Fare Data Research Articles

Airline price responses in the face of demand shocks: European lessons from the COVID-19 pandemic

We exploit the exogenous demand shock in the aviation sector that resulted from the spread of the COVID-19 pandemic to investigate how airlines responded in terms of pricing strategies. By using monthly fare data for flights operated within the EU-27 member states over the period 2017–2022 and by quantifying the intensity of pandemic cycles through a stringency index, we estimate price regressions at the route–airline level. Our findings reveal that the demand drop has prompted lower fares and increased intertemporal price dispersion. Furthermore, we show that fare cuts are not explained by airlines operating fewer flights. By analyzing heterogeneous price responses to the demand shock as determined by various route and airline characteristics, we find a stronger reaction on international markets and greater resilience of low-cost airlines. We also show that the regulations in the form of public service obligations and state aid programs for airlines helped the beneficiaries but not travelers.

Trip Purpose Inference and Spatio-Temporal Characterization Based on Anonymized Trip Data: Empirical Study from Dockless Shared Bicycle Dataset in Xi’an, China

The rapid and widespread deployment and applications of the Internet of Things (IoT) and big data technology has generated massive personal travel data. Although most of the data are anonymized, they still provide possibilities for analyzing individual travel behavior. It is worthwhile to explore how to more accurately infer travel purposes based on trajectory data, smart card fare data, or shared bicycle data. In this paper, an improved research framework is proposed for travel purpose inference by applying the gravity model, Bayesian criterion, and spatial clustering method. The gravity model and Bayesian rule are used to calculate the probability of users traveling to nearby POIs (points of interest), and the clustering algorithm is used to identify the locations regularly visited by users. Through the identification of maximum probability POI and regular trips, different travel purposes represented by POI can be distinguished. The results showed that the identification of regular trips could verify and complement the recognition of trip purposes by POIs. Using Xi’an city of China as an example for the study, the results show that: 1) regular trips accounted for 32% of the week’s trips; 2) there are 10% of the trips reflect different trip purposes for the same POI; 3) it is also important to note that 70% of the users have a parking error of fewer than 100 m when going to the exact location. This distance can provide a reference for the study of bicycle commuting. Finally, we analyzed the spatial and temporal distribution characteristics of different trip purposes. The analysis of the spatial and temporal distribution can provide suggestions for the operation of bike-sharing enterprises and the management of regulators.

Transferable supervised learning model for public transport service load estimation

AbstractDetailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.

Impacts of competition on connecting travelers: Evidence from the transatlantic aviation market

This paper examines the effects of competition driven by low-cost airlines and high-speed trains on long-haul connecting routings. We use passenger and fare data for 2010–2017 on transatlantic routings with a stopover in large European hub airports. Competition from low-cost airlines on the short segments of transatlantic routings reduces the number of connecting passengers channeled by hubbing airlines, who have to pay higher fares. In contrast, high-speed rail services are not found to lead to a reduction in the number of connecting passengers or to an increase in the price they have to pay.

Pricing by international airline alliances: A retrospective study

This study provides further empirical evidence on pricing by international airline alliances. The paper covers a long sample period, which runs from 1997 to 2016, and it supplements the usual USDOT fare data with confidential fare data reported by the foreign alliance partners of US carriers. The empirical results for connecting service match earlier findings, with alliances charging lower fares than nonaligned carriers. In contrast to almost all previous studies, the gateway-to-gateway results imply that, in the latter part of the sample period, granting antitrust immunity to two previously nonaligned carriers is equivalent to removing a competitor, with a consequent increase in fares. However, a simulation based on the results shows that this anticompetitive effect is more than offset by gains to connecting passengers, making alliances beneficial on balance.

Pricing by International Airline Alliances: A Retrospective Study Using Supplementary Foreign-Carrier Fare Data

This study provides further empirical evidence on pricing by international airline alliances. The paper covers a long sample period, which runs from 1997 to 2016, and it supplements the usual USDOT fare data with confidential fare data reported by the foreign alliance partners of US carriers. The empirical results for connecting service match earlier findings, with alliances charging lower fares than nonaligned carriers. The GTG results imply that, in the latter part of the sample period, granting antitrust immunity to two previously nonaligned carriers is equivalent to removing a competitor, with a consequent increase in fares (an effect seldom seen in previous work).

An Approach to Distinguish Destination from the Alighting Stop based on Fare Data

An Approach to Distinguish Destination from the Alighting Stop based on Fare Data

Public transport trip purpose inference using smart card fare data

Although smart card fare data has recently become more prevalent as a rich, comprehensive and continuous source of information, there is still some missing information which inhibits its capability in the research field. One key missing piece of information is the passengers’ trip purpose. This paper investigates the potential of the smart card data to infer passengers’ trip purpose, thereby reducing the use of the expensive and time-consuming Household Travel Surveys (HTS). On this basis, an improved model has been proposed, calibrated and validated for trip purpose inference by integrating different data sources, namely: HTS, a land use database, the South East Queensland Strategic Transport Model (SEQSTM), the General Transit Feed Specification (GTFS) data, O-D survey data, and most importantly the unique smart card fare data from Brisbane, Queensland. As smart card fare data does not record passengers’ trip purpose, the calibration and validation procedures are performed on HTS data. Based on the validation results, the proposed methodology shows a strong capability to predict trip purpose at a high level of accuracy. The results show an overall 67% correct inference after applying spatial attributes, but the correct inference increases to 78% after applying temporal attributes. Different trip purposes show different sensitivities to the applied spatial and temporal attributes. Work and home trips have the highest correct inference results, at 92% and 96%, respectively. Furthermore, the results of correct inference for shopping and education trips improved after applying the temporal attributes.

Could market power explain hub premiums?

Hub carriers tend to charge higher fares to their passengers at their hubs. The additional charge is called the hub premium. Different studies have discussed the possible causes of the hub premium. Using the fare data at Hong Kong International Airport, this paper aims at empirically examining the factors affecting the hub premium for both economy and business class markets. The random effect models are employed in the analysis. Consistent with the theoretical results in the literature, airport market concentration, market power, flight frequency and distance are identified as significant factors that affect the hub premium in Hong Kong. In addition, this paper further investigates the impact of strategic alliance and the acquisition of Dragonair by Cathay Pacific on the hub premium.

Are Passengers Compensated for Incurring an Airport Layover? Estimating the Value of Layover Time in the U.S. Airline Industry

American, Delta, and United have organized their operations into extensive hub-and-spoke networks that typically require passengers originating and concluding travel in non-hub cities to board a connecting flight at a hub en route to the final destination. From the passenger perspective, layovers are detrimental since the addition to total travel time relative to a nonstop itinerary is a cost incurred by the passenger. An airline is able to reduce a passenger's layover time by narrowing the gap between flights at the connecting airport. However, narrowing this flight gap has the adverse effect of increasing airport congestion. Taking these perspectives into account, it is clear that layover time influences a prospective passenger's purchasing decision and an airline's flight scheduling decision. Using published fare and itinerary data from Google Flights, this paper provides insight into both decisions by providing empirical estimates on the value of layover time in the U.S. airline industry. This paper finds that passengers are compensated with a fare that is $42.74-$47.60 cheaper per hour of layover time. Of the three dominant legacy carriers, United passengers are found to be compensated at an even higher rate of $61.89 per hour.

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806m using the original algorithm to 530m after applying the suggested improvements.

Reducing Subway Crowding: Analysis of an Off-Peak Discount Experiment in Hong Kong

Increases in ridership are outpacing capacity expansions in several transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system and get more out of their present capacity. This paper uses Hong Kong’s Mass Transit Railway (MTR) system as a case study to explore the effects of crowding reduction strategies and how to use fare data to support these measures. The MTR system introduced a discount in September 2014 to encourage users to travel before the peak and reduce onboard crowding. To understand the impacts of this intervention, first, existing congestion patterns were reviewed and a clustering analysis was used to reveal typical travel patterns among users. Then, changes to users’ departure times were studied at three levels to evaluate the promotion’s effects. Patterns of all users were measured across both the whole system and for specific rail segments. The travel patterns of the user groups, who have more homogeneous usage characteristics, were also evaluated and revealed groups who had differing responses to the promotion. The incentive was found to have affected morning travel, particularly at the beginning of the peak hour period and among users with commuter-like behavior. Aggregate and group-specific elasticities were developed to inform future promotions and the results were also used to suggest other potential incentive designs.

EasyJet pricing strategy: determinants and developments

We analysed the pricing policy of easyJet, the second largest low-cost carrier in Europe, using fare data for all easyJet flights from 2007 to 2012. For each flight, we determined the average fare per kilometre and advance booking discounts (ABDs) within a 90-day period before departure. By applying a panel methodology, we show that average fares tended to be lower, with larger discounts for advance booking, when easyJet dominated departure or arrival airports. Direct and indirect competition significantly reduced average fares and increased discounts for advance booking. Fares increased and ABDs decreased with easyJet's experience with specific routes. Other things being equal, the analysis also showed that easyJet reduced the average discount for advance booking during the period.

GEOGRAPHIC CORRELATES OF LOWEST AVAILABLE AIRFARES ON AUSTRALIAN AIR ROUTES

Deregulation of the airline industry in Australia has produced lower airfares. However this perspective rarely incorporates spatial insights and usually utilizes archival data. The purpose of this paper is to conduct a first-stage confirmatory analysis of up-to-date airfares charged on 24 major routes within Australia using Skyscanner, a web-based and consumer-oriented tool to access airfares. This tool displays fares during an on-line booking process prior to purchase, just as consumers would experience it. We apply Skyscanner to extract one set of current fares -- lowest fare data -- on the routes and then use linear modelling to establish variables that can be utilized to predict these lowest fare prices. While far from a full accounting of the cost of Australian air services, this test of Skyscanner as a data source, along with the successful confirmatory linear analysis, shows that the underlying configuration of the nation’s urban population, distance, direct connections, and characteristics of links and networks of low cost carriers are powerful influences upon prices charged. We suggest that Skyscanner and similar data sources may provide researchers with alternative low cost data that may shed insight into many air transport pricing questions.

Monopoly Rents in Motorcycle Taxi Services in Bangkok, Thailand

This paper examines the market for motorcycle taxi services in Bangkok, Thailand, in terms of economic rent and the effects of such rent on fares. Three sources of monopoly rent in motorcycle taxi services are identified. Locational rent is attributed to the level of accessibility that is specific to the location of a motorcycle taxi stand. Such locations are limited in terms of accessibility, with few locations that can be substituted with another. The quota rent is arbitrarily created by rent-seeking activities, that is, by setting a quota on the number of operators in each location. Territorial rent is created by restricting the service area for each operator group. Depending on the behavior of the so-called influential people, the monopoly can be either enforced or eroded. A regression analysis of fare data provides evidence that supports the hypothesis of locational and territorial rents. Locational rent decreases as a motorcycle taxi stand is located farther away from a rail transit station. Motorcycle taxi groups with demarcated territories command a fare premium, which may indicate territorial rent. Some stands enjoy a degree of monopoly where there are limited substitute feeder transport modes; stands located near bus stops also command a fare premium because of accessibility. This situation implies that bus services do not compete with motorcycle taxis but provide complementary services. The additional economic rent created by new transit systems is unfairly captured by operator groups and influential people. It is suggested that a competitive market be created for motorcycle taxi services, with value-capture measures that are intended to create a more efficient and fairer feeder transport system.

Use of Smart Card Fare Data to Estimate Public Transport Origin–Destination Matrix

Over the past few years, several techniques have been developed for using smart card fare data to estimate origin–destination (O-D) matrices for public transport. In the past, different walking distance and allowable transfer time assumptions had been applied because of a lack of information about the alighting stop for a trip. Such assumptions can significantly affect the accuracy of the estimated O-D matrices. Little evidence demonstrates the accuracy of O-D pairs estimated with smart card fare data. Unique smart card fare data from Brisbane, Queensland, Australia, offered an opportunity to assess previous methods and their assumptions. South East Queensland data were used to study the effects of different assumptions on estimated O-D matrices and to conduct a sensitivity analysis for different parameters. In addition, an algorithm was proposed for generating an O-D matrix from individual user transactions (trip legs). About 85% of the transfer time was non-walking time (wait and short activity time). More than 90% of passengers walked less than 10 min to transfer between alighting and the next boarding stop; this time represented about 10% of the allowable transfer time. A change in the assumed allowable transfer time from 15 to 90 min had a minor effect on the estimated O-D matrices. Most passengers returned to within 800 m of their first origin on the same day.

Estimating the Destination of Unlinked Trips in Transit Smart Card Fare Data

Smart card automated fare collection systems have been effective for the collection of data about the travel behavior of users on public transit networks. Because some systems record only the boarding (origin) locations, a method is needed for estimating the alighting (destination) locations. Existing algorithms can estimate the destination for most trips. However, unlinked trips, which are not part of a trip chain during the day, are more difficult to analyze. The proposed improvement to the existing model for destination estimation, especially for unlinked trips, is based on kernel density estimation of the spatial and temporal probabilities of each destination. The Société de Transport de l'Outaouais, a medium-sized bus service near Ottawa, Ontario, Canada, provided data for a 1-month period in 2009 (908,303 total transactions). Existing algorithms can handle only 80.64% of the trips; the proposed method handles an additional 10.9%. These results are analyzed, and future research directions are discussed.

Estimativa de matriz origem/destino utilizando dados do sistema de bilhetagem eletrônica: proposta metodológica

The characterization of demand is fundamental in the development of transport networks. There are numerous methods for the demand analysis, and the conventional methods have high costs and time of implementation, that restrict the frequency of its application. In order to mitigate the weaknesses of conventional methods of transport surveys, this paper presents a methodology for estimation of an Origin-Destination Matrix for public bus transport systems using information from the Automatic Fare Collection and from Geographic Information Systems (GIS) database of buses lines. This methodology consists in create the transport network in GIS, the characterization and processing of fare data for determining the seed matrix and procedures to estimate the final matrix. The methodology was tested for the municipal transportation system of Maceió/AL. The results were consistent and indicate that the proposed methodology allows incorporating the changes in the transport system. Moreover, the application of the methodology indicated its simplicity, convenience and practicality, and can be used for studies related to public transportation.

Low Cost Carriers and the Evolution of the Domestic U.S. Airline Industry

The article studies the evolution of the domestic U.S. airline industry from 1995 to 2009 using T-100 traffic data and DB1B fare data from the U.S. Department of Transportation. Unlike previous studies, a consistent split of the analysis in network carriers and low cost carriers is introduced. Based on a differentiation in market size and major players, concentration, fares, service, costs and profits, we find that the competitive interaction between network carriers and low cost carriers increased substantially throughout the last decade and must be considered as the main driver of competition in the domestic U.S. airline industry.

Proposition of a mathematical model for selecting possible low-cost airlines routes

This paper examines the effects that low-cost carriers (LCC's) produce when entering new routes operated only by full-service carriers (FSC's) and routes operated by low-cost carriers in competition with full-service carriers. A mathematical model has been developed to determine what routes should be operated by a low-cost carrier with better possibilities to subsist, as a first step towards reaching the most convenient route; additional factors must be considered after running the proposed model, such as route passenger demand and aircraft characteristics not took into account in this paper. The proposed model was set up by analyzing The United States domestic air transport market. Distance is the only variable taken into account by the model. This model analyses the relation between the real fare data ($) and the distance (miles). The model generates three lines that includes amongst them 68% of the approximately 18,000 routes by calculating a standard deviation and estimates the minimum, maximum and average fare for a low-cost carrier given the distance the model determines in which routes a low-cost carrier could be successful by comparing, route by route, different airline fares against the low-cost minimum, maximum and average fare estimated per distance.