The federal republic of Germany: the federal ministry of transport

The continued liberalization of the airline market in the European Community offers the prospect of competition in terms of price, frequency of service and many other attributes. Understanding these factors that influence passenger demand and incorporating them into a demand function poses a substantial theoretical and empirical challenge. A basis for estimating demand functions is the logit model. This paper examines the value of a logit share model in explaining how the share of airlines between airports varies as a function of measured and unmeasured attributes. It shows how demand functions are necessary for understanding a competitive market and evaluates the progress to date in identifying accurate coefficients of the logit function.     

Integrating congestion pricing,transit subsidies and mode choice

We model and analyze optimal (welfare maximizing) prices and design of transport services in a bimodal context. Car congestion and transit design are simultaneously introduced and consumers choose based on the full price they perceive. The optimization variables are the congestion toll, the transit fare (and hence the level of subsidies) and transit frequency. We obtain six main results: (i) the optimal car-transit split is generally different from the total cost minimizing one; (ii) optimal congestion and transit price are interdependent and have an optimal frequency attached; (iii) the optimal money price difference together with the optimal frequency yield the optimal modal split; (iv) if this modal split is used in traditional stand-alone formulations – where each mode is priced independently–resulting congestion tolls and transit subsidies and fares are consistent with the optimal money price difference; (v) self-financing of the transport sector is feasible; and (vi) investment in car infrastructure induces an increase in generalized cost for all public transport users.     

Analyzing high speed rail passengers’ train choices based on new online booking data in China

This study explores two nonparametric machine learning methods, namely support vector regression (SVR) and artificial neural networks (ANN), for understanding and predicting high-speed rail (HSR) travelers’ choices of ticket purchase timings, train types, and travel classes, using ticket sales data. In the train choice literature, discrete choice analysis is the predominant approach and many variants of logit models have been developed. Alternatively, emerging travel choice studies adopt non-utility-based methods, especially nonparametric machine learning methods including SVR and ANN, because (1) those methods do not rely on assumptions on the relations between choices and explanatory variables or any prior knowledge of the underlying relations; (2) they have superb capabilities of iteratively identifying patterns and extracting rules from data. This paper thus contributes to the HSR train choice literature by applying and comparing SVR and ANN with a real-world case study of the Shanghai-Beijing HSR market in China. A new normalized metric capturing both the load factor and the booking lead time is proposed as the target variable and several train service attributes, such as day of week, departure time, travel time, fare, are identified as input variables. Computational results demonstrate that both SVR and ANN can predict the train choice behavior with high accuracy, outperforming the linear regression approach. Potential applications of this study, such as rail pricing reform, have also been identified.     

An aggregate demand model for air passenger traffic in the hub-and-spoke network

In this paper, we build an aggregate demand model for air passenger traffic in a hub-and-spoke network. This model considers the roles of airline service variables such as service frequency, aircraft size, ticket price, flight distance, and number of spokes in the network. It also takes into account the influence of local passengers and social-economic and demographic conditions in the spoke and hub metropolitan areas. The hub airport capacity, which has a significant impact on service quality in the hub airport and in the whole hub-and-spoke network, is also taken into consideration.Our demand model reveals that airlines can attract more connecting passengers in a hub-and-spoke network by increasing service frequency than by increasing aircraft size in the same percentage. Our research confirms the importance of local service to connecting passengers, and finds that, interestingly, airlines’ services in the first flight leg are more important to attract passengers than those in the second flight segment. Based on data in this study, we also find that a 1% reduction of ticket price will bring about 0.9% more connecting passengers, and a 1% increase of airport acceptance rate can bring about 0.35% more connecting passengers in the network, with all else equal. These findings are helpful for airlines to understand the effects of changing their services, and also useful for us to quantify the benefits of hub airport expansion projects.At the end of this paper, we give an example as an application to demonstrate how the developed demand model could be used to valuate passengers’ direct benefit from airport capacity expansion.     

Measuring unobserved prices using the structural time-series model: The case of cycling

This study presents a means of determining a historic (generalised cost based) price index for cycling in the UK for the period 1949–2006 using annual demand data. By specifying demand as a function of generalised price and income and then applying a structural time-series model to elucidate the unobserved component of prices (while controlling for observed income levels), it is illustrated that the role of prices in influencing demand is non-trivial. Over the sample period price responses generally influence demand for cycling to a greater extent than income effects.     

Asymmetry in transport fuel demand: Evidence from household level data

Models for gasoline demand for transportation activities generally assume that demand is perfectly reversible with respect to gasoline price (and income). The small literature which relaxes the reversibility assumption in gasoline demand argues technological fixation leads to this asymmetry and utilizes aggregate time-series model to find evidence in favour of asymmetry. In this research it is suggested that there could also be behavioural factors behind this asymmetric response, possibly due to the loss aversion nature of human beings as described in the prospect theory. For the first time, household level data was used to understand asymmetry in gasoline demand in response to changes in gasoline price and income. There was statistical evidence that gasoline price and income both can induce asymmetric changes in gasoline demand among households. Specifically, elasticity with respect to rising prices and falling income is larger than the elasticity with respect to falling prices and rising income respectively, which is consistent with loss aversion in gasoline purchase behaviour. There was also some evidence of heterogeneity in the asymmetric responses between urban and rural households. The results have implications for transport-related energy tax policies or subsidies, while the method can be applied directly for non-energy goods as well.     

Optimizing sustainable feeder bus operation considering realistic networks and heterogeneous demand

A mathematical model is developed to optimize social and fiscal sustainable operation of a feeder bus system considering realistic network and heterogeneous demand. The objective total profit is a nonlinear, mixed integer function, which is maximized by optimizing the number of stops, headway, and fare. The stops are located which maximize the ridership. The demand elasticity for the bus service is dependent on passengers' access distance, wait time, in‐vehicle time, and fare. An optimization algorithm is developed to search for the optimal solution that maximizes the profit. The modeling approach is applied to planning a bus transit system within Woodbridge, New Jersey. Copyright © 2011 John Wiley & Sons, Ltd.     

Air transport and high-speed rail competition: Environmental implications and mitigation strategies

We build a duopoly model to shed light on the environmental impact of HSR-air transport competition, capturing the effects of induced demand, schedule frequency and HSR speed. The net environmental effect can be negative since there is a the trade-off between the substitution effect – how many passengers using the HSR are shifted from air transport – and the traffic generation effect – how much new demand is generated by the HSR. We conduct a simulation study based on the London-Paris market where HSR has served 70% of the market. The introduction of HSR is detrimental to LAP, while it is beneficial to GHG emissions. HSR entry increases neither LAP nor GHG emissions when the ratio between HSR and air transport emissions is relatively low. Moreover, competition is more likely to be detrimental to the environment when the weight of the social welfare in HSR objective function is high. Since the magnitude of the environmental friendliness of HSR compared to air transport hinges on the mix of energy sources used to generate the electricity (which is heavily constrained by the country in which HSR operates), regulators should assess the implications of HSR entry taking into account the energy policy and mitigation strategies available to transport modes.     

Travel itinerary problem

In this study, we propose a travel itinerary problem (TIP) which aims to find itineraries with the lowest cost for travelers visiting multiple cities, under the constraints of time horizon, stop times at cities and transport alternatives with fixed departure times, arrival times, and ticket prices. First, we formulate the TIP into a 0–1 integer programming model. Then, we decompose the itinerary optimization into a macroscopic tour (i.e., visiting sequence between cities) selection process and a microscopic number (i.e., flight number, train number for each piece of movement) selection process, and use an implicit enumeration algorithm to solve the optimal combination of tour and numbers. By integrating the itinerary optimization approach and Web crawler technology, we develop a smart travel system that is able to capture online transport data and recommend the optimal itinerary that satisfies travelers’ preferences in departure time, arrival time, cabin class, and transport mode. Finally, we present case studies based on real-life transport data to illustrate the usefulness of itinerary optimization for minimizing travel cost, the computational efficiency of the implicit enumeration algorithm, and the feasibility of the smart travel system.     

Multi-fleet ferry service network design with passenger preferences for differential services

This paper investigates a multi-fleet ferry routing and scheduling problem that takes into account ferry services with different operation characteristics and passengers with different preferred arrival time windows. The logit model is used to represent passengers’ service choices. The full problem is formulated as a mixed integer nonlinear programming problem and solved with a heuristic procedure that first fixes the demand and then decomposes the resultant model by ferry services. At each iteration of the algorithm, the demand is updated and the relaxed problem is re-solved. Numerical results for the case of ferry service network design in Hong Kong are provided to illustrate the properties of the model and the performance of the heuristic.     

Modeling traveler mode choice behavior of a new high-speed rail corridor in China

This study examines mode choice behavior for intercity business and personal/recreational trips. It uses multinomial logit and nested logit methods to analyze revealed preference data provided by travelers along the Yong-Tai-Wen multimodal corridor in Zhejiang, China. Income levels are found to be positively correlated with mode share increases for high-speed rail (HSR), expressway-based bus, and auto modes, while travel time and trip costs are negatively correlated with modal shift. Longer distance trips trigger modal shifts to HSR services but prevent modal shift to expressway-based auto use due to escalation of fuel cost and toll charges. Travelers are less elastic in their travel time and cost for trips by nonexpressway-based auto use modes. The magnitude of elasticity for travel time is higher than trip costs for business trips and lower for personal/recreational trips. The study provides some policy suggestions for transportation planners and decision-makers.     

Optimization of bus stop locations for improving transit accessibility

A mathematical model is developed in this paper to improve the accessibility of a bus service. To formulate the optimization model, a segment of a bus route is given, on which a number of demand entry points are distributed realistically. The objective total cost function (i.e. the sum of supplier and user costs) is minimized by optimizing the number and locations of stops, subject to non‐additive users' value of time. A numerical example is designed to demonstrate the effectiveness of the method thus developed to optimize the bus stop location problem. The sensitivity of the total cost to various parameters (e.g. value of users' time, access speed, and demand density) and the effect of the parameters on the optimal stop locations are analyzed and discussed.     

Cost and time damping: evidence from aggregate rail direct demand models

There is a significant body of evidence from both disaggregate choice modelling literature and practical travel demand forecasting that the responsiveness to cost and possibly to time diminishes with journey length. This has, in Britain at least, been termed ‘Cost Damping’, and is recognised in guidance issued by the UK Department for Transport. However, the consistency of the effect across modes and data types has not been established. Cost damping, if it exists, affects both the forecasting of demand and our understanding of behaviour. This paper aims to investigate the evidence for cost and time damping in rail demand using aggregate rail ticket sales data. The rail ticket sales data in Britain has, for many years, formed the basis of analysis of a wide range of impacts of rail demand. It records the number of tickets sold between station pairs, and it is generally felt to provide a reasonably accurate reflection of travel demand. However, the consistency of these direct demand models with choice modelling and highway demand model structures has not been investigated. Rail direct demand models estimated by ticket sales data indicate only slight variation in the fare elasticity with distance, as is evidenced in the largest meta-analysis of price elasticities conducted to date (Wardman in J Transp Econ Policy 48(3):367–384, 2014). This study of UK elasticities shows strong variation between urban and inter-urban trips, presumably a segmentation at least in part by purpose, but less remaining variation by trip length. A lack of variation by length supports the hypothesis of cost damping, because constant cost sensitivity would imply that fare elasticity would increase strongly with distance, because of the increasing impact of higher fares at longer distances. In this paper we indicate that rail direct demand models have some consistency of behavioural paradigm with utility based choice models used in highway planning. We go on to use rail demand data to estimate time and fare elasticities in the context of various cost damping functions. Our empirical contribution is to estimate time elasticities on a basis directly comparable with cost elasticities and to show that the phenomenon of cost damping is strongly present in ticket sales data. This finding implies that cost damping should be included in models intended for multimodal analysis, which may otherwise give incorrect predictions.     

Imperfect reversibility of air transport demand: Effects of air fare,fuel prices and price transmission

There are recent evidence that air transport demand may not have a perfectly reversible relationship with income and jet fuel prices, as is assumed in most demand models. However, it is not known if the imperfectly reversible effects of jet fuel price are a result of asymmetries in the supply side, i.e., asymmetries in cost pass through from fuel prices to air fare, or of demand side behavioral asymmetries whereby people value gains and losses differently. This paper uses US time series data and decomposes air fare and fuel price into three component series to develop an econometric model of air transport demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find that air transport demand shows asymmetry with respect to air fare, indicating potential imperfect reversibility in consumer behavior. We also find evidence of asymmetry and hysteresis in cost pass-through from jet fuel prices to air fare, showing rapid increases in airfare when fuel prices increases but a slower response in the opposite direction.     

The European freight railway system as a hub-and-spoke network

This paper addresses a hub-and-spoke network problem for railroad freight, where a central planner is to find transport routes, frequency of service, length of trains to be used, and transportation volume. Hub-and-spoke networks, often found in air freight, have not been favoured by railways in the past. Such a structure could be profitable, however, if there exist concentrated freight flows on some service links. We formulate a linear integer programming model whose objective function includes not only the typical operational cost, but also cost due to the transit time spent by freight in the network. We then develop heuristic algorithms to solve large scale instances occurring in rail freight systems in France plus Italy; Germany; and a 10-country European network. By assuming that every node is equipped with consolidation capability, we let the final solution naturally reveal potential hub locations, the impact of several of which is studied by sensitivity analysis.     

High speed rail passenger segmentation and ticketing channel preference

A multi-channel strategy for the purposes of marketing and ticketing has been widely employed in various fields, including the transportation industry, yet few transportation studies have investigated ticket channel-related issues. This study thus adopts Taiwan High Speed Rail (THSR) as a case study to identify passengers’ perceptions regarding key factors that affect the channel preference through which they receive their services, particularly across a four-stage ticket purchasing process, including information enquiry, booking, payment, and ticket pick-up. To investigate these key factors, we base the framework of our research model upon customers’ perceived value perspective, adopting a multinomial logit model to examine the influence of channel attributes on HSR passengers’ preferences. The relationship among the decisions made by these passengers at each ticket purchasing stage is also examined. The findings of our study demonstrate that perceived risk, perceived benefit, and perceived ease of use are critical factors influencing passengers’ channel preference throughout the ticket-purchasing process. Perceptual differences are proven to exist due to various demographic factors and trip characteristics. The conclusions of this study have implications for THSR management to design appropriate ticketing channel services for certain types of passengers and can also be generally applied to multiple distribution channels in conventional railway system and intercity bus services.     

Modeling a rail transit alignment considering different objectives

An optimization model for station locations for an on-ground rail transit line is developed using different objective functions of demand and cost as both influence the planning of a rail transit alignment. A microscopic analysis is performed to develop a rail transit alignment in a given corridor considering a many-to-one travel demand pattern. A variable demand case is considered as it replicates a realistic scenario for planning a rail transit line. A Genetic Algorithm (GA) based on a Geographical Information System (GIS) database is developed to optimize the station locations for a rail transit alignment. The first objective is to minimize the total system cost per person, which is a function of user cost, operator cost, and location cost. The second objective is to maximize the ridership or the service coverage of the rail transit alignment. The user cost per person is minimized separately as the third objective because the user cost is one of the most important decision-making factors for planning a transit system from the users’ perspective. A transit planner can make an informed decision between various alternatives based on the results obtained using different objective functions. The model is applied in a case study in the Washington, DC area. The optimal locations and sequence of stations obtained using the three objective functions are presented and a comparative study between the results obtained is shown in the paper. In future works we will develop a combinatorial optimization problem using the aforementioned objectives for the rail transit alignment planning and design problem.     

The influence of quality and price on the demand for urban transport: the case of university students

The aim of this work is to estimate a transport demand function for university students in the Bilbao area and to obtain the main variables that condition this demand. To do this, we use a nested logit model. First, we estimate price and time elasticities, and second the potential effects of changing the available supply of public transport in order to draw new collective transport users away from private vehicles.     

Estimation of rail passenger flow and system utilization with ticket transaction and gate data

Capturing the dynamics in passenger flow and system utilization over time and space is extremely important for railway operators. Previous studies usually estimated passenger flow using automatic fare collection data, and their applications are limited to a single stopping pattern and/or a single type of ticket. However, the conventional railway in Taiwan provides four types of ticket and five types of train service with a number of stopping patterns. This study develops a comprehensive framework and corresponding algorithms to map passenger flow and evaluate system utilization. A multinomial logit model is constructed and incorporated in the algorithms to estimate passenger train selection behavior. Results from the empirical studies demonstrate that the developed framework and algorithms can successfully match passengers with train services. With this tool, operators can efficiently examine passenger flow and service utilization, thereby quickly adjusting their service strategies accordingly to improve system performance.