The value of travel time and reliability-evidence from a stated preference survey and actual usage

This research examined travel behavior of Managed Lane (ML) users to better understand the value travelers place on travel time savings and travel time reliability. We also highlight the importance of survey design techniques. These objectives were accomplished through a stated preference survey of Houston’s Katy Freeway travelers. Three stated choice experiment survey design techniques were tested in this study: Bayesian (D_b) efficient, random level attribute generation, and an adaptive random approach. Mixed logit models were developed from responses using each of those designs. The value of travel time savings (VTTSs) estimates do vary across the design strategies, with the VTTS estimates based on the D_b-efficient design being approximately half the estimates from the other two designs. However, among the three design strategies, the value of travel time reliability (VOR) was only significant in the D_b-efficient design.The estimated VTTS from actual Katy Freeway usage (as measured using actual tolls paid and travel time saved on the managed lanes) is $51/h. This likely also includes any value that travelers place on travel time reliability. In comparison, our combined estimate of VTTS and VOR based on the SP survey (D_b-efficient design) was $50/h, which is remarkably close to the estimate from the actual usage data. Based on our dataset, the D_b-efficient design technique proved superior to the other two techniques. Finally, this research also supports the importance of including both travel time and travel time reliability parameters when estimating the willingness to pay for, and therefore benefits derived from, ML travel.     

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.     

The Effectiveness of Conceptual Airport Terminal Designs

Abstract

Aviation passenger traffic is forecast to grow significantly over the next decade and beyond. To accommodate this growth will require investment in airport infrastructure, including terminals. These buildings represent large, lumpy investments, so it is important to provide the capacity to accommodate the forecast traffic. However, this depends on at least two factors: the accuracy of the forecast of future demand, and the process of translating these forecasts into designs. Error in either factor can be potentially catastrophic financially. Translating forecasts into designs depends on ‘rules of thumb’ formulae that convert design hour flows into area requirements for each terminal facility. This paper examines the process of translating demand forecasts into conceptual terminal designs. The basic methods used are outlined, and how they affect the conceptual terminal design process are revealed. A model for conceptual terminal design is derived, presented and validated based on a sample of UK airports. It is shown that even if demand forecasts can be taken to be completely accurate, there can still be errors in terminal design and size resulting from the use of these ‘rules of thumb’.     

Towards a practical method to establish comparable values of travel time savings from stated choice experiments with differing design dimensions

Individual’s process the information in stated choice (SC) experiments in many different ways. In order to accommodate decisions rules that are used in processing information, there is good sense in conditioning the parameterisation of stated choice design attributes on these rules. In particular, rules might be invoked to cope with the dimensionality of the SC design. In this paper, we investigate the impact of rules such as attribute aggregation and reference dependency on preference profiles for specific design attributes, as well as the design specification, as we vary the dimensionality of an SC design. The heteroscedastic extreme value logit model is estimated to identify the role of design dimensionality and attribute processing rules, after accounting for scale differences across sixteen pooled data designs The empirical evidence, drawn from a study in Sydney of car commuter route choice, suggests that accounting for the way that stated choice designs are processed, given their dimensionality, does make a statistically significant difference on measures of willingness to pay, as does accounting for scale differences between pooled data designs. This evidence has practical value in guiding the design of SC experiments and in adjusting results from different SC designs when comparing the evidence. We propose a simple adjustment formula to use in adjusting VTTS from different studies so that they are comparable. From a practical policy perspective, the evidence sends a warning about the risk of undervaluing mean VTTS if the attribute processing rules are not accounted for.     

Three-dimensional conflict count models for unstructured and layered airspace designs

This paper presents analytical models that describe the safety of unstructured and layered en route airspace designs. Here, ‘unstructured airspace’ refers to airspace designs that offer operators complete freedom in path planning, whereas ‘layered airspace’ refers to airspace concepts that utilize heading-altitude rules to vertically separate cruising aircraft based on their travel directions. With a focus on the intrinsic safety provided by an airspace design, the models compute instantaneous conflict counts as a function of traffic demand and airspace design parameters, such as traffic separation requirements and the permitted heading range per flight level. While previous studies have focused primarily on conflicts between cruising aircraft, the models presented here also take into account conflicts involving climbing and descending traffic. Fast-time simulation experiments used to validate the modeling approach indicate that the models estimate instantaneous conflict counts with high accuracy for both airspace designs. The simulation results also show that climbing and descending traffic caused the majority of conflicts for layered airspaces with a narrow heading range per flight level, highlighting the importance of including all aircraft flight phases for a comprehensive safety analysis. Because such trends could be accurately predicted by the three-dimensional models derived here, these analytical models can be used as tools for airspace design applications as they provide a detailed understanding of the relationships between the parameters that influence the safety of unstructured and layered airspace designs.     

Estimating bus component failure distributions from censored samples

A technique is developed and demonstrated which can be used to estimate bus component failure distributions in an operational setting. The advantage of the described method is that it estimates the distribution of an entire population when only a portion of that population has experienced a failure (censored samples). Further, in an operational setting, components in a population do not age at the same rate and thus, their ages (in miles) are not ordered with respect to time. A sampling technique is derived to draw an ordered, censored sample from a population that has been aged in an operational setting. The resulting sample is used to estimate the life distribution of the component of interest.     

Estimating metro passengers’ path choices by combining self-reported revealed preference and smart card data

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.     

Estimating a model of dynamic activity generation based on one-day observations: Method and results

In this paper we develop and explore an approach to estimate dynamic models of activity generation on one-day travel-diary data. Dynamic models predict multi-day activity patterns of individuals taking into account dynamic needs as well as day-varying preferences and time-budgets. We formulate an ordered-logit model of dynamic activity-agenda-formation decisions and show how one-day observation probabilities can be derived from the model as a function of the model’s parameters and, with that, how parameters can be estimated using standard loglikelihood estimation. A scale parameter cannot be identified because information on within-person variability is lacking in one-day data. An application of the method to data from a national travel survey illustrates the method. A test on simulated data indicates that, given a pre-set scale, the parameters can be identified and that estimates are robust for a source of heterogeneity not captured in the model. This result indicates that dynamic activity-based models of the kind considered here can be estimated from data that are less costly to collect and that support the large sample sizes typically required for travel-demand modeling. We conclude therefore that the proposed approach opens up a way to develop large-scale dynamic activity-based models of travel demand.     

Guidelines for samplers: measuring a change in behaviour from before and after surveys

This paper addresses the issue of using before and after surveys to evaluate behavioural changes in response to transport policies and investments, a procedure that, we argue is done far too rarely in this profession. Further, it demonstrates very clearly that, in almost all conceivable cases, there are considerable economies to be obtained by using a panel (again, under-utilised in our profession) to undertake evaluation, rather than successive independent cross-sectional surveys. The paper also addresses the critical issue of sample size requirements for measuring changes of a relatively small magnitude in travel behaviour; i.e., to state, with 95% confidence, that if there is a ∂ percent change in behaviour for the sample, there is a ∂ percent ± e percent change in the behaviour of the population, where e is the sampling error. In this paper, we present a method for calculating such sample size requirements from first principles and demonstrate the applicability both hypothetically and then empirically using data from the Puget Sound Transportation Panel. The formulation enables designers of before and after surveys to investigate the trade-offs between the statistical accuracy of their predictions and the sample size requirements systematically, without the need to specify ∂ a priori. This latter point is crucial, we argue, because we have limited information on ∂, yet, as we explain here, it drives the sample size requirements using alternative, well-cited approaches for calculating sample sizes to assess behavioural change. The results have important ramifications both for those implementing transport policies intended to produce behavioural change, especially when a cost-benefit evaluation of the policy is desired, and for those interpreting the results reported in previous studies.     

Assessing the CO2 impact of current and future rail track in the UK

This paper investigates the CO₂ impact of current and future UK rail track and estimates the material, process and transport emissions associated with construction, maintenance and end-of-life activities for designs at high and low traffic loads. Analysis shows that for current track configurations, track with concrete sleepers has the lowest CO₂ impact, followed by steel, hardwood and softwood. Several potential future rail track designs have been analysed including embedded rail and double and quadruple-headed rail. All future track designs have a lower impact than current designs, but this improvement is more marked at high traffic loads. Up to a 40% reduction in CO₂ impact could be achieved if the UK rail network was to move from conventional track design to a double-headed embedded rail design. Key levers for reducing the CO₂ impact of track are identified as service life extension, traffic load reduction and the selection of low impact track designs.     

Reducing bias in probe-based arterial link travel time estimates

The use of probe vehicles to provide estimates of link travel times has been suggested as a means of obtaining travel times within signalized networks for use in advanced traveler information systems. Previous research has shown that bias in arrival time distributions of probe vehicles will lead to a systematic bias in the sample estimate of the mean. This paper proposes a methodology for reducing the effect of this bias. The method, based on stratified sampling techniques, requires that vehicle count data be obtained from an in-road loop detector or other traffic surveillance method. The effectiveness of the methodology is illustrated using simulation results for a single intersection approach and for an arterial corridor. The results for the single intersection approach indicate a correlation (R²) between the biased estimate and the population mean of 0.61, and an improved correlation between the proposed estimation method and the population mean of 0.81. Application of the proposed method to the arterial corridor resulted in a reduction in the mean travel time error of approximately 50%, further indicating that the proposed estimation method provides improved accuracy over the typical method of computing the arithmetic mean of the probe reports.     

Simultaneous estimation of an origin–destination matrix and link choice proportions using traffic counts

A procedure for the simultaneous estimation of an origin–destination (OD) matrix and link choice proportions from OD survey data and traffic counts for congested network is proposed in this paper. Recognizing that link choice proportions in a network change with traffic conditions, and that the dispersion parameter of the route choice model should be updated for a current data set, this procedure performs statistical estimation and traffic assignment alternately until convergence in order to obtain the best estimators for both the OD matrix and link choice proportions, which are consistent with the survey data and traffic counts.Results from a numerical study using a hypothetical network have shown that a model allowing θ to be estimated simultaneously with an OD matrix from the observed data performs better than the model with a fixed predetermined θ. The application of the proposed model to the Tuen Mun Corridor network in Hong Kong is also presented in this paper. A reasonable estimate of the dispersion parameter θ for this network is obtained.     

An aircraft performance model implementation for the estimation of global and regional commercial aviation fuel burn and emissions

Estimates of global aviation fuel burn and emissions are currently nearly 10 years out of date. Here, the development of the Aircraft Performance Model Implementation (APMI) software which is used to update global commercial aviation fuel burn and emissions estimates is described. The results from APMI are compared with published estimates obtained using the US Federal Aviation Administration’s System for Assessing Aviation’s Global Emissions (SAGE) for the year 2006. The number of global departures modelled with the APMI software is 8% lower compared with SAGE and reflects the difference between their commercial air traffic statistics data sources. The mission fuel burn, CO₂ and H₂O estimates from APMI are approximately 20% lower than those predicted by SAGE for 2006 while the estimate for the total global aircraft SO_x emissions is approximately 40% lower. The estimates for the emissions of CO, HC and NO_x are 10%, 140% and 30% higher than those predicted by SAGE respectively. The reasons for these differences are discussed in detail.     

Strategic sampling for large choice sets in estimation and application

Many discrete choice contexts in transportation deal with large choice sets, including destination, route, and vehicle choices. Model estimation with large numbers of alternatives remains computationally expensive. In the context of the multinomial logit (MNL) model, limiting the number of alternatives in estimation by simple random sampling (SRS) yields consistent parameter estimates, but estimator efficiency suffers. In the context of more general models, such as the mixed MNL, limiting the number of alternatives via SRS yields biased parameter estimates. In this paper, a new, strategic sampling scheme is introduced, which draws alternatives in proportion to updated choice-probability estimates. Since such probabilities are not known a priori, the first iteration uses SRS among all available alternatives. The sampling scheme is implemented here for a variety of simulated MNL and mixed-MNL data sets, with results suggesting that the new sampling scheme provides substantial efficiency benefits. Thanks to reductions in estimation error, parameter estimates are more accurate, on average. Moreover, in the mixed MNL case, where SRS produces biased estimates (due to violation of the independence of irrelevant alternatives property), the new sampling scheme appears to effectively eliminate such biases. Finally, it appears that only a single iteration of the new strategy (following the initialization step using SRS) is needed to deliver the strategy’s maximum efficiency gains.     

Operating costs in Norwegian toll companies: a panel data analysis

The objective of this paper is to ease the planning of new toll projects by providing estimates of operating costs, and to help us make better informed decisions about the design of toll collection systems. To do so we use panel data for Norwegian toll companies to estimate average cost functions. The main results can be summarised as follows. We provide evidence of very important unexploited economies of scale. The estimated cost curves are very steep for traffic levels below the sample mean, and become almost entirely flat over a wide range above the sample mean. A higher share of vehicles using on board units will significantly reduce average costs. Competitive tendering will significantly reduce average operating costs by as much as 25%. Our results also suggest that increased number of lanes, higher debt and passenger charging will increase average operating costs whereas average operating costs are lower for toll cordons compared with other projects.

Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.     

Are vehicle emission inspection programs living up to expectations?

To ensure that the advanced emission control systems installed on modern motor vehicles continue to work properly, motor vehicle inspection and maintenance (I/M) programs are now found in the major cities of many countries around the world. These programs are widely regarded as valuable and even essential to the achievement of air quality objectives, but there have been few ex post audits of these programs. In this paper, we examine the performance of one of the most sophisticated I/M programs, the USEPA’s Enhanced I/M Program. This program has now been implemented in five states. Using data from 1995 and 1996, we estimate the cost of the Arizona Enhanced I/M Program and the emission reductions achieved. We begin by enumerating briefly the components of I/M costs and discuss their size and incidence. Then we describe the empirical information from Arizona and how we use it to construct cost estimates for both vehicle inspection and repair of failing vehicles. Inspection costs include the costs of operating the test stations and the costs motorists incur in time and money to get to the station and go through the testing process. We find that the inspection costs account for over two-thirds of the full costs of I/M, while costs associated with actual vehicle repair account for only one-third. We conclude by comparing the empirical estimates of costs and program effectiveness in the Arizona program with the ex ante estimated Enhanced I/M program costs made by the EPA in the 1992 Regulatory Impact Analysis (RIA). The ex ante EPA analysis appears to have underestimated the costs of achieving the ambitious reductions in emissions hoped for under I/M.     

A nested logit model of automobile holdings for one vehicle households

This paper presents a model of automobile choice by single vehicle households. This effort is distinguished from previous disaggregate automobile holdings models primarily by the use of the nested logit model rather than the more restrictive multinomial logit model. We present a 2-step estimation technique that provides consistent and asymptotically efficient parameter estimates, yet is tractable for very large choice sets. Using disaggregate data on 237 one-vehicle households we estimate the unknown parameters on an automobile choice model containing 785 individual makes, models and vintages of passenger vehicles.     

Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)

Discrete choice modeling is widely applied in transportation studies. However, the need to consider correlation between observations creates a challenge. In spatial econometrics, a spatial lag term with a pre-defined weight matrix is often used to capture such a correlation. In most previous studies, the weight matrix is assumed to be exogenous. However, this assumption is invalid in many cases, leading to biased and inconsistent parameter estimates. Although some attempts have been made to address the endogenous weight matrix issue, none has focused on discrete choice modeling. This paper fills an existing gap by developing a Spatial Autoregressive Binary Probit Model with Endogenous Weight Matrix (SARBP-EWM). The SARBP-EWM model explicitly considers the endogeneity by using two equations whose error terms are correlated. Markov Chain Monte Carlo (MCMC) method is used to estimate the model. Model validation with simulated data shows that the model parameters can converge to their true values and the endogenous weight matrix can be reliably recovered. The model is then applied to a simplified firm relocation choice problem, assuming that similar size firms influence one another. The model quantifies the peer effect, and takes into consideration other independent variables including industry type and population density. The estimation results suggest that peer influence among firms indeed affect their relocation choices. The application results offer important insights into business location choice and can inform future policy making. The sample size for applying the model is currently limited to hundreds of observations. This paper contributes to the existing literature on discrete choice modeling and spatial econometrics. It provides a new tool to discover spatial correlations that are hidden in a wide range of transportation issues, such as land development, location choice, and various travel behavior. Those hidden spatial correlations are otherwise difficult to identify and estimation results may be biased. Establishing a new model that explicitly considers endogenous weight matrix and applying the model to a real life transportation issue represent a significant contribution to the body of literature.     

Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so,how do we deal with it?

The extent to which Stated Choice (SC) experiments suffer from hypothetical bias continues to be a controversial topic in the literature. This research provides further evidence in this debate by examining the existence of hypothetical bias in a transport-related SC experiment. Data for this research were sourced from a University of Sydney study exploring the effect of exposure-based charging on motorist behaviour. The sample included 148 Sydney motorists who were recruited to take part in the 10-week GPS driving field study (Revealed Preference/RP data). In addition, participants were also required to complete an SC survey which was designed to mimic the RP decision context in order to capture what participants indicated they would do as opposed to what participants actually did in reaction to the charging regime.The current state of practice for measuring hypothetical bias in the literature is to compare aggregate differences in model outcomes using SC and RP data sources. Aggregate analysis is limited in its scope and does not allow for the calculation of the prevalence of hypothetical bias (i.e., how many participants are affected by hypothetical bias). This research is uniquely structured to allow for individual categorisation of hypothetical bias by comparing SC and RP data from the same sample for the direct purpose of investigating the prevalence of hypothetical bias. Furthermore, the extent to which mitigation techniques (cheap talk and certainty scales) influence hypothetical bias is also explored. The findings from this research show that the SC model estimates are prone to hypothetical bias and that the mitigation techniques have potential to compensate for this inherent bias.