked model framework includes a data-driven component (i.e., gradient boosting machines) as well as a theory-driven component (i.e., utility maximization using generalized extreme value models). The resulting ensemble model combines attractive features of each, including the ability to conform to complex nonlinear relationships among itinerary characteristics, as well as the ability to leverage an analyst’s understanding of travel behavior tendencies and the natural relationship among itineraries. Using a real industry dataset containing purchase information for approximately 10 million air travelers, it is demonstrated that the resulting model outperforms either the gradient boosting or utility maximization modeling paradigm alone in forecasting air traveler choice behavior. Implementation of this model can be achieved using efficient open source tools including XGBoost and Larch, and requires relatively modest additional effort by an analyst above and beyond the effort to use either tool alone.

Research on Passenger Flow Assignment of High-Speed Trains Based on Personalized Itinerary Choice

Under the condition that ticket fare for high-speed train could fluctuate around a benchmark price in China, aimed at predicting how the passengers will distribute among different trains given a ticket fare, the passenger flow assignment method for high-speed trains is studied. Different from the classical researches on the passenger flow assignment, by introducing a variable that represents the value of time, this research allows passengers to make their personalized choice between the principles of time minimization and expense minimization, so as to demonstrate how the passengers holding different time values respond to each ticket fare scheme. An equilibrium passenger flow assignment model based on personalized choice is built and an improved Monte-Carlo random simulation algorithm is designed for solving the model. The actual ticket sale data for Beijing-Shanghai high-speed railway are used to verify the feasibility of the proposed model and algorithm. The passenger flow assignment results under various fare schemes show how the distribution of passenger flow changes with the adjustment of ticket fare.

Manage with care: the frailty of self-connections in the European airport network

This study evaluates the attractiveness of self-hubbing in terms of the (a) symmetry of itineraries and the consequences for passengers in the case of missed flights. We compute the most attractive European origin-destination (O-D) pairs through self-connection and evaluate their robustness by estimating the expected delays relative to connecting times and the travel options available when a connection is missed.Results show that the potential of self-connecting markets is reduced when accounting for asymmetrical travel options and the consequences for travelers in the case of missed flights. In terms of frequencies, self-connecting passengers are, on average, found to have fewer alternatives to complete a given O-D pair than in the case of alliance-based connections (− 33%). Our findings moderate the confidence of past evidence on self-hubbing in light of the concrete reliability of self-connections for passengers. The itinerary choice made by passengers inevitably depends on the evaluation of travel quality attributes related to the (a) symmetry of the itineraries and the costs incurred through missed connections.

Modeling social influence using sequential stated adaptation experiments: A study of city trip itinerary choice

This paper introduces a model that captures the effect of social influence on decision makers’ choice behavior in the context of sequential stated adaptation experiments, in which a decision maker first chooses the alternative he/she personally likes best from an experimentally varied choice set and then, after being informed about the choice of a certain social network member, chooses again from the same choice set. Social influence represents the effect of the choices of one or more social network members in the same choice situation on the choice behavior of a decision maker. It is assumed to depend on a decision maker’s socio-demographics and personality traits, and the strength of the relationship with the social network member. The concept of choice consistency is introduced to reflect a respondent’s tendency to stick with his/her original choice in completing the sequential stated choice tasks. By accounting for this effect, a less biased estimate of social influence is obtained. Similarly, the model accounts for the case that the first choice of the respondent and the social network member’s choice are identical by introducing a correction factor to further reduce the bias in the estimated social influence. To access the validity of the model, the choice of city trip itinerary was chosen as an example. Decision makers’ personality traits and their relationship with the social network member were treated as latent variables. Therefore, first, exploratory and confirmatory factor analysis was carried out to investigate the reliability and validity of the constructed scales. Then a sequential estimation approach was employed to avoid confounding between decision makers’ preferences towards attributes and the effects of social influence and choice consistency. In the first stage, the taste parameters were estimated and then in the second stage the composition of social influence effect and choice consistency effect were estimated simultaneously. Results show that the model reproduced the observed data well. Social influence has a modest but significant effect on decision makers’ choice behavior; decision makers’ socio-demographics and personality traits, social network type as well as the relationship with social network members all have significant effects on social influence. The effect of choice consistency is also significant.

Incorporating continuous representation of preferences for flight departure times into stated itinerary choice modeling

The passenger volumes of cross-Strait air travel between Taiwan and China have grown rapidly, largely due to the gradual introduction of direct flights. This study captures passengers’ preferences for airlines and flight departure times in the Taiwan–China market. The model structures examined include the multinomial logit, nested logit, and mixed nested logit models. In particular, trigonometric functions are incorporated into choice modeling to identify passenger preferences regarding flight departure times. A stated preference survey was conducted at Taiwan Taoyuan International Airport. A mixed nested logit model, which captures substitution patterns among competitive alternatives as well as individual heterogeneity, outperforms the other models. The estimation results indicate high degrees of competition among airlines and flight-departure times, especially highly substitutable departure times within the same airline. The continuous departure-time specification, which uses a series of sine and cosine functions, adequately captured passenger preferences. From the qualitative aspect of airline services, a premium for service improvement is highest for cabin crew service, followed by onboard food and check-in service. Airlines can use these results to improve their service quality and adjust flight schedules.

Using Internet-based marketplaces to conduct surveys: An application to airline itinerary choice models

Within the transportation community, there has been increasing interest in using online outsourcing platforms such as Amazon Mechanical Turk (AMT) to conduct surveys. To date, transportation researchers’ use of AMT has been justified based on findings from studies in other fields. That is, to the best of our knowledge, there has been no study that has evaluated how the distribution of responses associated with each question and behavioral model estimated from AMT survey data compares to survey data collected from a traditional platform for a travel behavior application. This paper fills an important gap in the literature by examining (1) whether the distributions of responses from AMT and Qualtrics (a traditional market research firm) respondents are statistically equivalent, and (2) whether itinerary choice models estimated from these two surveys are statistically equivalent? Results show that AMT and Qualtrics respondents reported similar air trip characteristics and were drawn from a similar geographic distribution, but they exhibited distinct sociodemographic characteristics. After controlling for different age distributions in the two datasets, we found that airline itinerary choice models estimated from the AMT and Qualtrics survey data produced similar results, with the key difference related to price sensitivities. Our study provides preliminary evidence on the viability of using AMT and similar online outsourcing platforms for air travel behavior studies.