Passenger Choice Behavior Research Articles

Study on Route Choice Behavior of Urban Rail Transit Passengers

In order to capture spatiotemporal distribution pattern of passenger flow under networked condition, it is necessary to analyze route choice behavior of urban rail transit passengers. First, angular cost value and comfort index are defined to reflect the influence of network structures, route directions and in-vehicle congestion on passengers’ route choice behavior respectively; Then, two route choice models are proposed respectively for peak and off-peak hours, in which new variables including angular cost value, comfort index and personal characteristics, as well as level of service variables (i.e. in-vehicle travel time, number of transfers and transfer time etc. , which are usually found in the base model) are considered. Finally, the models are calibrated with the surveyed data from Guangzhou Metro and compared with each other. The results show that the new variables significantly improve models’ explanatory and predictive abilities on route choice behavior of urban rail transit passengers.

Research on Passenger's Choice Behavior Based on Beijing-Guangzhou High-Speed Railway Ticket Data

This paper has analyzed passenger flows' characteristics of Beijing-Guangzhou high-speed railway based on the ticket data of 2013 January, including passengers' distribution on the ranges of trip distance and passengers' composition in each range. Furthermore, it concentrated on analyzing preferences on passenger's choice behavior among the passengers of different OD pairs and the difference between weekday and weekend on passenger's behavior. The result shows short journey passenger had lower sensitivities on ticket price and travel speed, the selection of passengers from low-level node to high-level node on departure time had obvious clustering effect. With the increase of trip distance, the sensitivities of passengers on ticket price, travel speed and originating train would increase correspondingly. The paper's conclusion has referential meaning for researches on the optimization of high-speed railway transport organization.

鉄道利用客の車両選択行動について

Vehicle choice behavior of railroad passengers was modeled using five essential factors. Using the actual data of passengers, unknown parameters were estimated, and the accuracy of the model was confirmed. From the estimated values of parameters, we discussed the degree of influence of each factor on vehicle choice behavior that varies according to travel direction and the time of day. Finally, we estimated the spatiotemporal distribution of passengers on each platform, and discussed the time loss, which will be observed in the process of passengers' getting on and off when they are distributing unevenly on platforms according to their preference.

Effect of Height on Pedestrian Route Choice between Stairs and Escalator

In order to overcome the subjectivity of existing pedestrian route choice models, an alternative choice model is presented based on the utility equation. It is composed of several indirectly objective characteristic variables, including the height, length, and width of interlayer facilities; speed of automated facilities; and carry-on luggage. Considering the scene that pedestrians choose between the stairs or escalators, an extended binary logit model is developed. Calibration and validation of the model are accomplished by using the data collected in four typical passenger transfer stations in Beijing, China. The results show that the proposed model has an average accuracy of 86.56% in bidirection for predicting pedestrians’ behavior. An interesting phenomenon can be found that the length of facility has poorer impact than height on pedestrians’ route choice behavior. Some quantitative and irradiative conclusions have been illustrated on the relationship between the selection probability and the variables, which is expected to be valuable for extracting the implicit theoretical mechanism of passenger choice behavior.

Probability Model of Upstream Passenger who Select Escalator in High-Speed Rail Hub

In order to explore the characteristics of upstream passengers select between stair and escalator in high-speed rail hub, this paper analyzed the influencing factors of passenger choice behavior, based on observing the behavior of upstream passengers in high-speed rail hub. Probability choice model of upstream passenger was established according to regression analysis using logistic function, the parameters of the model were calibrated and the validity was verified using survey data. The results show that, five factors—the height of the stair, the number of luggage, gender and age of passenger, and queuing number at the entrance to the escalator can affect the choice behavior of upstream passengers, and the forecast accuracy is up to 92.3% using the proposed probability model of upstream passenger who select escalator in high-speed rail hub.

The Reliability-Based Stochastic Transit Assignment Problem with Elastic Demand

This paper examines the reliability-based stochastic transit assignment problem with elastic demand. A Variational Inequality (VI) model for this problem is developed. The VI model considers capacity, waiting time and in-vehicle travel time as stochastic variables, and includes Spiess and Florian’s (1989) and de Cea and Fernandez’s (1993) models as special cases. A reliability-based stochastic user equilibrium condition is defined to capture the route choice behavior of passengers. To illustrate the properties of the VI model, numerical studies were conducted on de Cea and Fernandez’s (1993) network. The studies also show that Spiess and Florian’s and de Cea and Fernandez’s models can overestimate the system performance substantially.

Optimization of train plan for urban rail transit in the multi-routing mode

The train plan of urban rail transit under multi-routing mode can be divided into three parts: train formation, train operation periods and corresponding train counts of each routing in each period. Based on the analysis of passenger’s general travel expenses and operator’s benefits, the constraints and objective functions are defined and the multi-objective optimization model for the train plan of urban rail transit is presented. Factors considered in the multi-objective optimization model include transport capacity, the requirements of traffic organization, corporation benefits, passenger demands, and passenger choice behavior under multi-train-routing mode. According to the characteristics of this model and practical planning experience, a three-phase solution was designed to gradually optimize the train formation, train counts as well as operation periods. The instance of Changsha Metro Line 2 validates the feasibility and efficiency of this approach.

Airport ground access mode choice behavior after the introduction of a new mode: A case study of Taoyuan International Airport in Taiwan

To improve airport ground access at Taoyouan International Airport (TIA) in Taiwan, the government of Taiwan is constructing a mass rapid transit system (TIA MRT) to connect the airport and important traffic hubs such as the Taipei train station and the Taoyouan High Speed Rail station. Using revealed and stated preference data, we investigate the airport ground access mode choice behavior of air passengers who are traveling overseas from Taiwan. A mixed logit model is estimated to identify the preferences of air passengers for the new mode. The results indicate that while out-of-vehicle travel time and in-vehicle travel time are two important factors in affecting outbound travelers’ choice of airport access mode, the amount of overall time-savings and the user-friendly nature of the modal offers are also crucial attributes. We show the extent to which travel time improvements associated with public transportation reduces the market share of private transportation and taxi.

Model of Dynamic Pricing for Two Parallels Flights with Multiple Fare Classes Based on Passenger Choice Behavior

Airline revenue management (ARM) is one of emerging topics in transportation logistics areas. This paper discusses a problem in ARM which is dynamic pricing for two parallel flights owned by the same airline. We extended the existing model on Joint Pricing Model for Parallel Flights under passenger choice behavior in the literature. We generalized the model to consider multiple full-fare class instead of only single full-fare class. Consequently, we have to define the seat allocation for each fare class beforehand. We have combined the joint pricing model and the model of nested Expected Marginal Seat Revenue (EMSR) model. To solve this hybrid model, we have developed a dynamic programming-based algorithm. We also have conducted numerical experiments to show the behavior of our model. Our experiment results have showed that the expected revenue of both flights significantly induced by the proportion of the time flexible passengers and the number of allocated seat in each full-fare class. As managerial insights, our model has proved that there is a closed relationship between demand management, which is represented by the price of each fare class, and total expected revenue considering the passenger choice behavior.

Joint Dynamic Pricing for Two Parallel Flights based on Passenger Choice Behavior

Abstract Airline revenue management of a single flight has been a subject of extensive study in the last few decades. Starting from the choice behavior of passengers between flights, this article studies the joint dynamic pricing for two parallel flights, which are owned by a same airline company and are scheduled at different times. In the model, potential passengers make ticket-purchasing choices based on the combination of departure times and selling prices. We classify the passengers into two categories: those who have strict requirements on departure times and those who are flexible toward departure times. By dynamically adjusting the ticket prices, the airline controls/inducts the demand to match the fixed seat inventories, so as to maximize the total expected revenue of the two flights. We characterize the structure of the optimal joint pricing decisions by analyzing on a discrete-time model. Our research shows that the searching space of the optimal pricing decisions can be shrunk to a minimum level; and therefore the computational effort can be significantly reduced.

Modeling Impact of Transit Operator Fleet Size under various Market Regimes with Uncertainty in Network

A network-based model is proposed for investigating the impact of transit operator fleet size on a network with uncertainty under various market regimes. The market regimes concerned include monopoly, free competition, and oligopoly. In the proposed model, the transit operator aims to determine fleet size to optimize its objective function while accounting for transit passengers’ travel choice decisions that minimize their own perceived disutility of travel. To consider the uncertainty effects on transit networks, the proposed model incorporates the unreliability component of transit services into the passenger disutility function, which is mainly due to variations in in-vehicle travel time and the dwell time of transit vehicles at stops. A numerical example is used to illustrate the application of the proposed model. Numerical results show that transit service unreliability could significantly influence transit system performance and the travel choice behavior of transit passengers.

Modeling Joint Choice of Airline Itinerary and Fare Product

The rapid expansion of low-cost airlines and the development of online distribution of airline tickets have put pressure on the pricing and revenue management strategy of network airlines and have substantially modified the airline passenger choice environment, especially in short-haul markets. To analyze the choice behavior of airline passengers, a unique data set is being developed. Passenger booking data are combined with fare class availability data collected daily for a 90-day booking period, and fare rules are applied to reconstruct the passenger choice set and determine which fare products were available at the time of the booking on nonstop itineraries in select business-oriented European short-haul markets. Estimation results from a multinomial logit model of the joint choice of airline itinerary and fare product show that outbound passengers tend to prefer early morning and late afternoon flight departures that allow them to conduct their business activities either before or after their trips. In addition, a significant proportion of passengers traveling during the week tends to prefer higher-priced fully flexible fare products to cheaper nonflexible options; this preference shows the revenue potential of a multiproduct pricing strategy in markets affected by the presence of low-cost competition. This study illustrates how such models may be used to support airline decision making in scheduling, pricing, and revenue management.

A Study on the Traveling Time Shortened by Common Tariff System

Introduction of a common tariff system not only makes new demand by fare reduction in transfer but also shortens the traveling time forpresent passengers by providing various options of routes. This paper describes the construction of a dynamic transport network model which displays the condition of mass transit service and route choice behavior of passengers, for calculating the amount of shortening in the traveling time. The result of analysis in the urban network of Kyoto makes sure of shortening the traveling time by introduction of common tariff system, and displays its cause.

Impacts of yield management in competitive airline markets

This paper presents the results of the first known study of the impacts of airline yield management (YM) under competitive market conditions, taking into account the YM capabilities of competing airlines. This study makes use of a simulation model that includes both passenger choice behavior and the actual functions of airline yield management systems. We used this simulation model to evaluate the impacts of YM systems on the market shares, traffic and revenues of each competitor in a hypothetical market, as well as on the market as a whole. The simulation results confirm that effective yield management results in revenue increases for the users of YM in virtually all competitive situations. We found that there is a significant ‘first mover’ advantage for the airline that implements a YM system before its competitor, and that this implementation can actually lead to a revenue decrease for the airline without YM. We also examine some scenarios in which the airlines have differing schedules in the market, and conclude that there is an important interaction between the revenue benefits of YM and the relative schedule strengths of the competing airlines.

Sensitivity of Level of Service in Shorthaul Market

Current transportation planning requires a better understanding of the mode choice behavior of intercity passengers in the short-haul market. Considerable work has been completed on the topic for the medium and long-haul ranges, but a limited body of literature is addressed to the problem relative to the short-haul market. In this study, an attempt has been made to construct behavioral models incorporating modal level of service attributes. An aggregate and a disaggregate mode choice model are formulated based on the logit function. Using linear and nonlinear regression for the calibration of the aggregate model, and the maximum likelihood estimation method for the disaggregate model, the formulations are tested for reliability and validity by comparing them with other published research findings. The model specification is found to be acceptable. For the data base used in this research, the log-linear model produced better results. The method has been used to analyze the desirable strategy of local air services and to evaluate the sensitivity of the automobile user to highway conditons and highway tolls.