Paul Koster

A Rank-dependent Scheduling Model

This discussion paper resulted in a publication in the Journal of Transport Economics and Policy (2012), vol. 46(1), pages 123-138. This paper proposes an analytical framework for scheduling decisions of road travelers that takes into account probability weighting using rank dependent utility theory. The fundamental difference with the standard scheduling model based on expected utility is that the probabilities of arrivals are treated in a non-linear way. This paper shows how scheduling decisions are affected by the weighted probabilities of the traveler. We derive the costs of non-optimal chosen departure times because of probability weighting and show that if the parameterized probability weighting function issimilar to what has been found for gambling, the costs of probability weighting for morningpeak car travelers are around 3 per cent. For the full range of parameters tested, we find costs inthe range of 0-24 percent of total travel costs.

Long-Run vs. Short-Run Perspectives on Consumer Scheduling: Evidence from a Revealed-Preference Experiment Among Peak-Hour Road Commuters

Earlier studies on scheduling behavior have mostly ignored that consumers have more flexibility to adjust their schedule in the long run than in the short run. We introduce the distinction between long‐run choices of travel routines and short‐run choices of departure times, using data from a real‐life peak avoidance experiment. We find that participants value travel time higher in the long‐run context, supposedly because changes in travel time can be exploited better through the adjustment of routines. Schedule delays are valued higher in the short run, reflecting that scheduling restrictions are typically more binding in the short run.

Changing with the Tide: Semiparametric Estimation of Preference Dynamics

We contrast the discovered preference hypothesis against the theory of coherent arbitrariness in a split-sample stated choice experiment on flood-risk exposure. A semiparametric local multinomial logit model is developed as an alternative to the Swait and Louviere (1993) test procedure controlling for preference dynamics within and between samples. The proposed model supports the discovered preference hypothesis by means of a decaying starting point bias. The Swait and Louviere (1993) test procedure reaches a different conclusion. It rejects the assumption of stable preferences, but most preference dynamics tend to be smoothed out, causing a more erratic pattern of preference dynamics. (JEL C14, Q51)

Stated Choice Experimental Designs for Scheduling Models

Abstract In this paper, we analyze statistical properties of stated choice experimental designs when model attributes are functions of several design attributes. The scheduling model is taken as an example. This model is frequently used for estimating the willingness to pay (WTP) for a reduction in schedule delay early and schedule delay late. These WTP values can be used to calculate the costs of travel time variability. We apply the theoretical results to the scheduling model and design the choice experiment using measures of efficiency (S-efficiency and WTP-efficiency). In the simulation exercise, we show that the designs based on these efficiency criteria perform on average better than the designs used in the literature in terms of the WTP for travel time, schedule delay early, and schedule delay late variables. However, the gains in efficiency decrease in the number of respondents. Surprisingly, the orthogonal design performs rather well in the example we demonstrated.

Changing with the Tide: Semi-Parametric Estimation of Preference Dynamics

This paper contrasts the discovered preference hypothesis against the theory of coherent arbitrariness in a split-sample stated choice experiment on flood risk exposure in the Netherlands. A semi-parametric local multinomial logit model (L-MNL) is developed as an alternative to the Swait and Louviere (1993) procedure to control for preference dynamics within and between samples. The L-MNL model finds empirical support for the discovered preference hypothesis in the form of a declining starting point bias induced by the first choice task. These results differ from the Swait and Louviere procedure which, due to its limited flexibility, accepts the standard assumption underlying microeconomic theory of stable preference parameters throughout the choice sequence. The observed preference dynamics puts the use of choice experiments at risk of generating biased welfare estimates if not controlled for.

The User Costs of Air Travel Delay Variability

We derive the expected user costs of U.S. domestic air travel delay variability taking into account scheduling behavior of travelers. Travelers do not only consider mean arrival delays but also face scheduling costs because they arrive too early or too late at their destination. The model allows travelers to anticipate arrival delay variability by choosing an earlier flight. We show that the expected user costs of U.S. air traffic delays are almost doubled if expected schedule delay of travelers is accounted for, whereas the benefits of improvements in mean delay are underestimated by 16% if arrival delay variability is ignored.

Welfare Effects of Distortionary Tax Incentives Under Preference Heterogeneity: An Application to Employer-Provided Electric Cars

This paper presents an approach for the estimation of welfare effects of tax policy changes under heterogeneity in consumer preferences. The approach is applied to evaluate the welfare effects of current tax advantages for electric vehicles supplied as fringe benefits by employers. Drawing on stated preferences of Dutch company car drivers, we assess the short-run welfare effects of changes in the taxation of the private use of these vehicles. We find that the welfare gain of a marginal increase in the taxation of electric company cars is substantial and even outweighs the marginal tax revenue raised.

A Practical Method to estimate the Benefits of Improved Network Reliability: An Application to Departing Air Passengers

This paper develops and applies a practical method to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the scheduling costs due to travel time variability for car travel. In contrast to existing practical methods, we explicitly consider the effect of travel time variability on departure time choices. We focus on situations when only mean delays are known, which is typically the case when standard transport models are used. We first show how travel time variability can be predicted from mean delays. We then estimate the scheduling costs of travellers, taking into account their optimal departure time choice given the estimated travel time variability. We illustrate the methodology for air passengers traveling by car to Amsterdam Schiphol Airport. We find that on average planned improvements in network reliability only lead to a small reduction in access costs per trip in absolute terms, mainly because most air passengers drive to the airport outside peak hours, when travel time variability tends to be low. However, in relative terms the reduction in access costs due to the improvements in network reliability is substantial. In our case we find that for every 1 Euro reduction in travel time costs, there is an additional cost reduction of 0.7 Euro due to lower travel time variability, and hence lower scheduling costs. Ignoring the benefits from improved reliability may therefore lead to a severe underestimation of the total benefits of infrastructure improvements.

Probabilistic Choice and Congestion Pricing with Heterogeneous Travellers and Price-Sensitive Demand

This paper deals with first-best and second-best congestion pricing of a stylised two-link network with probabilistic route choice of travellers. Travellers may have heterogeneous values of travel times and may differ in their idiosyncratic route preferences. We derive first-best and second-best tolls taking into account how the overall network demand responds to generalized costs including the tolls that are levied. We show that with homogeneous values of times the welfare losses of second-best pricing, of one link only, may be smaller if route choice is probabilistic. Furthermore, we show that with heterogeneous values of times, common second-best tolls and group-differentiated tolls can be very close when route choice is governed by random utility maximisation, leading to low welfare losses from the inability to differentiate tolls.

Market Structure and the Pricing of New Products: A Nested Logit Approach with Asymmetric Firms

This article investigates competition in a market with an emerging technology using a discrete choice model to analyze demand and welfare. We focus on industry structure and investigate the impact of different market structures on demand for the new technology and on welfare. The car market serves as a prime example of such a market, where electric vehicles (EV’s) represent the new technology competing with standard cars with internal combustion engines (ICV’s). To analyze such a market, we use a nested logit model. In contrast to earlier literature, we allow firms to be asymmetric and active in multiple nests, with different numbers of variants in each nest, which can add up to any market share. Additionally, we add to existing literature by considering the case where substitutability between firms is stronger than between technologies, by nesting products by technology instead of by firm. We find implicit analytical solutions for the equilibrium mark-ups which can be used when there are two nests in the market; within that restriction firms can be asymmetric. Numerically, we find that EV sales are higher if offered by a new entrant only selling EV’s as opposed to when it is supplied by a firm selling variants of both types. We present an index based on mark-up differences between variants in the market, which can be used to a priori determine whether a change in market structure would increase or decrease welfare. These results are general to the nested logit model, and the index can thus be used in any market, as long as the market is sufficiently accurately described by the nested logit model.