Discrete Choice Models for Revenue Management

In the transportation field, the shift of airline and railway industries toward web-based distribution channels has provided passengers with better access to fare information. This has resulted in passengers becoming more strategic to price. Therefore, a better understanding of passenger choice behavior is required in order to support fare strategies. Methods based on discrete choice (DC) analysis have recently been introduced in revenue management (RM). However, applications of DC models in railway ticket pricing are limited and heterogeneity in choice behavior across different categories of travelers has mostly been ignored. Differences in individual taste are crucial for the RM sector. Additionally, strategic passenger behavior is significant, especially in markets with flexible refund and exchange policy, where ticket cancellation and exchange behavior has been recognized as having major impacts on revenues. This dissertation examines innovative approaches in discrete choice modeling to support RM systems for intercity passenger railway. The analysis, based on ticket reservation data, contributes to the existing literature in three main aspects. Firstly, this dissertation develops choice models of ticket purchase timing which account for heterogeneity across different categories of passengers. The methodology based on latent class (LC) and mixed logit (ML) model framework offers an alternative approach to demand segmentation without using trip purposes which are not available in the data set used for the analysis. Secondly, this dissertation develops RM optimization models which use parameters estimated from the choice models and demand functions as key inputs to represent passenger response to RM policy. The approach distinguishes between leisure and business travelers, depending on departure time and day of week. The formulated optimization problem maximizes ticket revenue by simultaneously solving for ticket pricing and seat allocation. Strategies are subjected to capacity constraints determined on the basis of the railway network characteristics. Finally, this dissertation develops ticket cancellation and exchange model using dynamic discrete choice model (DDCM) framework. The estimated model predicts the timing of ticket cancellations and exchanges in response to trip schedule uncertainty, fare, and refund/exchange policy of the railway service. The model is able to predict new departure times of the exchanged tickets and covers the full range of departure time alternatives offered by the railway company

Dynamic pricing under customer choice behavior for revenue management in passenger railway networks

Revenue management (RM) for passenger railway is a small but active research field with an increasing attention during the past years. However, a detailed look into existing research shows that most of the current models in theory rely on traditional RM techniques and that advanced models are rare. This thesis aims to close the gap by proposing a state-of-the-art passenger railway pricing model that covers the most important properties from practice, with a special focus on the German railway network and long-distance rail company Deutsche Bahn Fernverkehr (DB). The new model has multiple advantages over DB’s current RM system. Particularly, it uses a choice-based demand function rather than a traditional independent demand model, is formulated as a network model instead of the current leg-based approach and finally optimizes prices on a continuous level instead of controlling booking classes. Since each itinerary in the network is considered by multiple heterogeneous customer segments (e.g., differentiated by travel purpose, desired departure time) a discrete mixed multinomial logit model (MMNL) is applied to represent demand. Compared to alternative choice models such as the multinomial logit model (MNL) or the nested logit model (NL), the MMNL is significantly less considered in pricing research. Furthermore, since the resulting deterministic multi-product multi-resource dynamic pricing model under the MMNL turns out to be non- linear non-convex, an open question is still how to obtain a globally optimal solution. To narrow this gap, this thesis provides multiple approaches that make it able to derive a solution close to the global optimum. For medium-sized networks, a mixed-integer programming approach is proposed that determines an upper bound close to the global optimum of the original model (gap < 1.5%). For large-scale networks, a heuristic approach is presented that significantly decreases the solution time (by factor up to 56) and derives a good solution for an application in practice. Based on these findings, the model and heuristic are extended to fit further price constraints from railway practice and are tested in an extensive simulation study. The results show that the new pricing approach outperforms both benchmark RM policies (i.e., DB’s existing model and EMSR-b) with a revenue improvement of approx. +13-15% over DB’s existing approach under a realistic demand scenario. Finally, to prepare data for large-scale railway networks, an algorithm is presented that automatically derives a large proportion of necessary data to solve choice-based network RM models. This includes, e.g., the set of all meaningful itineraries (incl. transfers) and resources in a network, the corresponding resource consumption and product attribute values such as travel time or number of transfers. All taken together, the goal of this thesis is to give a broad picture about choice-based dynamic pricing for passenger railway networks

Demand forecasting in a railway revenue management system

The research in Revenue Management has tightly focused on airline markets and somewhat neglected other similar markets. The purpose of this thesis is to offer an extensive overview on RM in the railway context. The backgrounds and concepts of RM are discussed and the applicability of RM in railway markets is evaluated. The differences between railways and airlines are also explored. I am especially focused on the demand forecasting process and different methods that can be used to forecast uncertain demand for a specific train. I also discuss how demand forecasting relates to other RM components, such as capacity allocation. Relevant RM theories and demand forecasting methods are compiled based on the existing literature. Because of the limited availability of real demand data, I use hypothetical demand data to illustrate how different forecasting methods can be applied and how the performance of each method can be evaluated. I also compile an illustrative capacity allocation example using EMSR -model. I conclude that the applicability of RM in railway markets is evident. I find four significant differences between railways and airlines that are relevant to RM. Railways tend to have more complex networks, less price differentiation, shorter booking lead times, and less competitive markets. Illustrative demand forecasting examples indicate that the evaluation of different forecasting methods is essential, since the performance of different methods might vary substantially, depending on the available data and the time horizon of desired forecast. Capacity allocation examples suggest that it is particularly important that demand forecasts would provide the accurate predictions of total demand and demand distribution between fare classes. However, it should be taken into account that the findings of illustrative examples cannot be generalized, since the hypothetical data was used in the analysis. Thus further examination with real demand data should be required. Additionally, the issues of constrained data and network effect are omitted from the analysis

Modus D3.1 Modal choice analysis and expert assessment

Modus Deliverable 3.1 has the objective to identify and assess (future) drivers that influence passenger demand and supply of mobility, and how these affect passenger modal choice. A comprehensive literature review is provided and identifies a set of high-level and detailed drivers of supply and demand. This analysis is complemented by an expert survey, to gain initial high-level insights regarding the potential importance of various factors, and by a multimodality workshop, to identify additional factors and acquire a first insight into potential enablers and barriers of future mobility solutions. Combining all the identified drivers reveals that most drivers are of a social, economic or technological nature. A large number of social drivers are demand drivers concerned with the passenger aspects of mobility. On the other hand, a large number of economic drivers belong to the supply drivers concerned with various cost-related factors or with transport operations, the market structure and available infrastructure

Informedness and Customer-Centric Revenue management

The recent pervasive adoption of modern IT in the marketplace has profoundly changed information availability to customers and firms. This improved information endowment results in changes in consumer behavior and corporate strategy. This dissertation proposes new theoretical perspectives – firm informedness, customer informedness, and informedness through learning – to re-conceptualize the decision making process of customer-centric revenue management. It consists of three studies. First, using multiple cases in which firms adopt smart cards and mobile technologies in America, Europe, and Asia, we examine the value creation process of the firm using the explanation of firm informedness and investigate how it advances revenue management. Second, we test the theory of consumer informedness and examine heterogeneity in consumer preferences using stated choice experiments. We find the evidence for trading down and trading out behavior and show that the use of mobile ticketing technologies can help firms to build a hyper-differentiated transport market. Finally, using a computational simulation, we explore the opportunity for devising service offerings to capture profitable consumer responses, considering demand-driven revenue and capacity-management. Overall, this research introduces methods, models, and guidelines for organizations to strategize the informational challenge, make informed decisions, and create transformational values to win in today’s competitive network environment

Quantifying the Effects of Sustainable Urban Mobility Plans

This technical note uses the expert scoring information available in current scientific literature in order to explore the impacts and effects that different urban measures may have in planning for sustainability on a European wide level.JRC.J.1-Economics of Climate Change, Energy and Transpor

Information systems failure, politics and the sociology of translation : the problematic introduction of an American computerised reservation system and yield management at French Railways

This in-depth cases tudy examinest he troubled introduction of a new computerisedr eservationsystem at French Railways. Socrale, based on the American Airlines Sabre system, had a disastrousbeginning.I t wasb adly receivedb y the Frenchp ublic, led to strikes andg overnmentin quiries,a nd had tobe modified substantially.T he literatureo n information systemsf ailure is reviewedf rom functionalistt osocial constructivista nd critical perspectivesa nd the thesis aims to challengeb eliefs and assumptionsabout technological success and failure. The notion of 'symmetry' from the sociology of technologyemphasisetsh at failures expresst he samed ynamicsa s successess,h owingh ow technologicalc hoicesa renot obvious or unproblematic.Differences between air and rail transporý between American and European transportderegulation and between the needs of national identity, regional development and public access totransporta re all reflectedi n the questiono f yield managementY. ield managemenist a crucial componentof computerisedre servations ystemsa nd was first adoptedd uring the deregulationo f the US air transportindustry in the early 80s. It requires complex optimisation software designed to manage passengerrevenues and control demand, by manipulating the availability of full and discounted fares according tomonitoredd emanda nds tatisticaal nalysis.Latour and Callon's sociology of 'translation' helps analyse how the Socrate project wasundertaken and interpreted as: borrowing from airline pricing, aiming to gain competitive advantage,associatingS ocrate to the successo f high-speedt rains, attemptingt o changep assengersb' uying andtravelling behaviour, transformingt he organisationa nd helping identify profitable market segmentsA.non-essentialisst tanceh elpsu nderstandh ow social and technicald istinctionsa re socially constructeda ndhow the differentiation between what is technical and what is social, for instance in the conception andapplication of yield managementi,s a mattero f power and politics. Clegg's circuits of power are usedt ocomplement the sociology of translation in examining how power and political factors contribute toinformation systemsb ecoming( or not) obligatoryp assagep oints.Politically controversialc hangesin Frenchr ail transporta re associatedw ith the role of computertechnology in deregulated European and global electronic markets and its effects on the concept ofnationali dentity and sovereigntyin transportp olicy-making