Passenger Choice Research Articles

Game model of high-speed railway delay and passenger choice behavior under incomplete information

The arrival status of high-speed trains is an important factor affecting the choice behavior of railway passengers. In order to specifically analyze the influence of the arrival state of high-speed trains on the choice behavior of railway passengers, evolutionary game theory was used to analyze the arrival state of high-speed trains and the choice behavior of railway passengers, and the game matrix of the arrival state of high-speed trains and the choice behavior of railway passengers was constructed. The location of the equilibrium point and the system evolution mechanism are deeply analyzed. Matlab simulation results show that the evolutionary game behavior finally has two evolutionary stable states. When high-speed trains delay time is less than 30 min , the train tends to arrive on time, and the railway passengers tend to choose the ride strategy. When the delay time is greater than 30min, the train tends to arrive on time, and the railway passengers tend to choose the refund strategy. By improving the facilities in the high-speed railway station, the railway passengers can choose the transfer strategy. Changing the value of the game parameters can adjust the evolution direction of both sides of the game and effectively improve the proportion of railway passengers when the high-speed train is delayed.

Assessment on resilience of urban agglomeration transportation system considering passenger choice and load-capacity factor

Intercity transportation system (ICTS), characterized by large-scale, high spatial-temporal concentration, and sparser departure frequencies, is more vulnerable in unexpected events. Understanding the resilience characteristics of ICTS is crucial for maintaining the network service capabilities. Aiming to conduct effective resilience assessment on ICTS, we develop the resilience simulation model by introduce dual-regulated parameters for network load and capacity into cascading propagation model under interruption events, and quantify the impact of travel distance, time costs, and route redundancy on travel choice of passengers. Meanwhile, propose service resilience indicators from both the passenger's and the system's perspectives. Finally, we conduct a case study on the resilience of ICTS in Beijing-Tianjin-Hebei Urban Agglomerations (BTH-UA). The results show that: 1) Multimodal transportation systems usually exhibit better resilience than unimodal systems. 2) For various resilience optimization metrics, it is essential to choose targeted recovery strategies to maximize network resilience. 3) Traveler sensitivity to travel time significantly influences the resilience of passenger-based network services. 4) Changes in transportation supply capacity and travel demand will impact the system's resilience. The research findings can provide valuable references for the resilience development and management of urban transportation systems.

Integrated train timetable with supply–demand interactions at the Chinese high-speed railway

In traditional Chinese railway operations, train timetable and unit assignment are usually separated from pricing and seat allocation, resulting in a decrease in revenue and an increase in operating costs. In this paper, we study the integrated problem of train timetable, unit assignment, pricing, and seat allocation, where passenger choice behavior is modeled by the multinomial logit (MNL) model. This integration enables us to explicitly capture supply–demand interactions. The problem is formulated as a mixed integer nonlinear programming model with the objective of maximizing total profit. By converting the MNL model into its equivalent convex form, we recast the model as a tractable reformulation, allowing the use of a general-purpose solver to solve practical-sized instances. Based on real data from high-speed railway lines in China, three sets of case studies are conducted to validate the effectiveness and efficiency of the proposed approach.

From galley to gourmet: experience accounting perspective on the evolving dining choices of cruise passengers

ABSTRACT This study examines shifts in consumer dining preferences among cruise passengers in response to the COVID-19 pandemic. Utilizing text-mining techniques applied to a collection of online reviews, the discourse analysis in this study is guided by the restaurant-management tool, Experience Accounting (EA), which represents an integrated marketing-accounting analytical framework. We further extend the experiencescape model to the cruise dining context by subcategorizing the sensory component as a more instrumental approach for the food service industry.Our findings indicate a significant shift in cruise ship passengers’ dining preferences away from utilitarian meals toward the fine-dining setup, representing significant challenges for the hospitality industry.

Optimal fare and headway for a demand adaptive paired-line hybrid transit system in a rectangular area with elastic demand

Demand adaptive paired-line hybrid transit systems that integrate fixed- and flex-route transit have emerged in the last decade and attracted increasing attention because of their potential to improve accessibility for passengers. To facilitate the operation of such a hybrid transit system, this study develops a model to determine the optimal fare and headways associated with fixed- and flex-route transit along a rectangular corridor. Compared with existing literature, the novelty of this study lies in designing the fare structure while simultaneously considering demand elasticity and passenger behaviour. A continuous approximation modelling approach is employed to derive the agency’s and travellers’ cost components. Using these, a nonlinear programming optimisation model is formulated to minimise the total user cost subject to the agency’s nonnegative revenue constraints and passengers’ route choice behaviour, which is characterised as a path-size logit model. Numerical experiments are performed using a stylised network to examine the properties of the model, in which the solution is obtained by combining a brute force method that enumerates headway and fare combinations and an iterative method that determines equilibrated passenger choices. The results show that as potential demand density increases, fare and headway fluctuate and drop, while the percentage of passengers choosing to ride on flex-route transit increases. In addition, there may be an optimal maximum offset distance, which is defined as the width of the corridor to be covered by the transit system, when the potential demand density is low, leading to minimum user cost and maximum travel demand within the service area.

Evaluating the Eco‐Efficiency of Urban Air Mobility: Understanding Environmental and Social Impacts for Informed Passenger Choices

AbstractFull electric aircraft with zero CO2 emissions during operations are a promising pathway towards achieving the Fly Net Zero by 2050 goal proposed by the International Air Transport Association (IATA). In this context, Urban Air Mobility (UAM) expects to offer an environmentally friendly alternative through electric Vertical Takeoff and Landing (eVTOL) aircraft. While eVTOLs produce no greenhouse gas emissions, the comprehensive eco‐efficiency of UAM goes beyond the flight phase. This paper delves into evaluating UAM operation's environmental and social impacts, considering the urban space, public perception, operational profiles, and power consumption. Employing causal loop analysis, the study uncovers the relationships that add value or increase impact, assessing UAM passenger transportation's eco‐efficiency. Moreover, the Unified Architecture Framework is used to model the UAM ecosystem and to propose strategies to balance values and impacts in achieving eco‐efficiency. By shedding light on the sustainability viewpoint, this study emphasizes the importance of holistically understanding UAM's operational impact and empowering users to make eco‐efficient choices when opting for UAM transportation. Finally, strategies to provide sustainability awareness to the user are discussed in the Mobility‐as‐a‐System (MaaS) context.

Analysis of multi-modal public transportation system performance under metro disruptions: A dynamic resilience assessment framework

The well-functioning multi-modal public transportation systems play crucial roles in reducing traffic congestion, alleviating environmental pollution, and improving mobility. There are variants of disruptions that may impede the smooth functioning of public transportation systems and challenge their normal operations. It is essential to develop resilient multi-modal public transportation systems, and a reasonable resilience assessment framework serves as the foundation for developing such systems. In this paper, we propose a resilience assessment framework based on performance-based measurement metrics. We develop a dynamic simulation procedure that considers the impacts of disruptions on passenger choices of path and departure time. By incorporating these features into the simulation procedure, we can effectively model the variations of passenger flow under disruptions. Finally, to demonstrate the proposed resilience assessment framework and dynamic simulation procedure, the multi-modal public transportation system in Beijing is considered as a case study. The results reveal that the system exhibits good resistance and recovery capabilities, but it lacks robustness. Additionally, we discuss the effects of downtime, bus frequency, bus bridging services, and proportions of commuters on resilience, which lead to several valuable policy implications.

Is increasing the frequency an effective measure for the renaissance of inter-city coach? A longitudinal study of the relation between volume and frequency

In recent years, inter-city coach (ICC) services have seen a gradual decline in passenger volumes due to various external influences. Typically, ICC companies respond to this situation by reducing the frequency of their services. However, this reduction in frequency can affect passenger choice, potentially leading to an even further decrease in volumes. This suggests a potential endogeneity between ICC volumes and frequencies. To investigate this often-overlooked issue, our study establishes a simultaneous equation model. We use four years of longitudinal ICC journey data and apply Ordinary Least Squares (OLS) and Fixed-Effect Three Stage Least Squares (FE-3SLS) regression methods to compare the differences brought about by endogeneity. Our findings indicate that the interaction between volumes and frequencies will be underestimated without considering the endogeneity. Furthermore, we segment the ICC market to explore the heterogeneity of different routes. Our results show that the volumes of long-distance routes are most significantly impacted by frequency changes. Moreover, volumes of routes connecting counties appear to be more sensitive than those between cities. While increasing service frequency may boost volumes to a certain extent, it is crucial for ICC companies to develop differentiated strategies based on market segmentation. Shifting the operational focus to county-connecting routes could help avoid direct competition with other transportation modes, such as high-speed railways.

Behavioural modelling of metro car choice

Overcrowding in metro systems can exert a negative impact on the travel experience of the passengers and on the system’s capacity. This can affect passengers in different ways, worsening their perception of safety, altering their behaviour, decreasing their satisfaction, and leading to higher travel-time sensitivities. One of the causes of overcrowding can be attributed to some metro cars being selected more often than others, and thus, the distribution of passengers inside the train not being homogeneous. This study adopts a disaggregated approach based on the observed behaviour to understand why some specific metro cars are preferred. The characteristics of passengers and metro cars that generate heterogeneity in the load profiles are identified and the motives behind passenger choices are understood by formulating a hybrid latent-class choice model. The behavioural models consider sociodemographic characteristics, level-of-service information, and attitudinal latent variables. Once a metro car choice model was obtained, an operational micro-simulator of the metro was programmed to evaluate how passengers' choices would be affected by altering variables related to the design of the stations, assessing it based on the current situation. Subsequently, interventions to station layouts are made based on the forecast of the behavioural models, to affect passenger choices and produce more homogeneous passenger distributions in the train. The intervention presented in this study reduced 46.49 h of total travel time during the morning peak hours; the impact was greater when considering individual perceptions. Incentives can be developed for optimising the distribution of passengers in metro cars by understanding passenger choice using a behavioural approach, and therefore, they can improve the passenger experience.

Modelling bus bunching along a common line corridor considering passenger arrival time and transfer choice under stochastic travel time

This study examines bus bunching along a common-line corridor, considering crucial factors underexplored in existing literature, such as stochastic travel times, passenger arrival patterns, and passenger transfer behaviours. We first develop a bus motion model that captures the interaction between bus trajectories and passenger movement. Then we formulate a reliability-based passenger arrival time choice and a transfer choice model to characterise passengers’ behaviours. Afterwards, the bus motion model and the passenger choice models are integrated, and a Method of Successive Averages type iterative algorithm is developed to obtain stable passenger arrival patterns and transfer choices. Numerical experiments are carried out on a hypothetical network followed by a case with real-world data. Our findings demonstrate that a high transfer demand could amplify the propagation of bus bunching across lines along the common-line corridor. Meanwhile, a 50% increase in transfer demand leads to a 24%–30% rise in headway fluctuation. Furthermore, our results suggest that non-uniform passenger accumulation patterns can restore headway regularity as a result of coordinated passenger movement and bus motions, thus alleviating the persistent deterioration in bus bunching.

Airport choices and resulting catchments in the U.S. Midwest

Air travelers originating from the catchment areas of airports – typically defined by administrative or geographic boundaries – will sometimes “leak” across these boundaries to farther, often larger airports that offer more attractive services and options. To investigate the attractiveness of different airports based on their proximity and geographic position relative to one another, we assess the airport choices of air passengers across a large region of the US Midwest that spans multiple administrative boundaries and airports. We combine a dataset of air tickets purchased by these passengers with other publicly available datasets, and apply mixed logit airport choice models based on air service attributes and airport access distance. We take multiple subsets of the total air tickets dataset using Halton sampling in order to achieve model estimation convergence. Model estimates are compared between data subsets, and consistently lead to the following key results. First, changes in air service attributes on short- and medium-length routes will have a more uniform influence on air passengers' airport choices, compared with long routes. Second, comparable proportional changes in air service attributes lead to greater changes in the catchment areas and market shares of small and medium airports, compared with large hubs. Within these service characteristics, airfare is found to impact passengers' airport preferences more than flight frequency. Finally, the catchment areas and market shares of small- and medium-size airports are influenced by the proximity and geographic location of neighboring airports; this differs from the catchments of large airports, which strongly extend into multiple jurisdictional boundaries in all directions despite the presence of surrounding airports. The study results provide further insight into the influence of air service attributes on long-distance airport choices over large geographic regions served by a diverse set of airports, and the resulting implications on multimodal transportation planning within and across such regions.

Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China

To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.

Competitive integrated airline schedule design and fleet assignment

Airline profits are significantly influenced by competitive timetables that match passenger demand to fleet resources. In this research, we develop an integrated, mixed-integer optimization model to derive a comprehensive flight schedule, fleet assignment, and average airfares jointly. Passenger choice behavior is incorporated through a prospect theory-adjusted, nested, multinomial logit model which estimates market share. The competitive fleet assignment and schedule design (CFSD) formulation is embedded in a differentiated Bertrand game, such that each transport operator optimizes their best response function connected through the market share model. To solve the integrated optimization problem efficiently, a hybrid algorithm is developed that combines stabilized column generation with a large neighborhood search algorithm.The game-theoretic framework is applied to a case study in China involving legacy airlines, a low-cost carrier, and a high-speed rail (HSR) operator. The equilibrium outcomes suggest that the low-cost carrier is likely to stimulate demand and improve the level of service through secondary airports. The further development of high-speed rail service is expected to intensify competition among airlines and reduce the overall fare level. The low-cost carriers and HSR are able to significantly increase overall consumer surplus, providing insights into the potential for low-cost service in the Chinese aviation markets and the entrance of the public service obligation program at minimum cost.

Research on Choice Preference of Parallel Trains for High-Speed Rail Heterogeneous Passengers

The passenger capacity utilization of parallel trains on China’s high-speed railway (HSR) varies greatly, which seriously affects the revenue and passenger service quality of railroad companies. One of the key factors contributing to this issue is the uniformity of fares. To address this, China’s railway enterprises are implementing differential pricing reforms for parallel trains. This paper presents a two-step method that combines latent class analysis (LCA) and random parameter logit (RPL) models to estimate the preferences of diverse passengers for parallel trains and quantify them using willingness to pay. To illustrate this, we conducted revealed preference and stated preference questionnaire surveys at Nanning East Railway Station, China, focusing on the Nanning-Guangzhou HSR route. These surveys gathered data on passengers’ personal and travel attributes. LCA was carried out according to passenger attributes, and passengers were divided into three categories according to the goodness-of-fit measures. Through the questionnaire data, the travel time and departure time period of each category of the passenger were converted into costs, and the absolute value of each influencing factor of passengers for parallel trains was obtained. Considering the limited rational choice behavior of passengers, the perceived value of each influencing factor of passengers on parallel trains was calculated through prospect theory. Finally, an RPL model for parallel train choice of heterogeneous passengers was constructed, and the parameters of each category of the passenger were estimated based on the absolute value and perceived value of the influencing factors.

Introducing TimPassLib – A Library for Integrated Periodic Timetabling and Passenger Routing

Classic models to derive a timetable for public transport often face a chicken-and-egg situation: A good timetable should offer passengers routes with small travel times, but the route choice of passengers depends on the timetable. While models that fix passenger routes were frequently considered in the literature, integrated models that simultaneously optimize timetables and passenger routes have seen increasing attention lately. This creates a growing need for a set of instances that allows to test and compare new algorithmic developments for the integrated problem. Our paper addresses this requirement by presenting TimPassLib, a new benchmark library of instances for integrated periodic timetabling and passenger routing.

MODELING THE SELECTION OF A SUSTAINABLE BICYCLE TRANSPORT SERVICE

This article investigates the impact of travel information on transport choice behavior and the impact of travel time display. To this end, we estimate linear regression models using data from the Mobility Survey, which consists of observational data on transport mode choice. Based on the results obtained through the conducted questionnaire surveys, many influencing factors on the choice of transport were studied through the correlation equation. Using the applied Logit model, the maximum likelihood method was used for the selection of the mode of movement in the evaluation of the objective function of passengers in inter-address movement. The objective function in the choice of which of the available alternatives was preferred by each passenger in moving to the intended destination was evaluated. The important variable attributes of the alternatives that influence the choice of inter-destination transportation means of passengers are identified. The choice model represents the probability of choosing each alternative through the calculated Logit model. A linear regression equation was developed using the coefficients obtained on the basis of the correlation matrix. The results of the regression model were obtained as part of the interaction of the transport choices of passengers.

Probability Analysis of Passenger Safety When Using Rail Transport

The factors that most often determine the choice of a place by a traveler have been identified and studied. Probabilities related to passenger choice based on distance, frequency of use, type of carriage/compartment and specific seat are defined. Conclusions are drawn for the safety of passengers. Specific measures are proposed to improve safety in the identified scenarios.

Level-$K$ Reasoning, Deep Reinforcement Learning, and Monte Carlo Decision Process for Fast and Safe Automated Lane Change and Speed Management

This paper presents a decision process model for real-time automated lane change and speed management in highway traffic. The presented algorithm is developed based on level- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> game theory to model and predict the interaction between the vehicles. Using deep reinforcement learning, this algorithm encodes and memorizes the past experiences that are recurrently used to reduce the computations and speed up motion planning. Also, we use Monte Carlo Tree Search (MCTS) as an effective tool that is employed nowadays for fast planning in complex and dynamic game environments. This development leverages the computation power efficiently and showcases promising outcomes for maneuver planning and predicting the environment's dynamics. In the absence of traffic connectivity that may be due to either passenger's choice of privacy or the vehicle's lack of technology, this development can be extended and employed in fully-automated vehicles for real-world and practical applications.

Inequity averse optimization of railway traffic management considering passenger route choice and Gini Coefficient

Traffic management is crucial for improving the punctuality and reliability of train operations, enabling train operating companies (TOCs) to maintain their competitiveness and further increases the share and profits. A common goal of the train rescheduling problem is to minimize train delays, which fails to examine the results from the perspective of passengers. Moreover, focusing only on the punctuality performance overlooks how the delay is distributed among entities (i.e., trains, passengers, and train operating companies).We study the train rescheduling problem with the inclusion of passenger choices and the equity concerns. A mixed-integer linear programming (MILP) model is proposed to find the optimal train schedules and the best route for passengers at the same time, with respect to the demanded equity level. Passengers choose a sequence of train services to complete their trip with the least amount of costs (i.e., delays). To evaluate the equity performance of the system, we define equity by means of Gini Coefficient and Maximal Deviation, included in the MILP model as constraints.Experiments are conducted to explore the impacts of the objective change, i.e., from reducing train delays to reducing passenger delays, and to compare the system performance of using the two equity measures in terms of punctuality and equity. According to the results, the average passenger delay decreases by 34% when minimizing passenger delays, compared with that of minimizing train delays. Moreover, the Gini Coefficient yields less cost of equity (i.e., less increase of delays), compared to that of the Maximal Deviation.

Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan

The Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should also be arranged to consider the substitutability of trains between multiple routes combined with the passenger choice. Based on the principle of “operating by the flow” and the requirement for precise allocation of transport capacity for multiple routes, this article constructs a multiobjective nonlinear integer programming model by taking the minimized generalized travel cost of passengers, total running mileage of trains, fluctuation of trains for each route (as optimization targets), and the combination of requirements of both headways and fully loaded rates as constraints. A multiobjective genetic-based algorithm is designed to simultaneously optimize the TOP and the two-way train stopping time in each period. Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. The results show that the Pareto optimal TOP and dynamic train stopping time are significantly improved compared to the original values.