In this paper, we investigate the role consideration of the alternatives plays in mode choice models. On the Rome-Milan corridor, in Italy, where seven alternative modes of transport are available, we administered a stated choice (SC) experiment. Responses to supplementary questions on consideration of the different modes of transport and the presence of thresholds for the travel time attribute indicate travellers are less likely to consider the slower modes. Two model specifications, in which consideration for the slower alternatives is measured using both sets of supplementary questions, are proposed and contrasted against a model which assumes all alternatives are considered. Our results suggests that some of the unobserved preference heterogeneity could potentially be due to consideration effects. Accounting for consideration of alternatives also has direct impacts on choice probabilities, parameter estimates and willingness-to-pay measures.
Capurso, M., Hess, S. & Dekker, T. (2019), Modelling the role of consideration of alternatives in mode choice: An application on the Rome-Milan corridor. Transportation Research Part A, 129, November 2019, Pages 170-184.
A considerable amount of studies in the transport literature is aimed at understanding the behavioural processes underlying travel choices, like mode and destination choices. In the present work, we propose the use of evolutionary game theory as a framework to study commuter mode choice. Evolutionary game models work under the assumptions that agents are boundedly rational and imitate others’ behaviour. We examine the possible dynamics that can emerge in a homogeneous urban population where commuters can choose between two modes, private car or public transport. We obtain a different number of equilibria depending on the values of the parameters of the model. We carry out comparative-static exercises and examine possible policy measures that can be implemented in order to modify the agents’ payoff, and consequently the equilibria of the system, leading society towards more sustainable transportation patterns.
Calastri, C., Borghesi, S., & Fagiolo, G. (2019). How do people choose their commuting mode? An evolutionary approach to travel choices. Economia Politica, 36(3), 887-912.
In many countries of the developing world, it is difficult to conduct large-scale household travel surveys to collect data for travel behaviour model estimation and application. This paper focuses on two candidate solutions to the problem: (1) developing models that can be applied for prediction using secondary data collected for other purposes and include socio-demographic information but do not include transport specific information such as the car and/or transit pass ownership (e.g. census, public health records, etc.), (2) ‘borrowing’ a model developed using data from a similar city within the same region. In the first approach, we investigate the feasibility of developing car trip generation models which imputes the car ownership variable with estimated car ownership propensities. The proposed framework is applied in two East African cities, Nairobi and Dar-es-Salaam. The estimation results indicate that for both cities the proposed approach outperforms the models that exclude the car ownership variable. In the second approach, we investigate the spatial transferability of the models developed in the first approach between the two cities to evaluate if it is justified to apply models from one developing country to another in the absence of local models. Results indicate that though some of the estimated parameters are not significantly different from each other between the two cities, statistical tests do not support direct transferability of all the models from Nairobi to Dar-es-Salaam or vice versa. However, interestingly, the simpler model (which excludes car-ownership) outperforms the model with imputed car ownership propensity in terms of transferability. These findings provide useful insights into the development of trip generation models under data constraints which can practically be very useful for developing countries.
Bwambale, A., Choudhury, C. F., & Sanko, N. (2019). Car Trip Generation Models in the Developing World: Data Issues and Spatial Transferability. Transportation in Developing Economies, 5(2), 10.
Household residential relocation can happen at different scales - local, regional national and international. The impacts of the different scales of residential relocation is likely to have varying impacts on mid-term (e.g. car or transit pass ownership) and day-to-day mobility decisions (e.g. mode choice for a specific trip for example). These mobility changes can be of different levels as well. For example, there are differences between the decision to transition from owning no car to one car and from one car to two cars. Identifying which factors affect the different magnitudes of mobility changes and quantifying the impact of various scales of residential relocation on these changes are crucial to better understanding of travel behaviour. The present study uses discrete choice models on revealed preference data to address these research questions. To complement the travel behaviour models, a residential relocation model has also been developed to predict the probability of a household to stay in the current location vs. to move locally, regionally or nationally at a given point of time. Given that the residential relocations are rare events, the British household panel survey (BHPS) spanning 18 years has been used to model the choices made by the same households in terms of residential relocation, car ownership and commute mode of the household head. Our results indicate that sociodemographic characteristics, travel behaviour and life events of the households have a significant effect on relocation, car ownership and commute mode choice. As expected, the parameters of the car ownership and commute mode choice models vary significantly with the type of relocation. Further, the socio-demographic factors and life-events also have a varying impact on the scale of relocation. The residential relocation, car ownership and commute mode choice models developed in this research can be used to better predict the medium and long term changes in travel behaviour over course of time.
Haque, Md B., Choudhury, C.F., Hess, S. & Crastes dit Sourd, R. (2019), Modelling residential mobility decision and its impact on car ownership and travel mode. Travel Behaviour and Society, Volume 17, October 2019, Pages 104-119.
Small and Rosen’s (Econometrica 49(1):105–130, 1981) method for measuring consumer surplus using discrete choice models has been widely adopted in public policy analysis. For the case of a price change, the present paper elucidates five theoretical assumptions inherent within Small and Rosen’s measure, and employs indifference maps to demonstrate that this measure is only applicable to the context of a single discrete choice free of non-linear income effects. The paper argues that, where non-linear income effects are present, the aforementioned theoretical assumptions should be relaxed, and the consumption context revised from discrete choice to discrete–continuous demand. Furthermore, the paper proposes a simple analytical method for approximating the expected Hicksian compensating variation in the presence of non-linear income effects, and compares the empirical performance of this method against existing methods using data from Morey et al. (Am J Agric Econ 75(3):578–592, 1993). As well as offering a simple approximation, the proposed method yields insights on the potential range of the compensating variation depending on the extent of switching between choice alternatives, and on the attribution of the compensating variation to the relevant choice alternatives.
Batley, R., & Dekker, T. (2019). The intuition behind income effects of price changes in discrete choice models, and a simple method for measuring the compensating variation. Environmental and Resource Economics, 1-30.
Despite the increasing scholarly attention that Payments for Ecosystem Services (PES) are receiving, little is yet known about the process of price setting. This is key knowledge that relates directly to the economic efficiency of an instrument that is spreading widely worldwide. Through a meta-analysis of payments for forest watershed services in Latin America, this study finds that there exists a very substantial difference between the price that buyers pay and that sellers receive for ecosystem services and that this difference is not due to transaction costs. Instead, it reveals a substantial subsidising component. Our results would suggest that this discrepancy in prices might be due to the ‘start-up’ effect and that as programmes mature, this effect may attenuate. However, the entry of new buyers does not make over for the subsidization of schemes and would require the implementation of specific mechanisms to adjust prices. According to our results, one of such possible mechanisms would be to increase participation on price setting processes, allowing for more price negotiation between parties rather than the predominant top-down approach.
Martin-Ortega, J., Dekker, T., Ojea, E., & Lorenzo-Arribas, A. (2019). Dissecting price setting efficiency in Payments for Ecosystem Services: A meta-analysis of payments for watershed services in Latin America. Ecosystem Services, 38, 100961.
Dementia-specific and proxy-completed preference-based measures have been proposed for use in intervention studies involving people living in residential care, in instances where generic, self-reported preference-based measures have been deemed inappropriate.
This study was conducted to investigate the construct validity, criterion validity, and responsiveness of DEMQOL-Proxy-U and of self- and proxy-completed EQ-5D-5L.
The analysis used a 3-wave, individual-level data set of 1004 people living with dementia in residential care that included self-completed EQ-5D-5L and formal-carer and informal-carer proxy-completed EQ-5D-5L and DEMQOL-Proxy-U utility values, in addition to other nonutility cognitive measures (Functional Assessment Staging [FAST], Clinical Dementia Rating [CDR], Cohen-Mansfield Agitation Inventory [CMAI]) and health-related quality of life (HRQOL) measures (nursing home version of the Quality of Life with Alzheimer's disease scale [QOL-AD-NH], Quality of Life in Late-Stage Dementia [QUALID] scale). Construct validity, criterion validity, and responsiveness were assessed using correlation, Bland-Altman plots, and panel data regression models.
Self-completed EQ-5D-5L failed to reflect clinically important differences and changes in FAST, CDR, and CMAI but did capture the resident's own view of HRQOL (QOL-AD-NH). As dementia severity increased, collection of EQ-5D-5L-proxy and DEMQOL-Proxy-U data was more feasible than collection of self-completed EQ-5D-5L. These formal-carer and informal-carer proxy measures also better reflected changes in FAST, CDR, and CMAI but did not capture the resident's own view of HRQOL (QOL-AD-NH), despite adequately capturing the proxy's own view of the resident's HRQOL (QUALID). This indicates discrepancies between a proxy's view and resident's view of the impact that tangible declines in health, cognition, or functional abilities have on HRQOL. The EQ-5D-5L-proxy and DEMQOL-Proxy-U were generally poor substitutes. Regardless of which proxy completed it, the EQ-5D-5L-proxy was typically more responsive than the DEMQOL-Proxy-U to changes in CDR, FAST, and CMAI, indicating that use of the DEMQOL-Proxy-U is not always justified.
Disparities in the measurement properties of different utility measures mean that choices about how to measure utility in trials could affect economic evaluation outcomes and hence how resources are allocated for dementia care.
Martin, A., Meads, D., Griffiths, A. W., & Surr, C. A. (2019). How Should We Capture Health State Utility in Dementia? Comparisons of DEMQOL-Proxy-U and of Self-and Proxy-Completed EQ-5D-5L. Value in Health.
Car-following models, which are used to predict the acceleration-deceleration decisions of drivers in the presence of a closely spaced lead vehicle, are critical components of traffic microsimulation tools and useful for safety evaluation. Existing car-following models primarily account for the effects of surrounding traffic conditions on a driver’s decision to accelerate or decelerate. However, research in human factors and safety has demonstrated that driving decisions are also significantly affected by individuals’ characteristics and their emotional states like stress, fatigue, etc. This motivates us to develop a car-following model where we explicitly account for the stress level of the driver and quantify its impact on acceleration-deceleration decisions. An extension of the GM stimulus-response model framework is proposed in this regard, where stress is treated as a latent (unobserved) variable, while the specification also accounts for the effects of drivers’ sociodemographic characteristics. The proposed hybrid models are calibrated using data collected with the University of Leeds Driving Simulator where participants are deliberately subjected to stress in the form of aggressive surrounding vehicles, slow leaders and/or time pressure while driving in a motorway setting. Alongside commonly used variables, physiological measures of stress (i.e. heart rate, blood volume pulse, skin conductance) are collected with a non-intrusive wristband. These measurements are used as indicators of the latent stress level in a hybrid model framework and the model parameters are estimated using Maximum Likelihood Technique. Estimation results indicate that car-following behaviour is significantly influenced by stress alongside speed, headway and drivers’ characteristics. The findings can be used to improve the fidelity of simulation tools and designing interventions to improve safety.
Paschalidis, E., Choudhury, C.F. & Hess, S. (2019), Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour. Analytic Methods in Accident Research, 22, 100089.
Cost-Benefit Analysis (CBA) is a widely applied economic appraisal tool to support the planning and decision-making process for transport projects. However, in the planning literature CBA has been criticized for at least three reasons: 1) CBA focuses on traditional transport system related planning goals and poorly considers the broader goals of urban transport planning such as social equity; 2) CBA corrodes and degrades the forward looking nature of the planning proficiency. The instrument can be conceived as a backward looking methodology as it assumes that people’s past decisions in a (private) market setting reflect their normative ideas regarding their preferred future urban mobility system; 3) CBA fails to recognize the specific (local) features of the problem which a transport project aspires to solve as practical CBA studies use generic price tags to value impacts of a transport project. Participatory Value Evaluation (PVE) is a novel evaluation approach specifically designed to overcome these criticisms while preserving the positive aspects that CBA brings to planning. This paper illustrates the PVE method with a case study on the evaluation of a transport investment scheme of the Transport Authority Amsterdam. In total 2,498 citizens participated in the PVE. We find that projects with the highest social value focus on safety and improvements for cyclists and pedestrians, whereas projects that focus on reducing travel times for car users have lower value. Moreover, we establish that PVE captures citizens’ preferences towards broader goals of transport planning such as improving health and the environment, fostering city cycling as well as the inclusion of ethical considerations such as spatial equality. PVE also allows for the inclusion of citizens’ normative ideas regarding their preferred future urban mobility system and local characteristics of the transport problem/solution.
Mouter, N., Koster, P., & Dekker, T. (2019). Participatory Value Evaluation: a novel method to evaluate future urban mobility investments. Tinbergen Institute Discussion Paper 2019-046/VIII
Choice set generation is a challenging aspect of disaggregate level residential location choice modelling due to the large number of candidate alternatives in the universal choice set (hundreds to hundreds of thousands). The classical Manski method (Manski, 1977) is infeasible here because of the explosion of the number of possible choice sets with the increase in the number of alternatives. Several alternative approaches have been proposed in recent years to deal with this issue, but these have limitations alongside strengths. For example, the Constrained Multinomial Logit (CMNL) model (Martínez et al., 2009) offers gains in efficiency and improvements in model fit but has weaknesses in terms of replicating the Manski model parameters. The rth-order Constrained Multinomial Logit (rCMNL) model (Paleti, 2015) performs better than the CMNL model in producing results consistent with the Manski model, but the benefits disappear when the number of alternatives in the universal choice set increases. In this study, we propose an improved CMNL model (referred to as Improved Constrained Multinomial Logit Model, ICMNL) with a higher order formulation of the CMNL penalty term that does not depend on the number of alternatives in the choice set. Therefore, it is expected to result in better model fit compared to the CMNL and the rCMNL model in cases with large universal choice sets. The performance of the ICMNL model against the CMNL and the rCMNL model is evaluated in an empirical study of residential location choices of households living in the Greater London Area. Zone level models are estimated for residential ownership and renting decisions where the number of alternatives in the universal choice set is 498 in each case. The performance of the models is examined both on the estimation sample and the holdout sample used for validation. The results of both ownership and renting models indicate that the ICMNL model performs considerably better compared to the CMNL and the rCMNL model for both the estimation and validation samples. The ICMNL model can thus help transport and urban planners in developing better prediction tools.
Haque, M.D., Choudhury, C.F. & Hess, S. (2019), Modelling Residential Location Choices with Implicit Availability of Alternatives. Journal of Transport and Land Use, 12(1), pp. 597–618.
This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium for the UK Department for Transport (DfT). The paper summarises recommendations for revised national average values of in-vehicle travel time savings, reliability and time-related quality (e.g. crowding and congestion), which were developed using willingness-to-pay (WTP) methods, for a range of modes, and covering both business and non-work travel purposes. The paper examines variation in these values by characteristics of the traveller and trip, and offers insights into the uncertainties around the values, especially through the calculation of confidence intervals. With regards to non-work, our recommendations entail an increase of around 50% in values for commute, but a reduction of around 25% for other non-work—relative to previous DfT ‘WebTAG’ guidance. With regards to business, our recommendations are based on WTP, and thus represent a methodological shift away from the cost saving approach (CSA) traditionally used in WebTAG. These WTP-based business values show marked variation by distance; for trips of less than 20 miles, values are around 75% lower than previous WebTAG values; for trips of around 100 miles, WTP-based values are comparable to previous WebTAG; and for longer trips still, WTP-based values exceed those previously in WebTAG.
Batley, R.P., Bates, J., Bliemer, M., Börjesson, M., Bourdon, J., Ojeda Cabral, M., Chintakayala, P.K., Choudhury, C., Daly, A.J., Dekker, T., Drivyla, E., Fowkes, A., Hess, S., Heywood, C., Johnson, D., Laird, J., Mackie, P., Parkin, J., Sanders, S., Sheldon, R., Wardman, M. & Worsley, T. (2019), New appraisal values of travel time saving and reliability in Great Britain. Transportation, 46, Issue 3, pp 583–621.
Environmental effects of transport projects have a weak position in Cost-Benefit Analysis (CBA) which might be rooted in the valuation approach adopted in the dominant style of CBA. This conventional valuation approach has been criticized for not valuing positive and negative impacts of transport projects in relation to each other and for not valuing such impacts in a public context, but in the context of private decisions. These critiques might be circumvented through valuing transport projects in a social choice context in which overall burdens and benefits of proposed transport projects are considered together in a public context. We investigate the extent to which a social choice valuation approach produces different outcomes than a conventional valuation approach. We conducted four social choice valuation experiments in which respondents were asked to choose between alternatives for a new road, trading off travel time and three environmental impacts (noise, recreation and biodiversity). Our findings suggest that, under social choice valuation, individuals assign substantially more value to environmental impacts than travel time as compared to conventional valuation studies. Moreover, in a social choice setting, respondents assigned monetary values to impacts that are not (or only qualitatively) considered in conventional CBAs of transport projects.
Mouter, N., Cabral, M. O., Dekker, T., & van Cranenburgh, S. (2019). The value of travel time, noise pollution, recreation and biodiversity: A social choice valuation perspective. Research in Transportation Economics (in press)
The growing mobile phone penetration rates have led to the emergence of large-scale call detail records (CDRs) that could serve as a low-cost data source for travel behaviour modelling. However, to the best of our knowledge, there is no previous study evaluating the potential of CDR data in the context of route choice behaviour modelling. Being event-driven, the data are discontinuous and only able to yield partial trajectories, thus presenting serious challenges for route identification. This paper proposes techniques for inferring the users' chosen routes or subsets of their likely routes from partial CDR trajectories, as well as data fusion with external sources of information such as route costs, and then adapts the broad choice framework to the current modelling scenario. The model results show that CDR data can capture the expected travel behaviour and the derived values of travel time are found to be realistic for the study area.
Bwambale, A., Choudhury, C.F. & Hess, S. (2019), Modelling long-distance route choice using mobile phone call detail record data: A case study of Senegal. Transportmetrica A, 15(2), pp. 1543-1568.
In health, stated preference data from discrete choice experiments (DCEs) are commonly used to estimate discrete choice models that are then used for forecasting behavioral change, often with the goal of informing policy decisions. Data from DCEs are potentially subject to hypothetical bias. In turn, forecasts may be biased, yielding substandard evidence for policymakers. Bias can enter both through the elasticities as well as through the model constants. Simple correction approaches exist (using revealed preference data) but are seemingly not widely used in health economics. We use DCE data from an experiment on smokers in the US. Real-world data are used to calibrate the scale of utility (in two ways) and the alternative-specific constants (ASCs); several innovations for calibration are proposed. We find that embedding revealed preference data in the model makes a substantial difference to the forecasts; and that how models are calibrated also makes a substantial difference.
Buckell, J. & Hess, S. (2019), Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data. Journal of Health Economics, forthcoming.
To determine the extent to which the current care pathway in hypodontia promotes shared decision-making (SDM).
Exploratory cross-sectional study using qualitative methods.
Orthodontic department of two NHS teaching hospitals in Yorkshire.
Young people aged 12–16 years with hypodontia of any severity and at any stage of treatment, and their parents and guardians.
(1) Observation and audio-recording of interdisciplinary consultation in hypodontia clinics (n = 5) without any researcher interference; (2) short, structured interviews with young people with hypodontia (n = 8) and their parent (n = 8) using a topic guide to explore themes around decision-making. Audio-recordings were transcribed and analysed using a thematic framework.
Consultations were used as an opportunity for interdisciplinary discussion, information provision and treatment planning. Evidence of good communication was observed but patient engagement was low. The decision to be made was usually stated and treatment options discussed, but time constraints limited the scope for adequate information exchange and assessment of understanding. No methods were used to establish patient and family preferences or values. Interviews suggested parents expect the dental team to make decisions and young people rely on parental advocacy. Despite little evidence of SDM, participants reported satisfaction with their treatment.
The current care pathway for hypodontia does not support clinicians in the steps of SDM. Recommendations for improving SDM processes include support to identify preference-based decisions, greater access to comprehensive and accessible patient information to enable preparation for consultation, alternative methods for effective communication of complex information and use of preference elicitation tools to aid value-driven decision-making.
Barber, S., Pavitt, S., Meads, D., Khambay, B., & Bekker, H. (2019). Can the current hypodontia care pathway promote shared decision-making?. Journal of orthodontics, 46(2), 126-136.
Cost-Benefit Analysis (CBA) for public policies assumes ‘consumer sovereignty’, implying that impacts of government projects can be expressed in monetary terms by aggregating individuals’ willingness to pay. However, individuals’ willingness to pay might not accurately reflect preferences towards public policies. Participatory Value Evaluation (PVE) is a novel evaluation framework specifically designed to rectify this issue by going beyond the paradigm of ‘consumer sovereignty’. PVE infers the social welfare effects of public policies through eliciting individuals’ preferences over the allocation of public budgets (‘citizen sovereignty’) as well as their private income (‘consumer sovereignty’). In a PVE, individuals are asked to choose the best portfolio of projects with corresponding impacts for society and themselves subject to
governmental and private budget constraints. This paper positions PVE relative to past innovations in applied welfare economics and illustrates the potential of the approach through a case study on projects to mitigate flood risks at locations along the Dutch river ‘Waal’. In total 2,900 citizens participated in this PVE. The main result of the case study is that citizens have a preference for projects that combine strengthening dikes and giving the river space to flood safely, particularly when such projects positively influence biodiversity and recreational opportunities.
Mouter, N., Koster, P., & Dekker, T. (2019). An introduction to Participatory Value Evaluation. Tinbergen Institute Discussion Paper 2019-024/V
Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. Our findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice.
Calastri, C., Hess, S., Choudhury, C.F., Daly, A.J. & Gabrielli, L. (2019), Mode choice with latent availability and consideration: theory and a case study. Transportation Research Part B, 123, pp. 374-385.
Traditional approaches to trip generation modelling rely on household travel surveys which are expensive and prone to reporting errors. On the other hand, mobile phone data, where spatio-temporal trajectories of millions of users are passively recorded has recently emerged as a promising input for transport analyses. However, such data has primarily been used for the development of human mobility models, extraction of statistics on human mobility behaviour, and origin-destination matrix estimation as opposed to the development of econometric models of travel demand. This is primarily due to the exclusion of user demographics from mobile phone data made available for research (owing to privacy reasons). In this study, we address this limitation by proposing a hybrid trip generation model framework where demographic groups are treated as latent or unobserved. The proposed model first predicts the demographic group membership probabilities of individuals based on their phone usage characteristics and then uses these probabilities as weights inside a latent class model for trip generation, with different classes representing different socio-demographic groups. The model is calibrated using the call log data of a sub-sample of users with known demographics and trip rates extracted from their GSM mobility data. The performance of the hybrid model is compared with that of a traditional trip generation model which uses observed demographic variables to validate the proposed methodology. This comparative analysis shows that the model fit and the prediction results of the hybrid model are close to those of the traditional model. The research thus serves as a proof-of-concept that the mobile phone data can be successfully used to develop econometric models of transport planning by having additional information for a subset of the users.
Bwambale, A., Choudhury, C.F. & Hess, S. (2019), Modelling trip generation using mobile phone data: a latent demographics approach. Journal of Transport Geography, 76, Pages 276-286.
It is common practice to build Stated Preference (SP) attributes and alternatives from observed Revealed Preference (RP) choices with a view to increasing realism. While many surveys pivot all alternatives around an observed choice, others use more adaptive approaches in which changes are made depending on what alternative was chosen in the RP setting. For example, in SP-off-RP data, the alternative chosen in the RP setting is worsened in the SP setting and other alternatives are improved to induce a change in behaviour. This facilitates the creation of meaningful trade-offs or tipping points but introduces endogeneity. This source of endogeneity was largely ignored until Train and Wilson (T&W) proposed a full information maximum likelihood (FIML) solution that can be implemented with simulation. In this article, we propose a limited information maximum likelihood (LIML) approach to address the SP-off-RP problem using a method which does not need simulation, can be applied with standard software and uses data that is already available for the stated problem. The proposed method is an application of the control-function (CF) method to correct for endogeneity in discrete choice models, using the RP attributes as instrumental variables. We discuss the theoretical and practical advantages and disadvantages of the CF and T&W methods and illustrate them using Monte Carlo and real data. Results show that, while the T&W method may be more efficient in theory, it may however fail to retrieve consistent estimators when it does not account properly for the data generation process if, e.g., an exogenous source of correlation among the SP choice tasks exists. On the other hand, the CF is more robust, i.e. less sensitive, to the data generation process assumptions, and is considerably easier to apply with standard software and does not require simulation, facilitating its adoption and the more extensive use of SP-off-RP data.
Guevara, C.A. & Hess, S. (2019), A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train & Wilson. Transportation Research Part B, forthcoming.
Temporal transferability of model parameters is a critical issue, especially in the context of developing countries where data and resources for transport model development are extremely limited. This study investigates the temporal transferability of vehicle ownership models with special emphasis on exploring the effect of model structure on temporal transferability. The performance of potential updating methods for making the models more transferable are also compared. The household survey data collected from Dhaka, Bangladesh in 2005 and 2010 have been used in this regard. Different forms of random utility and count regression models of car, motorcycle, and bicycle ownership have been developed using income and household size, and number of workers, children, and licensed drivers as explanatory variables. The temporal transferability of each model between the two time periods has been compared rigorously using statistical tests. Results indicate that the multinomial logit model has better temporal transferability than the count regression models. In relation to model updating, the combined transfer estimation method for model updating is found to perform better than the Bayesian updating. The findings can provide useful guidance during application of a pre-existing model in the context of a developing country.
Flavia, A., & Choudhury, C. (2019). Temporal transferability of vehicle ownership models in the developing world: case study of Dhaka, Bangladesh. Transportation Research Record, 2673(3), 722-732.
Objectives. Uncontrolled pain in advanced cancer is a common problem and has significant impact on individuals’ quality of life and use of healthcare resources. Interventions to help manage pain at the end of life are available, but there is limited economic evidence to support their wider implementation. We conducted a case study economic evaluation of two pain selfmanagement interventions (PainCheck and Tackling Cancer Pain Toolkit [TCPT]) compared with usual care.
Methods. We generated a decision-analytic model to facilitate the evaluation. This modelled the survival of individuals at the end of life as they moved through pain severity categories. Intervention effectiveness was based on published meta-analyses results. The evaluation was conducted from the perspective of the U.K. health service provider and reported cost per qualityadjusted life-year (QALY).
Results. PainCheck and TCPT were cheaper (respective incremental costs -GBP148 [-EUR168.53] and -GBP474 [-EUR539.74]) and more effective (respective incremental QALYs of 0.010 and 0.013) than usual care. There was a 65 percent and 99.5 percent chance of cost-effectiveness for PainCheck and TCPT, respectively. Results were relatively robust to sensitivity analyses. The most important driver of cost-effectiveness was level of pain reduction (intervention effectiveness). Although cost savings were modest per patient, these were considerable when accounting for the number of potential intervention beneficiaries.
Conclusions. Educational and monitoring/feedback interventions have the potential to be cost-effective. Economic evaluations based on estimates of effectiveness from published meta-analyses and using a decision modeling approach can support commissioning decisions and implementation of pain management strategies.
Meads, D. M., O'Dwyer, J. L., Hulme, C. T., Lopez, R. R., & Bennett, M. I. (2019). Cost-Effectiveness of Pain Management Strategies in Advanced Cancer. International journal of technology assessment in health care, 35(2), 141-149.
With the advent of activity-based modelling, transport planners’ focus has shifted from isolated trips to tours. Tours are series of interconnected trips that start and finish at home. There are different types of tours; we focus on two: hwh (start at home; go to work; and then go back home) and hw+wh (where + represents a non-work activity). Tour types introduce a new dimension to the traditional problem of travel mode choice, as the mode choice might be influenced by the type of tour. This study attempts to measure and compare the relationship between tour type and mode choice using three different modelling approaches: Multinomial Logit (MNL); Nested Logit (NL) and Cross-Nested Logit (CNL). We compare each approach using secondary data from a larger survey: 24-h daily activity patterns of 420 commuters between Bekasi and Jakarta; one of the busiest commuting routes in Indonesia. Among other results, we found that gender and income significantly influence commuter’s choice of mode and that reducing travel time and cost can increase the ridership of public transport. Furthermore, the NL and CNL models showed significant improvement over the simpler MNL when grouping the alternatives based on tour types. This points to a significant influence of the tour type on the mode choice. Policy recommendations to increase traveler’s wellbeing are also formulated.
Bastarianto, F. F., Irawan, M. Z., Choudhury, C., Palma, D., & Muthohar, I. (2019). A Tour-Based Mode Choice Model for Commuters in Indonesia. Sustainability, 11(3), 788.
Our objective was to develop and test a discrete-choice experiment (DCE) survey to elicit adolescent and parent preferences for dental care for hypodontia (a developmental condition where one or more teeth fail to develop).
This was a mixed-methods study. Participants were adolescents (aged 12–16 years) with hypodontia and their parents and the dentists providing hypodontia care. Stage one entailed attribute development, as follows. (1) Attribute identification: systematic review of hypodontia literature; interviews with adolescents with hypodontia (n = 8) and parents (n = 8); observation of hypodontia clinical consultations (n = 5); environmental scan of hypodontia patient information resources (n = 30); and systematic analysis of social media posts (n = 176). (2) Attribute selection: stakeholder consultation to develop items for a questionnaire; rating and ranking questionnaire for adolescents with hypodontia and parents (n = 18); further stakeholder consultation. Stage two involved the development of the DCE survey, and stage three included the pre-testing using cognitive interviews with adolescents (n = 12) and parents (n = 8) to assess face and content validity.
The attribute long list included 27 attributes focusing on service delivery and treatment outcome, from which seven ‘important’ attributes were selected for pre-testing. Cognitive interviewing suggested adolescents found the DCE choice tasks challenging to understand; the survey was modified to enhance its acceptability. One attribute was excluded as it showed poor validity with adolescents. Pre-testing suggested DCE choice tasks encouraged thinking and discussion about preferences for treatment.
Including the target respondent group in all stages of DCE development ensured the final DCE survey was valid and acceptable. DCE methods appear to be a useful tool for exploring joint decision making alongside conventional preference elicitation.
Barber, S., Bekker, H., Marti, J., Pavitt, S., Khambay, B., & Meads, D. (2019). Development of a Discrete-Choice Experiment (DCE) to Elicit Adolescent and Parent Preferences for Hypodontia Treatment. The Patient-Patient-Centered Outcomes Research, 12(1), 137-148.
The choice of which AAC device to provide for a child can have long lasting consequences, but little is known about the decision-making of AAC professionals who make recommendations in this context. A survey was conducted with AAC professionals using best–worst scaling methodology examining what characteristics of children and attributes of AAC devices are considered most important in decision-making. A total of 19 child characteristics and 18 device attributes were selected by the authors from lists generated from literature reviews and from focus groups with AAC professionals, people who use AAC, and other stakeholders. The characteristics and attributes were used to develop two best–worst scaling surveys that were administered to 93 AAC professionals based in the UK. The relative importance of characteristics/attributes was estimated using statistical modelling. Child characteristics related to language and communication, cognitive and learning abilities, and personality traits were generally found to be more important than physical features. Communication, language, and interface-related AAC device attributes were generally more important than hardware and physical attributes. Respondent demographics (e.g., experience, professional background) did not seem to influence the importance assigned to device characteristics or attributes. Findings may inform both future quantitative research into decision-making and efforts to improve decision-making in practice.
Webb, E.J.D., Meads, D., Lynch, Y., Randall, N., Judge, S., Goldbart, J., Meredith, S., Moulam, L., Hess, S. & Murray, J. (2019), What’s Important in AAC Decision Making for Children? Evidence from a Best-worst Scaling Survey. Augmentative and Alternative Communication, 35(2), pp. 80-94.
This paper develops a novel approach to the economic evaluation of public policies: participatory value evaluation (PVE). PVE involves citizens directly in decisions of the government, taking into account governmental and individual budget constraints. Citizens receive reliable information on social impacts and can choose the best portfolio of projects according to their social preferences. This paper develops the economic and econometric theoretical framework for fixed budget and flexible budget PVE experiments which allows us to directly measure the change in social welfare for investments in water infrastructure in The Netherlands.
Dekker, T., Koster, P., & Mouter, N. (2019). The economics of participatory value evaluation. Tinbergen Institute Discussion Paper 2019-008/VIII
Mode choice models traditionally assume that all objectively available alternatives are considered. This might not always be a reasonable assumption, even when the number of alternatives is limited. Consideration of alternatives, like many other
aspects of the decision-making process, cannot be observed by the analyst, and can only be imperfectly measured. As part of a stated choice survey aimed at unveiling air passengers’ preferences for access modes to Bari International Airport in Italy,
we collected a wide set of indicators that either directly or indirectly measure respondents’ consideration of the public transport alternatives. In our access mode choice model, consideration of public transport services was treated as a latent variable, and entered the utility function for this mode through a ‘‘discounting’’ factor. The proposed integrated choice and latent variable approach allows the analyst not only to overcome potential endogeneity and measurement error issues associated with the indicators, but also makes the model suitable for forecasting. As a result of accounting for consideration effects, we observed an improvement in fit that also held in a validation sample; moreover, the effects of policy changes aimed at improving the modal share of public transport were considerably reduced.
Bergantino, A.S., Capurso, M., Dekker, T. & Hess, S. (2019), Allowing for heterogeneity in the consideration of airport access modes: the case of Bari airport, Transportation Research Record, 2673 (8), pp. 50-61.
This paper presents the estimation of a discrete freight transport chain choice model for Europe, which was developed for the European Union as part of the Transtools 3 project. The model describes nine different multi- and single mode chain alternatives of which three can be either container or non-containerised, and it segments freight into dry bulk, liquid bulk, containers and general cargo. The model was estimated on the basis of disaggregate data at the shipment level (Swedish CFS and French ECHO data). Several transport costs specifications and nesting structures were tested and elasticities compared with reference literature. It was found that freight models are characterised by heterogeneity, non-linearity in transport costs and hence Value of Times and non-constant rates of substitution. Not taking these elements into account will have consequences for the evaluation of transport policies using the freight transport model.
Jensen, A. F., Thorhauge, M., de Jong, G., Rich, J., Dekker, T., Johnson, D., ... & Nielsen, O. A. (2019). A disaggregate freight transport chain choice model for Europe. Transportation Research Part E: Logistics and Transportation Review, 121, 43-62.
Numerous cities around the world are considering the implementation of road pricing to ease urban traffic congestion, following on from the success in cities such as London and Singapore. However, policy-makers are also all too aware of the generally negative public opinion toward such measures. This study makes use of data collected in four cities (two in Sweden, one in Finland, and one in France) using a very consistent survey probing for citizens’ attitudes toward pricing. We find very strong similarities across the four cities in terms of a number of underlying attitudinal constructs that help explain people’s answers in a hypothetical referendum on congestion pricing. The similarities across cities indicate that the increase in the opinion toward congestion pricing once they are introduced is not primarily an effect of changes in underlying attitudes, changes in how the underlying attitudes influence the support for congestion pricing, or differences in anticipated versus experienced or perceived self-interest. Instead, this effect seems to be caused by a status quo acceptance, tending to increase the support for the current situation.
Hess, S. & Börjesson, M. (2019), Understanding attitudes towards congestion pricing: a latent variable investigation with data from four cities. Transportation Letters, 11(2), pp. 63-77.
Pedestrians and freeways are not supposed to coexist in any proximity to each other, yet in Cape Town, South Africa, the Freeway Management System (FMS) has recorded an alarming increase in pedestrian activity on its freeways in recent years, with a similar trend in (fatal) freeway pedestrian crashes. This paper reports on the development of a series of discrete choice models for pedestrian crossing decisions in a highly volatile and vulnerable freeway crossing environment. We hypothesize that freeway pedestrian crossing is driven by personal factors and the perceived risks associated with completing the crossing using a footbridge or (illegally) at-grade. We test this assumption by making use of stated choice and risk perception data collected from (adult) participants intercepted along three Cape Town freeways. Estimating mixed logit and hybrid choice models we look at the role of random heterogeneity and latent risk perception in the choice to cross at-grade or using a footbridge. The model estimates confirm that, as expected, crossing choice is largely influenced by a combination of built environment, vehicular and pedestrian traffic, next to some socio-demographic factors, but also risk perception. The study brings to light the seemingly opposite effect of some of the factors on risk perception and crossing choice. We also show that risk perception really only influences crossing choice in terms of the perception of at-grade crossing risk, where the impact is however non-trivial. Finally, we look at the implied relative sensitivities of the choice attributes within and between the crossing alternatives, as well between the three estimated models, amongst others demonstrating the power of the hybrid choice model over the other two. The results of the study can inform opportunities to counter the upward trend of fatalities and provide suggestions for policy-making that would lead to improved freeway crossing safety.
Dada, M., Zuidgeest, M. & Hess, S. (2019), Modelling pedestrian crossing choice behaviour on Cape Town freeways: caught between a rock and a hard place? Transportation Research Part F, 60, pp. 245-261.
The value of travel time (VTT) can be said to be the most important number in transport economics, and its estimation has been the topic of extensive academic and applied work. Numerous papers use the term “value of travel time savings”, or VTTS. The addition of the word “savings” has not arisen suddenly but goes back to the 1970s, and has also been used in the titles of national studies. The addition
of ‘savings’ is in our view incorrect, misleading and unhelpful. Unlike money, time cannot be stored or borrowed – there is no piggy bank for spare minutes. In addition, the modelling approaches used for many of the more advanced VTT studies in fact produce valuations that are ‘bracketed' between gains
and losses in time, and an average between these gains and losses, typically the geometric mean, is then used as the VTT. It is then clear that the value obtained from this averaging cannot be described as the value of time savings (or reductions), as it includes the higher value of losses (i.e. increases) as well. To exemplify the magnitude of our theoretical points, we show how for the 2015 UK VTT study, using
the bracketed value for commuters and labelling it as a VTTS implies an overestimation by a factor of more than 2.
Daly, A. & Hess, S. (2019), VTT or VTTS: a note on terminology for value of travel time work, Transportation, forthcoming.
Discrete choice models are a key technique for estimating the value of travel time (VTT). Often, stated choice data are used in which respondents are presented with trade-offs between travel time and travel cost and possibly additional attributes. There is a clear possibility that some respondents experience time constraints, leaving some of the presented options unfeasible. A model not incorporating information on these constraints would explain choices for faster and more expensive options as an indication that those respondents have a higher VTT when in reality they may be forced to select the more expensive option as a result of their personal constraints. This paper puts forward the hypothesis that this can have major impacts on findings in terms of heterogeneity in VTT measures. This paper examines via simulation the bias in VTT estimates and especially preference heterogeneity when such constraints are (not) accounted for. Empirical evidence is provided that preference heterogeneity is confounded with the travel budget impact on the availabilities of alternatives, and it is shown that there is a risk of producing biased estimates for appraisal VTT if studies do not explicitly model choice set formation. The inclusion of an opt-out alternative could be an effective measure to reduce the bias. This paper also explores the potential use of non-linear functional forms to capture the time budget impacts.
Tjiong, J., Hess, S., Dekker, T. & Ojeda-Cabral, M. (2018), Impact of travel time constraints on taste heterogeneity and non-linearity in simple time-cost trade-offs, Transportation Research Record, 2672(49) 135–145.
Identification and appraisal of the outcome measures that have been used to evaluate hypodontia treatment and deliver services are essential for improving care. A lack of alignment between outcomes and patient values can limit the scope for patient-centered care. Our objectives were to identify and appraise the outcomes selected to evaluate hypodontia care.
Data sources included 10 electronic databases and grey literature, searched using terms for hypodontia and its treatment methods. Study eligibility included mixed study designs to ensure comprehensive identification of outcomes, excluding case reports and case series with fewer than 10 participants and nonsystematic reviews. Participants and interventions involved people with hypodontia receiving any dental treatment to manage their hypodontia. Simulated treatment, purely laboratory-based interventions, and future treatments still in development were excluded. Research outcomes were identified and synthesised into 4 categories: clinical indicators, and patient-reported, clinician-reported, and lay-reported outcomes. No synthesis of efficacy data was planned, and consequently no methodologic quality appraisal of the studies was undertaken.
The search identified 497 abstracts, from which 106 eligible articles were retrieved in full. Fifty-six studies and 8 quality-improvement reports were included. Clinical indicators were reported in 49 studies (88%) including appearance, function, dental health, treatment longevity, treatment success and service delivery. Patient-reported outcomes were given in 22 studies (39%) including oral health-related quality of life, appearance, function, symptoms of temporomandibular dysfunction, and patient experience. Clinician-reported outcomes were limited to appearance. Variability was seen in the tools used for measuring outcomes.
There is a lack of rationale and consistency in the selection of outcome measures used to evaluate hypodontia care. Outcomes are largely clinician and researcher-driven with little evidence of their relevance to patients. There was a paucity of outcomes measuring access to care, quality of care, and cost. Evidence from hypodontia research is clinician-focused and likely to have limited value to support patients during decision making. Attempts to synthesise the evidence base for translation into practice will be challenging. There is a need for a core outcomes set with a patient-centric approach to drive improvements in health services.
Barber, S., Bekker, H. L., Meads, D., Pavitt, S., & Khambay, B. (2018). Identification and appraisal of outcome measures used to evaluate hypodontia care: A systematic review. American Journal of Orthodontics and Dentofacial Orthopedics, 153(2), 184-194.
Recent work in transport research has increasingly tried to broaden out beyond traditional areas such as mode choice or car ownership and has tried to position travel decisions within the broader life context. However, while important progress has been made in terms of how to capture these additional dimensions, both in terms of detailed tracking of movements and in-depth data collection of long term decisions or social network influences, surveys have tended to look at only a handful (or often one) of these issues in isolation, especially at the data collection end. Making these links is the key aim of the data collection described in this paper. We conducted a comprehensive survey capturing respondents’ travel, energy and residential choices, their social environment, life history and short-term travel patterns. The survey is composed of a detailed background questionnaire, a life-course calendar and a name generator and name interpreter. Participants were also required to use a smartphone tracking app for 2-weeks. We believe that this is an unprecedented effort that joins complexity of the survey design, amount of information collected and sample size. The present paper gives a detailed overview of the different survey components and provides initial insights into the resulting data. We share lessons that we have learned and explain how our decisions in terms of specification were shaped by experiences from other data collections.
Calastri, C., Crastes dit Sourd, R. & Hess, S. (2018), We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning. Transportation, forthcoming.
Limited memory capacity, retrieval constraints and anchoring are central to expectation formation processes. We develop a model of adaptive expectations where individuals are able to store only a finite number of past experiences of a stochastic state variable. Retrieval of these experiences is probabilistic and subject to error. We apply the model to scheduling choices of commuters and demonstrate that memory constraints lead to sub-optimal choices. We analytically and numerically show how memory-based adaptive expectations may substantially increase commuters’ willingness-to-pay for reductions in travel time variability, relative to the rational expectations outcome.
Koster, P., Peer, S. & Dekker, T. (2015), Memory, expectation formation and scheduling choices. Economics of Transportation, 4 (4), pp 256-265.
Increasing commute distances often lead to increased auto-dependency and is a major problem in many developed as well as developing countries. While in developed countries, the propensity to commute long distances generally originates from the preference to work in the core of the city and live in the suburb or periphery, in developing countries, the trend is often quite the opposite. For example, in Bangladesh, people generally have a strong preference to live at the heart of the major cities even if they work at the peripheral areas of the city, in another city or in a rural area. Further, it is also not uncommon to maintain split-families where the earning member of the family lives near the workplace while the rest of the family is based in a big city (subject to affordability). These phenomena lead to substantial increase in Vehicle Miles Traveled (VMT) and add burden to the transport infrastructure. The focus of the research is to explore the key factors that induce middle and upper-middle class commuters in Bangladesh to live away from their workplace and/or maintain split-families. A case study is conducted using Stated Preference (SP) surveys conducted among the faculty members of two universities: one located at the periphery of the capital city and the other quite far away. Discrete Choice Models are developed using the collected data. Results reveal that albeit some differences, for both cases, the choices are strongly driven by quality of the education institutes and the house rent. Factors like gender, income and car-ownership, which traditionally play a strong role in the context of developed countries, are found to be of less significance. The models, though estimated with limited data, provide useful insights about the factors that drive residential location choices in the context of a developing country and can help in formulating policies for encouraging people to live closer to their workplaces and thereby reduce commuter VMT.
Choudhury, C.F. & Ayaz, S.B. (2015), Why live far? — Insights from modeling residential location choice in Bangladesh. Journal of Transport Geography, 48, pp 1-9.
Objectives To assess whether patients’ willingness to add a blood pressure-lowering drug and the importance they attach to specific treatment characteristics differ among age groups in patients with type 2 diabetes. Materials and Methods Patients being prescribed at least an oral glucose-lowering and a blood pressure-lowering drug completed a questionnaire including a discrete choice experiment. This experiment contained choice sets with hypothetical blood pressure-lowering drugs and a no additional drug alternative, which differed in their characteristics (i.e. effects and intake moments). Differences in willingness to add a drug were compared between patients <75 years (non-aged) and ≥75 years (aged) using Pearson χ2-tests. Multinomial logit models were used to assess and compare the importance attached to the characteristics. Results Of the 161 patients who completed the questionnaire, 151 (72%) could be included in the analyses (mean age 68 years; 42% female). Aged patients were less willing to add a drug than non-aged patients (67% versus 84% respectively; P = 0.017). In both age groups, the effect on blood pressure was most important for choosing a drug, followed by the risk of adverse drug events and the risk of death. The effect on limitations due to stroke was only significant in the non-aged group. The effect on blood pressure was slightly more important in the non-aged than the aged group (P = 0.043). Conclusions Aged patients appear less willing to add a preventive drug than non-aged patients. The importance attached to various treatment characteristics does not seem to differ much among age groups.
de Vries, S.T., de Vries, F.M., Dekker, T., et al. (2015), The role of patients’ age on their preferences for choosing additional blood pressure-lowering drugs: a discrete choice experiment in patients with diabetes. PLOS ONE, 10 (10).
Introduction Outpatient parenteral antimicrobial therapy (OPAT) is used to treat a wide range of infections, and is common practice in countries such as the USA and Australia. In the UK, national guidelines (standards of care) for OPAT services have been developed to act as a benchmark for clinical monitoring and quality. However, the availability of OPAT services in the UK is still patchy and until quite recently was available only in specialist centres. Over time, National Health Service (NHS) Trusts have developed OPAT services in response to local needs, which has resulted in different service configurations and models of care. However, there has been no robust examination comparing the cost-effectiveness of each service type, or any systematic examination of patient preferences for services on which to base any business case decision. Methods and analysis The study will use a mixed methods approach, to evaluate patient preferences for and the cost-effectiveness of OPAT service models. The study includes seven NHS Trusts located in four counties. There are five inter-related work packages: a systematic review of the published research on the safety, efficacy and cost-effectiveness of intravenous antibiotic delivery services; a qualitative study to explore existing OPAT services and perceived barriers to future development; an economic model to estimate the comparative value of four different community intravenous antibiotic services; a discrete choice experiment to assess patient preferences for services, and an expert panel to agree which service models may constitute the optimal service model(s) of community intravenous antibiotics delivery. Ethics and dissemination The study has been approved by the NRES Committee, South West—Frenchay using the Proportionate Review Service (ref 13/SW/0060). The results of the study will be disseminated at national and international conferences, and in international journals.
Czoski Murray, C., Twiddy, M., Meads, D., Hess, S., et al. (2015), Community IntraVenous Antibiotic Study (CIVAS): Protocol for An Evaluation of Patient Preferences for and Cost effectiveness of Community Intravenous Antibiotic Services. BMJ Open, 5 (8).
This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.
Hess, S., Quddus, M.A., Rieser-Schüssler, N. & Daly, A.J. (2014), Developing Advanced Route Choice Models for Heavy Goods Vehicles Using GPS Data. Transportation Research Part E, 77, pp 29-44.
This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.
Hess, S., Quddus, M., Rieser, N. & Daly, A.J. (2015), Developing advanced route choice models for heavy goods vehicles using GPS data. Transportation Research Part E, 77, pp 29-44.
The German Federal Ministry of Transport and Digital Infrastructure is currently preparing the 2015 Federal Transport Investment Plan. Because this effort includes an update to the overall methodology of the ministry's cost–benefit analysis, both the value of reliability (VOR) and the value of travel time (VOT) for personal and business travel will be estimated. The VOT will replace a set of existing values, but the VOR will be estimated for the first time because they are not yet incorporated in the standard appraisal. A two-stage approach was used for data collection: first respondents reported about current trips (revealed preference), and then these responses were systematically varied to become the basis for stated preference experiments. This paper presents the findings of estimating the VOR. In the stated preference experiments the reliability of the travel modes was presented with different formats. The final model formulation differed in the definition of reliability for private and public transport. For car trips, saving travel time had more value for the respondents than reducing the variability. The calculated VOR for the mean expected unscheduled delay of public transport trips was slightly lower than the VOT; this result indicates that the reliability was less important to the respondents than the relevant travel time saving. A mean expected unscheduled delay of 1 min and 1 min of standard deviation are almost equivalent to 1 min of travel time saving (reliability ratio). Because this was the first official estimation of VOR and VOT for Germany, the values should be reconsidered and updated on a regular basis.
Ehreke, I., Hess, S., Weis, C. & Axhausen, K.W. (2015), Reliability in the German value of time study. Transportation Research Record, 2495 (1), pp 14-22.
With a view to better capturing heterogeneity across decision makers and improving prediction of choices, there is increasing interest in estimating separate models for each person. Almost exclusively, this work has however taken place outside the field of transport research. The aim of the present paper is twofold. We first wish to give an account of the potential benefits of a greater focus on individual level estimates in transport applications. Secondly, we wish to offer further insights into the relative benefits of sample level and individual level models (ILMs) by drawing on a data set containing an unusually large number (144) of decisions on holiday travel per individual. In addition to comparing existing approaches, we also put forward the use of a novel technique which draws on the relative benefits of both sample level and ILMs by estimating ILMs in a Bayesian fashion with priors drawn from a sample level model. Our results show only limited differences between ILMs and conditionals from sample level models when working with the full set of choices. When working with more realistic sample sizes at the person level, our results suggest that ILMs can offer better performance on the estimation data but that this is a result of overfitting which can lead to inferior prediction performance. Our proposed Bayesian ILM model offers good intermediary performance. The use of best-worst data rather than simple stated choice, as is done commonly in published ILM work, does not lead to major changes to these findings.
Dumont, J., Giergiczny, M. & Hess, S. (2015), Individual level models vs. sample level models: contrasts and mutual benefits. Transportmetrica, 11 (6), pp 465-483.
Applications of discrete choice models in environmental valuation increasingly use a random coefficient specification, such as mixed logit, to represent taste heterogeneity. The majority of applications rely on data containing multiple observations for each respondent, where a common assumption is that tastes stay constant across choices for the same respondent. We question this assumption and make use of a model developed in the transport field which allows tastes to vary over choices for each consumer in addition to variation across consumers. An empirical analysis making use of a stated choice dataset for wetland conservation in Belarus shows that superior performance is obtained by allowing jointly for the two types of heterogeneity and that recovery of these intra-respondent variations is not possible using standard approaches, such as allowing for scale heterogeneity across tasks. We show also that intra-respondent heterogeneity can be especially high for attributes which respondents are unfamiliar with, and that a failure to account for it can substantially affect welfare estimates. We interpret this as an indication that this heterogeneity relates primarily to uncertainty. Finally, we offer initial insights into the relationship between intra-respondent heterogeneity and findings on uncertainty in a contingent valuation context.
Hess, S. & Giergiczny, M. (2015), Intra-respondent heterogeneity in a stated choice survey on wetland conservation in Belarus: first steps towards creating a link with uncertainty in contingent valuation. Environmental & Resource Economics, 60 (3), pp 327-347.
Environmental economists are increasingly interested in better understanding how people cognitively organise their beliefs and attitudes towards environmental change in order to identify key motives and barriers that stimulate or prevent action. In this paper, we explore the utility of a commonly used psychometric scale, the awareness of consequences (AC) scale, in order to better understand stated choices. The main contribution of the paper is that it provides a novel approach to incorporate attitudinal information into discrete choice models for environmental valuation: firstly, environmental attitudes are incorporated using a reinterpretation of the classical AC scale recently proposed by Ryan and Spash (2012); and, secondly, attitudinal data is incorporated as latent variables under a hybrid choice modelling framework. This novel approach is applied to data from a survey conducted in the Basque Country (Spain) in 2008 aimed at valuing land-use policies in a Natura 2000 Network site. The results are relevant to policy-making because choice models that are able to accommodate underlying environmental attitudes may help in designing more effective environmental policies.
Hoyos, D., Mariel, P. & Hess, S. (2015), Incorporating environmental attitudes in discrete choice models: an exploration of the utility of the awareness of consequences scale. Science of the Total Environment, 505, pp 1100-1111.
This study tests the transferability of the nonmarket values of water conservation for domestic and environmental purposes across three south European countries and Australia applying a common choice experiment design. Different approaches are followed to test the transferability of the estimated values, aiming to minimise transfer errors for use in policy analysis, comparing both single‐ and multicountry transfers, with and without socio‐economic adjustments. Within Europe, significant differences are found between implicit prices for environmental water use, but not for domestic water use. In the Australian case study, alleviating restrictions on domestic water use has no significant value. Pooling the three European samples improves the transferability of the environmental flow values between Europe and Australia. Results show that a reduction in transfer error is achieved when controlling for unobserved and observed preference heterogeneity in the single‐ and multicountry transfers, providing additional support for the superiority of socio‐economic adjustment procedures in value transfer.
Brouwer, R., Martin-Ortega, J., Dekker, T., et al. (2015), Improving value transfer through socio-economic adjustments in a multicountry choice experiment of water conservation alternatives. Australian Journal of Agricultural and Resource Economics, 59 (3), pp 458-478.
Expanding the share of renewable energy sources might substantially increase externalities as, for example, wind turbines may disturb the landscape and negatively affect biodiversity. This paper investigates the public׳s sensitivities towards these externalities by using discrete choice experiments and shows how preferences differ across inhabitants of our study region. As a further insight into the sources for these variations, a hybrid choice model is employed in order to incorporate individuals׳ latent attitudes in the estimated model. Our latent class structure allocates individuals to classes according to underlying latent attitudes that also influence the answers to attitudinal questions. We show that these underlying attitudes are a function of a number of socio-demographic characteristics, with young people, men with low income and those living closer to turbines having a stronger pro-wind power generation attitude. The inclusion of the attitudes in the class allocation component of the latent class model leads to a richer picture of people׳s valuations, revealing, for example, antagonistic preferences of two distinct groups of respondents, i.e. advocates and opponents of wind power generation.
Mariel, P., Meyerhoff, J. & Hess, S. (2015), Heterogeneous preferences toward landscape externalities of wind turbines – combining choices and attitudes in a hybrid model. Renewable & Sustainable Energy Reviews, 41, pp 647-657.
The official appraisal values of travel time savings (VTTS) for non-work trips in UK were estimated by basic discrete choice model on stated choice data collected over 20 years ago. This choice model developed by Bates and Whalen (2001) was specified to address some long-standing issues in the field of VTTS valuation including the sign and size of VTTS while allowing continuous interactions between VTTS and journey covariates. With respect to the size issue, it was found that a “tapering” function, whereby time changes are increasingly discounted, could best explain the lower unit utility observed for small time savings (STS). While this set of non-work VTTS is still being used for transport appraisal in UK, the field of discrete choice modelling has evolved significantly brought by a leap of computing power and improved simulation techniques. Notably, advanced model such as mixed multinomial logit (MMNL) has been widely used to facilitate more realistic travel behavioural modelling by explaining random taste heterogeneity across respondents, which cannot be achieved in a deterministic manner. Also, techniques in specifying such model for VTTS valuation are well established by researchers nowadays. The key objective of this research was then to apply the MMNL model and re-estimate the current UK VTTS within a random coefficient logit framework. Alongside the theoretical discussions, this paper presents a synthesis of empirical evidence to support an updated appraisal value for non-work travel time savings in UK. Some key findings from this paper include a much higher mean value for the VTTS and the significantly reduced “perception effect” for the STS. In particular, this research found that MMNL model substantially reduces the “tapering” parameter of the discounting function for STS such that the “perception effect” of the VTTS becomes minimal. This finding suggests that travel benefits due to STS should be included for transport appraisal and it challenges some appraisal frameworks for countries like Germany where VTTS are discounted or even completely ignored for STS.
Tjiong, J. (2015), Re-estimating UK appraisal values for non-work travel time savings using random coefficient logit model. Transportation Research Procedia, 8, pp 50-61.
Random Regret Minimization for consumer choice research.
This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM–RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts.
Chorus, C. G., van Cranenburgh, S. & Dekker, T. (2014), Random Regret Minimization for consumer choice research. Journal of Business Research, 67, pp 2428-2436.
We test 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 semiparametric local multinomial logit model is proposed as an alternative method to the Swait and Louviere (1993) test procedure to control for preference dynamics in stated choice experiments. We find evidence of a declining impact over the choice sequence of an induced starting point bias in the first choice task. The results provide indicative support for convergence in preferences between both samples, which is in line with the discovered preference hypothesis.
Dekker, T., Koster, P. & Brouwer, R. (2014), Changing with the tide: semi-parametric estimation of preference dynamics. Land Economics, 90 (4), pp 717-745.
A major transport project would typically affect the cost of travel of several different alternatives, and give rise to a combination of gains and losses to users of each alternative. The attribution of benefits to each of the travel alternatives needs to recognise that travellers may change their behaviour as a result of the project. These changes in demand arise not only from changes in the cost of each specific alternative but also from cost changes in other competing alternatives. The appropriate treatment of inter-modal effects is central to the determination of the user benefit produced by each alternative. The paper sets out a number of desirable criteria that source-related measures of user benefit should satisfy which include local consistency with the rule of a half. It explores the effect of alternative path specifications on the resulting measures and demonstrate that they can give different results when larger cost changes can occur, such as in modelling a new alternative. Appropriate measures that are able to treat this problem are developed and the results compared to those obtained by numerical methods.
Hyman, G. & Daly, A.J. (2014), The Attribution of Transport User Benefits by Source using Discrete Choice Models. Research in Transport Economics, 47, pp 103-111.