The sensitivity of the valuation of travel time savings to the specification of unobserved effects

The behavioural value of travel time savings (VTTS) remains a controversial data item in the evaluation of transport projects. Over the last 40 years we have seen numerous empirical studies on the amount that an individual or firm is willing to pay to save a unit of travel time (or more precisely transfer a fixed amount of time from one activity to another). With a few exceptions, the majority of empirical studies have used essentially the same data metric and model estimation procedure. The most popular approaches have used either revealed preference (RP) or stated preference (SP) data and a multinomial logit (MNL) choice model to identify marginal rates of substitution between travel time and price of a trip. A few more advanced studies have integrated RP and SP data and have explored the gains in behavioural power of models that relax, to varying degrees, the underlying assumptions that produce the MNL model. This paper highlights the potential gains from a more carefully considered structure of the unobserved effects which condition the form of a discrete choice model, and hence the possible misinference from the simpler MNL specification. We demonstrate the implication for VTTS estimates of serial correlation between the SP treatments, covariance amongst alternatives, presence of individual specific (random) effects or heterogeneity, and differential variance of the unobserved effects.     

Joint RP–SP data in a mixed logit analysis of trip timing decisions

In the current paper, a departure time choice model including travel time variability is estimated, combining stated preference and revealed preference data. We account for response scale differences between RP and SP data and, applying the mixed logit model, test for correlation of scheduling sensitivity across RP and SP choices within individuals. The analysis implies systematic differences in the RP and SP data. With support of the evaluation from the Stockholm trial, this indicates that SP is less trustworthy for trip timing analysis and forecasting, presumably because there are temporal differences in RP and SP choice situations.     

Identifying sources of systematic variation in direct price elasticities from revealed preference studies of inter-city freight demand

Freight demand elasticity studies vary significantly in terms of the demand measure, data type, estimation method, commodity type, etc. This wide variation makes it difficult to compare empirical estimates when the differences may arise in part from the methods and data used. In this paper we conduct a comparative analysis to identify systematic sources of influence on direct price elasticity estimates in the context of freight transport, distinguishing between road, rail, and sea transport, using published direct price elasticities from 12 elasticity-derivative studies from five countries. The study focuses on revealed preference elasticities defined by the freight rate for tonnes and tonne kilometres of inter-city freight movements. Systematic sources that explain differences in direct price elasticities include the demand elasticity measure, mode, commodity class, model estimation form, country, and temporal nature of data (e.g., cross-section). Analysts can utilise the model outputs to adjust the empirical evidence from specific studies to control for differences that impact on the behavioural implications of comparative evidence.     

Modelling airport and airline choice behaviour with the use of stated preference survey data

The majority of studies of air travel choice behavior make use of revealed preference (RP) data, generally in the form of survey data collected from departing passengers. While the use of RP data has certain methodological advantages over the use of stated preference (SP) data, major issues arise because of the often low quality of the data relating to the un-chosen alternatives, in terms of explanatory variables as well as availability. As such, studies using RP survey data often fail to recover a meaningful fare coefficient, and are generally not able to offer a treatment of the effects of airline allegiance. In this paper, we make use of SP data for airport and airline choice collected in the US in 2001. The analysis retrieves significant effects relating to factors such as airfare, access time, flight time and airline and airport allegiance, illustrating the advantages of SP data in this context. Additionally, the analysis explores the use of non-linear transforms of the explanatory variables, as well as the treatment of continuous variations in choice behavior across respondents.     

Investigating household vehicle ownership,mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region

This study proposes a Nested Logit model to investigate household travel behaviour in respect to vehicle ownership, mode choice and trip sharing decisions. The model is analysed using revealed preference (RP) and stated preference (SP) data since a combined estimation of RP/SP data is an effective method of expressing complex travel behaviour and forecasting travel demand for new transport services. In the proposed model, the nesting structure has two levels. The upper level shows car ownership, motorcycle ownership, and no vehicle-ownership choices, and the lower level shows the mode choice combinations for two-traveller households. Trip sharing is considered as one of the mode-choice options in the model. The proposed model is analysed using data from the Bangkok Metropolitan Region. The analysis conducted informs that Central Business District (CBD) travel, long distance travel, household income, job status, age of travellers and presence of school children in households are key aspects in household travel decisions. Based on these aspects, households make important decisions on vehicle ownership, mode choice and trip sharing. In addition, this study reveals commuters’ hidden preferences for modes that are not in existence, in particular the Mass Rapid Transit System in the Bangkok Metropolitan Region due to be fully implemented in 2010.     

Price elasticities of demand for passenger air travel: a meta-analysis

The demand for air transport is largely determined by the spending capacity of customers. This paper aims to offer more insight into the determinants of price elasticities in the aviation sector. It seeks to identify both common and contrasting factors that influence the price elasticities, on the basis of a comparative analysis among a large number of empirical studies in this field. By means of meta-analytical methods, the relative importance of several driving forces such as distance, type of ticket and the nature of study is investigated.     

Spatial and temporal transferability of relationships between travel demand,trip cost and travel time

The paper draws together results from several major revealed preference (RP) and stated preference (SP) data sets, collected for large-scale disaggregate models and VTTS research, respectively, to comment on trends which appear in time and cost trading as between different time periods and different countries.     

Heteroscedastic control for random coefficients and error components in mixed logit

Developments in simulation methods, and the computational power that is now available, have enabled open-form discrete choice models such as mixed logit to be estimated with relative ease. The random parameter (RP) form has been used to identify preference heterogeneity, which can be mapped to specific individuals through re-parameterisation of the mean and/or variance of each RP’s distribution. However this formulation depends on the selection of random parameters to reveal such heterogeneity, with any residual heterogeneity forced into the constant variance condition of the extreme value type 1 distribution of the classical multinomial logit model. In this paper we enhance the mixed logit model to capture additional alternative-specific unobserved variation not subject to the constant variance condition, which is independent of sources revealed through random parameters. An empirical example is presented to illustrate the additional information obtained from this model.     

Elasticities of mode choice probabilities and market elasticities of demand: Evidence from a simultaneous mode choice/shipment-size freight transport model

This paper presents empirical estimates of market elasticities of demand and elasticities of mode choice probabilities in the intercity freight transport market. Results are derived from a mixed discrete/continuous choice model of mode and shipment size. The mode choice component of the full model was specified as a binary probit function. The two modes considered were rail and regulated common carriers (full truck load). Data was drawn from the US Commodity Transportation Survey consisting of individual shipments of manufactured goods identified at the most disaggregate level. Results obtained in this study are compared with those obtained in previous studies, and areas of similarities and dissimilarities in the magnitude as well as interpretation of the results are highlighted. The own-price and cross-price elasticities of mode choice probabilities were found to vary from 1.44 to 1.88, and from 1.54 to 1.75, respectively. The market price elasticities of demand were found to vary significantly across commodity groups and geographic territories. Among the 40 market segments considered, the truck price elasticity of demand ranged between −0.749 and −2.525; the rail price elasticity of demand was slightly larger, ranging between −0.956 and −2.489; and the rail–truck cross-price elasticity of demand ranged between 0.904 and 2.532.     

A weighted logit freight mode-choice model

The paper describes the estimation of a weighted discrete choice model applicable for analysing choice of mode and crossing for freight in the Oresund region. The study finds that, by applying a weighted logit approach, it is possible to establish a suitable decoupling of agents and shipments. Moreover, by assessing preferences on the basis of a representative baseline OD matrix it is possible to better reflect the dependence between mode substitution effects and geography/infrastructure. The paper presents demand elasticities with respect to monetary cost and travel time as well as value-of-time estimates for five modes and thirteen commodity groups.     

Sample size needed for calibrating trip distribution and behavior of the gravity model

Conventional calibration algorithms of trip distribution models assume that the analyst has a whole base year trip matrix. To attain a whole trip matrix, the sample size for travel surveys needed to be as large as possible. However, this could be very expensive especially in large cities. Some studies in the past showed a small sized sample would be enough to estimate functional parameters of observed trip length frequency distribution. But the performance of a gravity model with small sized samples has never been addressed. This empirical study has shown that sample sizes as small as 1000 (even smaller for quick response studies) could be as dependable as large sample surveys using a line search calibration algorithm.     

Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.     

Experience conditioning in commuter modal choice modelling – Does it make a difference?

This paper investigates the impact of decision maker’s experience on model elasticities and predicted market share, using data collected in Sydney on commuter mode choice. Usage frequency is used as a proxy for experience and two separate mode choice models are estimated – one with experience conditioning choice and one without. Key model outputs are compared and we find that differences in the value of travel time savings and model elasticities are very marked. This suggests that ignoring experience that one has with each alternative in their choice set may be a candidate source of error in travel demand forecasts. We develop a method to obtain the level of experience for use in application of choice models to increase their prediction power.     

Level of aggregation of zoning and temporal transferability of the gravity distribution model: The case of Lyon

This study deals with the temporal transferability of the parameters of the gravity model of trip distribution and focuses on the trade-off between spatial resolution and data requirements. The models are calibrated using O–D matrices constructed from the three most recent Lyon household travel surveys (1985, 1995 and 2006) and generalised travel time data from coded transport networks for the three dates. Calibration has been conducted for three different zoning levels which have been chosen in line with common practice. The parameters obtained from model calibration are then applied to estimate O–D matrices at a later date and the results are compared using indicators that have been established for the zoning level applied in calibration, but also using indicators that have been aggregated in two different ways: aggregation to create larger zones or distance segments.Our findings confirm our initial intuition: the choice of zoning is fundamentally important. Moreover, in the best case, the parameters of the model change, but not sufficiently for the goodness-of-fit of the “predicted” model to be very different from that of the matrix obtained during calibration. It is possible to use the gravity model for forecasting purposes, but on condition that the goals of the study are compatible with the level of error in the reproduction of the observed matrices. If the zoning is either too coarse or too fine grained, forecasting performance is compromised.     

A dynamic panel analysis of urban metro demand

A dynamic panel model is used to estimate the effect that fares, income and quality of service have on demand for a sample of 22 urban metros. The estimated price elasticity is −0.05 in the short run and −0.33 in the long run. The estimated long run income elasticity is small but positive (0.18), indicating that metros are perceived as normal goods. The quality of service elasticities are positive and substantially higher than the absolute value of fare elasticities. The implication is that quality of service improvements, rather than fare reductions, may be more effective in increasing metro patronage.     

The effect of travel cost on frequencies of shopping and recreational trips in Sweden

This article focus on how the cost of travel affects travel behavior. A trip frequency model for recreational and shopping trips is suggested and used to investigate this. The data that is used comes from a Swedish travel habit survey where the respondents’ trip frequencies of both types of trips on a certain day are recorded. This is likely to introduce a correlation structure, which is incorporated in the model. Special attention is paid to the effect of travel cost on trip frequencies for different regions and income groups. As a measure of the sensitivity of cost changes, elasticity of demand is calculated. The precision of the elasticities are evaluated with simulated p-values.     

Is the Mexico City metro an inferior good?

Time series cointegration techniques are used to estimate whether the Mexico City metro is perceived as a normal or inferior good. Owing to the fact that the Mexico City metro mainly serves the low income sectors of the population, this paper attempts to separate the overall income effect into two uncorrelated components: a vehicle stock effect (used as a proxy for medium/high income) and minimum wage effect net of the influence of vehicle stock (used as a proxy for low income). The time series cointegration results show that minimum wage elasticities are positive whilst vehicle stock elasticities are negative. These results suggest that for the majority of metro users, whose salaries are based on low multiples of the minimum wage and are not potential car owners, the Mexico City metro is perceived as a normal good. However, for middle/high income earners, who can afford to buy a private vehicle when their incomes increase, the Mexico City metro is perceived as an inferior good.     

Low-cost airlines and airport competition

An important question from the viewpoint of competition analysis in the air transport industry is the extent to which low-cost airlines operating from a secondary airport compete with full-service airlines serving a main airport in a multiple airport region. In this paper we address the issue of the competition between full-service and low-cost airlines serving adjacent airports in the Greater London using econometric estimation of demand structure (own- and cross-price elasticities). Our analysis follows the methodology in (Pels, E., Nijkamp, P., Rietveld, P., 2000. Airport and airline competition for passengers departing from a large metropolitan area. Journal of Urban Economics, 48 (1), 29–45, Pels, E., Nijkamp, P., Rietveld, P., 2003. Access to and competition between airports: a case study for the San Francisco Bay area. Transportation Research Part A: Policy and Practice, 37 (1), 71–83). It is based on the nested logit model which we use to capture three key dimensions of passenger choice: air fare, surface-access costs and frequency. We obtained estimates of the own- and cross-price elasticities, which was the focus of our interest. On the basis of our understanding of the industry dynamics we find these estimates, especially of the cross-price elasticities, to be on the low side.     

The value and effects of introducing high standard train and bus concepts in Blekinge, Sweden

A set of improvements were introduced on a railway line in the south of Sweden in January 1992, including a new train concept, improved timetable and lower fares. The new train replaced a combination of old rail cars and standard coaches (bus). Furthermore, 18 months later, a new high comfort coach (bus) service was introduced as a complement to the train timetable to increase the number of departures. Forecasts from a national four-step model system, based on Revealed Preference data, which did not cover explanatory factors like mode concept, comfort, mode image, was made and compared with a forecast based on stated preference data choice model. Both type of forecasts were compared to travel statistics. Stated Preference data have been collected from car drivers, coach and train passengers. The set of improvements have been included in the SP alternatives presented to the subjects. A structured logit model, a regression model and a linear programming model are compared. Unstructured and structured logit models are compared. Logit models have been estimated and used to form a mode choice model which predicts the changes in market shares between the train and car. The choice model forecast coincide, accidentally, with actual demand 8–9 months after the introduction of the set of improvements (a 40% increase in journeys). The set of improvements, including the new train service, has proven to be a success with more than 100% increase in travel after only two years of operation. Differences in estimated logit models for 1991 and 1992 are explained as partly due to asymmetry effects and partly to shortcomings in the SP data collection technique. Logit model estimates for train and coach are compared. The value of high standard coach service, running in parallel to the train, is estimated. The new coach gained few new passengers and also a much lower monetary value than the train by both train and bus passengers.     

Airport pricing systems in europe and an application of Ramsey pricing to Spanish airports

This paper describes the landing fee structures which are prevalent at most European airports: a mix of weight-based landing fees, passenger fees, and other charges. The paper describes alternative pricing systems which emphasize economic efficiency rather than airport finance considerations. Finally the paper calculates the structure of landing fees for a hypothetical uncongested airport using a Ramsey pricing formulation with data representative of uncongested Spanish airports. Further research is necessary before recommending implementation because the elasticities of demand and operating costs with distance are borrowed from North American studies.