Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Motor fuel choice: An econometric analysis

Concerns about air pollution and energy security have stimulated interest in alternative automotive fuels and in vehicles that can use multiple fuels and combinations of fuels. Consumer behavior in the choice of motor fuel for flexible-fuel vehicles is likely to be a key factor in the creation and stability of markets for new fuels. The sensitivity of fuel choice to fuel prices is investigated here using data on purchases of regular, premium, leaded, and unleaded grades of gasoline. Multinomial logit choice models are estimated for the years 1982, 1983, and 1984. Consumer choices are found to be highly elastic with respect to fuel prices. New fuels will have to be priced within a few cents of existing fuels to capture significant market shares. Consumers also exhibit strong preferences for the fuel type specified by law (unleaded vs. leaded) for their vehicles. Thus, legal restrictions could play an important role in stabilizing alternative fuels markets. The typical consumer is willing to pay 5–10 cents more per gallon for premium grade gasoline. The premium for premium has been increasing as increasing numbers of turbocharged and fuel-injected engines join the fleet, reflecting the fact that consumers are willing to pay more for a fuel if they believe their engines require it.     

An empirical analysis on the arterial fundamental diagram

For uninterrupted traffic flow, it is well-known that the fundamental diagram (FD) describes the relationship between traffic flow and density under steady state. For interrupted traffic flow on a signalized road, it has been recognized that the arterial fundamental diagram (AFD) is significantly affected by signal operations. But little research up to date has discussed in detail how signal operations impact the AFD. In this paper, based upon empirical observations from high-resolution event-based traffic signal data collected from a major arterial in the Twin Cities area, we study the impacts of g/C ratio, signal coordination, and turning movements on the cycle-based AFD, which describes the relationship between traffic flow and occupancy in a signal cycle. By microscopically investigating individual vehicle trajectories from event-based data, we demonstrate that not only g/C ratio constrains the capacity of a signalized approach, poor signal coordination and turning movements from upstream intersections also have significant impact on the capacity. We show that an arterial link may not be congested even with high occupancy values. Such high values could result from queue build-up during red light that occupies the detector, i.e. the Queue-Over-Detector (QOD) phenomenon discussed in this paper. More importantly, by removing the impact of QOD, a stable form of AFD is revealed, and one can use that to identify three different regimes including under-saturation, saturation, and over-saturation with queue spillovers. We believe the stable form of AFD is of great importance for traffic signal control because of its ability to identify traffic states on a signal link.     

Random covariance heterogeneity in discrete choice models

In this paper, we extend the standard discrete choice modelling framework by allowing for random variations in the substitution patterns between alternatives across respondents, leading to increased model flexibility. The paper shows how such a Mixed Covariance model can be specified either with purely random variation or with a mixture of random and deterministic variation. Additionally, the model can be based on an underlying GEV or ECL structure. Finally, the model can be specified as a continuous mixture or as a discrete mixture. An application on Stated Preference data for the choice of departure time and travel mode shows that important gains in model performance can be obtained by allowing for random covariance heterogeneity. Furthermore, the approach leads to significant differences in the implied willingness to pay measures, and the substitution patterns between alternatives.     

An empirical comparison of travel choice models that capture preferences for compromise alternatives

Compromise alternatives have an intermediate performance on each or most attributes rather than having a poor performance on some attributes and a strong performance on others. The relative popularity of compromise alternatives among decision-makers has been convincingly established in a wide range of decision contexts, while being largely ignored in travel behavior research. We discuss three (travel) choice models that capture a potential preference for compromise alternatives. One approach, which is introduced in this paper, involves the construction of a so-called compromise variable which indicates to what extent (i.e., on how many attributes) a given alternative is a compromise alternative in its choice set. Another approach consists of the recently introduced random regret-model form, where the popularity of compromise alternatives emerges endogenously from the regret minimization-based decision rule. A third approach consists of the contextual concavity model, which is known for favoring compromise alternatives by means of a locally concave utility function. Estimation results on a stated route choice dataset show that, in terms of model fit and predictive ability, the contextual concavity and random regret models appear to perform better than the model that contains an added compromise variable.     

Cognitive cost in route choice with real-time information: An exploratory analysis

Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.     

An analysis of instability in a departure time choice problem

This study performs a theoretical analysis of instability in a departure time choice problem. Stability of equilibrium is an important factor for reliability of travel time. If equilibrium is not stable, travel time changes over a period of days even if demand and network performance are stable. This study examines the stability of a dynamic user equilibrium problem by using the departure time choice problem. The mechanism of day‐to‐day changes in a traveller's behaviour is determined first, and then a function that indicates dissimilarity to equilibrium is defined. The day‐to‐day changes in the dissimilarity function are mathematically examined using approximations. A numerical test is also carried out to verify the result. Results of these analyses show that there can be a case where the system does not converge to equilibrium. It is also indicated that this instability should be caused by the non‐monotonicity of the schedule cost.     

Modeling the modal choice decision process

Most models of modal choice are macroanalytic in nature — focusing on the behavior of large groups of travelers — and have limited explanatory power. Transportation managers need to know more about the decision processes of individual travelers in selecting a mode for a particular trip, if they are to be able to develop strategies for influencing these decisions. A microanalytic model of modal choice is therefore developed in flow-chart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need by first specifying the characteristics of the trip itself and then specifying the “ideal” modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this “ideal” solution and the consumer is assumed to select that mode which provides the best match. The model explicitly recognizes the impact of psychological variables on modal choice as well as the consumer's need for information if he or she is to evaluate realistically all alternatives.     

An empirical study on aggregation of alternatives and its influence on prediction in car type choice models

Transportation - Assessing and predicting car type choices are important for policy analysis. Car type choice models are often based on aggregate alternatives. This is due to the fact that analysts...     

An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

Temporal stability of travelers' activity choice and home-stay duration: Some empirical evidence

This paper explores the temporal stability of activity type-choice models and models of travelers' home-stay duration. To empirically evaluate this stability, a nested logit model of activity-type choice and a proportional hazards model of home-stay duration are estimated using data from two-day travel diaries collected in the fall of 1989 and again, from the same individuals, in the fall of 1990. The results show that the models are not temporally stable over the one year time period separating the two travel-diary samples. A number of possible reasons for this instability are discussed.     

Route choice and the value of commuting time: An economic model of dichotomous choice

This paper presents an economic model of generalized travel cost and provides an empirical study of the parameters of the cost function. The route-choice model that is estimated combines McFadden's theory of qualitative choice behavior with a function for the value of travel time in which total trip time and the income level are assumed to influence the marginal value of time. The empirical results indicate that, for a sample of commuters in the Chicago metropolitan area in 1972, the value of time is a positive function of total trip time, but is not a function of income.     

An integrated assessment of factors affecting modal choice: towards a better understanding of the causal effects of built environment

This paper discusses the methodological challenges in understanding causal relationships between urban form and travel behavior and uses a holistic quasi-experimental approach to investigate the separable marginal influence of each of several urban form factors on mode choice as well as the complex relationships between those factors and a wide range of personal traits. Data analysis and models are used to reveal the effect of such interactions on mode choice for both work and non-work trips in Rome, Italy. It is found that population density does not have a significant marginal positive effect on sustainable mode choice for work trips. Conversely, this factor decreases sustainable mode choice for non-work trips. Small scale street design quality alone increases sustainable mode choice for non-work trips. This is while presence of street network integration alone increases automobile use for all trip purposes. The results point to the importance of incorporating all the urban form factors of diversity, design and street network integration if the goal is to increase the use of more sustainable modes of transportation for both work and non-work trips, but also show that attitudes and preferences can modify the response to urban design factors. The findings suggest that thoughtful policies triggering certain attitudes (cost sensitivity, sensitivity to peer pressure regarding the value attributed to sustainable transportation, and transit preference) can be adopted to significantly increase sustainable mode choice even in the neighborhoods with specific physical restrictions.     

Consistency and fungibility of monetary valuations in transport: An empirical analysis of framing and mental accounting effects

Governments around the world use monetised values of transport externalities to undertake project appraisal and cost–benefit analysis. However, because different types of benefits are monetised (e.g., travel time savings, preventing statistical fatalities, reliability, etc.) the question naturally arises as to whether they are consistent. That is, whether a “dollar is a dollar” as welfare economics requires, or whether spending money in one area carries a different disutility from spending money in another area. This would equate to a violation of fungibility, which is the property of a good or a commodity whose individual units are capable of mutual substitution. The view that money is not fungible is explained in behavioural economics through theories of framing and mental accounting. This paper describes the results of a stated choice experiment designed to test the fungibility and consistency of monetary valuations in transport. From a nationally representative sample, we elicit direct values for the three pairwise trade-offs between travel time, travel cost, and safety. We then show that in the context of our analysis, any trade-offs inferred on the basis of other trade-offs, as is common practice (e.g. inferring a safety vs time trade-off on the basis of monetary valuations for time and safety), produces biased results, suggesting that the assumption of fungibility does not hold. Specifically, we find that time is valued more highly when valued directly by cost than when traded with safety, and the reverse is true for safety.     

An empirical comparison of disaggregate category and regression trip generation analysis techniques

Category and regression household trip generation analysis techniques were compared and contrasted. The comparative research was facilitated through a discussion that revealed the interchangeability of two methods of calibrating a category model. While the cell mean method is simple to implement, it does not readily yield statistical indexes for comparison with regression models. The general linear model analysis of variance (GLANOVA) readily provides statistical indexes for the comparison of category and regression trip generation models, and it produces identical empirical results to the simpler cell mean approach of calibrating a category model.The empirical comparison supports the widespread use of category models for trip generation analysis in transportation planning studies. It was found that regression and category models yielded equivalent results for typical planning applications at the district level of aggregation. In addition, both techniques estimated overall trip rate with equal accuracy in the calibration phase, and the two approaches were indistinguishable with respect to sample size sensitivity. However, households with extremely large trip rates were underestimated to a greater degree by category models than regression models. This tendency, in turn, resulted in larger calibration coefficients of determination for regression models. Since the cell mean method of calibrating a model is simpler and easier to understand than a regression model representation, category models can be recommended over regression models for planning studies.     

Stated adaptation surveys and choice process: Some methodological issues

The "Stated Adaptation" survey is an interactive technique which allows us to obtain a clearer picture of the attitudes and behaviours of individuals when confronted with hypothetical situations, in particular inexperienced travel conditions. This method makes use of a simulation game whose purpose is to explore on small samples individuals' choice processes when selecting between the different transport alternatives which are available to them. This paper describes how gaming-simulation is designed, with reference to the issues tackled by two surveys which have recently been carried out in France (reactions to urban road pricing and perception of electric vehicles). It describes the benefits of this experimental approach which allows stated behaviours to be checked to a considerable degree. The limits and potential developments of this survey technique are also discussed.     

An activity-episode generation model that captures interactions between household heads: development and empirical analysis

In this paper, we develop an approach for modeling the daily number of non-work, out-of-home activity episodes for household heads that incorporates in its framework both interactions between such members and activity setting (i.e. independent and joint activities). Trivariate ordered probit models are estimated for the heads of three household types – couple, non-worker; couple, one-worker; and couple, two-worker households – using data from a trip diary survey that was conducted in the Greater Toronto Area (GTA) during 1987. Significant interactions between household heads are found. Moreover, the nature of these interactions is shown to vary by household type implying that decision-making structures and, more generally, household dynamics also vary by household type. In terms of predictive ability, the models incorporating interactions are found to predict more accurately than models excluding interactions. The empirical findings emphasize the importance of incorporating interactions between household members in activity-based forecasting models.     

On the perceived justice of urban road pricing: An empirical study in Lyon

Various methods of restricting automobile traffic, by price (tolls) or by quantity (odd/even license plates or limited days of traffic), are tested in a survey (N ～ 400) about attitudes toward traffic restrictions in Lyon, France. Ordered probit models with random-effects panel allow us to estimate the survey respondents’ perceptions of these methods, as well as the roles of individual socio-demographic characteristics in the formation of these perceptions. Both the restriction of automobile traffic and its regulation by congestion (waiting in line) are widely considered unjust by the respondents, regardless of whether they work and whether they are drivers or non-drivers. Their attitudes towards tolls justified by the pollution caused by automobile traffic are less negative. As regards compensation, in addition to emergency vehicles and those that transport people with limited mobility, respondents believe car-pooling ought to benefit of a toll exemption. The support for a reduced rate for low-income users shows a concern for justice to which it will be necessary to respond. The respondents’ socio-professional status, level of education, car use or non-use, and residence inside or outside of the toll zone clearly play a role in their perceptions of these methods of regulation and compensation.     

Exploring ride-hailing fares: an empirical analysis of the case of Madrid

Transportation - Ride-hailing is an emerging service that is transforming door to door mobility in urban areas. Users can easily request a ride through a smartphone app that informs them of the...     

Estimating commuter mode choice: A discrete choice analysis of the impact of road pricing and parking charges

Automobile use leads to external costs associated with emissions, congestion, noise and other impacts. One option for minimizing these costs is to introduce road pricing and parking charges to reduce demand for single occupant vehicle (SOV) use, while providing improvements to alternatives to encourage mode switching. However, the impact of these policies on urban mode choice is uncertain, and results reported from regions where charging has been introduced may not be transferable. In particular, revealed preference data associated with cost recovery tolls on single facilities may not provide a clear picture of driver response to tolls for demand management. To estimate commuter mode choice behaviour in response to such policies, 548 commuters from a Greater Vancouver suburb who presently drive alone to work completed an individually customized discrete choice experiment (DCE) in which they chose between driving alone, carpooling or taking a hypothetical express bus service when choices varied in terms of time and cost attributes. Attribute coefficients identified with the DCE were used in a predictive model to estimate commuter response to various policy oriented combinations of charges and incentives. Model results suggest that increases in drive alone costs will bring about greater reductions in SOV demand than increases in SOV travel time or improvements in the times and costs of alternatives beyond a base level of service. The methods described here provide an effective and efficient way for policy makers to develop an initial assessment of driver reactions to the introduction of pricing policies in their particular regions.