Tag Archive: Hess

Modelling the role of consideration of alternatives in mode choice: An application on the Rome-Milan corridor.

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.

Modelling residential mobility decision and its impact on car ownership and travel mode.

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.

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour.

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.

Modelling Residential Location Choices with Implicit Availability of Alternatives.

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.

New appraisal values of travel time saving and reliability in Great Britain.

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.

Modelling long-distance route choice using mobile phone call detail record data: A case study of Senegal.

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.

Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data.

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.

Mode choice with latent availability and consideration: theory and a case study.

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.

Modelling trip generation using mobile phone data: a latent demographics approach.

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.

 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.

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.

 What’s Important in AAC Decision Making for Children? Evidence from a Best-worst Scaling Survey.

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.

Allowing for heterogeneity in the consideration of airport access modes: the case of Bari airport.

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.

Understanding attitudes towards congestion pricing: a latent variable investigation with data from four cities.

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.

Modelling pedestrian crossing choice behaviour on Cape Town freeways: caught between a rock and a hard place?

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.

VTT or VTTS: a note on terminology for value of travel time work.

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.

Impact of travel time constraints on taste heterogeneity and non-linearity in simple time-cost trade-offs.

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.

We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning.

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.

Community IntraVenous Antibiotic Study (CIVAS): Protocol for An Evaluation of Patient Preferences for and Cost effectiveness of Community Intravenous Antibiotic Services.

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).

 

Developing Advanced Route Choice Models for Heavy Goods Vehicles Using GPS Data.

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.

Developing advanced route choice models for heavy goods vehicles using GPS data.

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.

Reliability in the German value of time study.

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.

Individual level models vs. sample level models: contrasts and mutual benefits

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.

Intra-respondent heterogeneity in a stated choice survey on wetland conservation in Belarus: first steps towards creating a link with uncertainty in contingent valuation.

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.

Incorporating environmental attitudes in discrete choice models: an exploration of the utility of the awareness of consequences scale.

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.

Heterogeneous preferences toward landscape externalities of wind turbines – combining choices and attitudes in a hybrid model.

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.

Analytic approximations for computing probit choice probabilities.

The multinomial probit model has long been used in transport applications as the basis for mode- and route-choice in computing network flows, and in other choice contexts when estimating preference parameters. It is well known that computation of the probit choice probabilities presents a significant computational burden, since they are based on multivariate normal integrals. Various methods exist for computing these choice probabilities, though standard Monte Carlo is most commonly used. In this article we compare two analytical approximation methods (Mendell–Elston and Solow–Joe) with three Monte Carlo approaches for computing probit choice probabilities. We systematically investigate a wide range of parameter settings and report on the accuracy and computational efficiency of each method. The findings suggest that the accuracy and efficiency of an optimally ordered Mendell–Elston analytic approximation method offers great potential for wider use.

Connors, R. D., Hess, S., & Daly, A. (2014). Analytic approximations for computing probit choice probabilities. Transportmetrica A: Transport Science, 10(2), 119-139.

 

Contrasting imputation with a latent variable approach to dealing with missing income in choice models.

Income is a key variable in many choice models. It is also one of the most salient examples of a variable affected by data problems. Issues with income arise as measurement errors in categorically captured income, correlation between stated income and unobserved variables, systematic over- or under-statement of income and missing income values for those who refuse to answer or do not know their (household) income. A common approach for dealing especially with missing income is to use imputation based on the relationship among those who report income between their stated income for reporters and their socio-demographic characteristics. A number of authors have also recently put forward a latent variable treatment of the issue, which has theoretical advantages over imputation, not least by drawing not just on data on stated income for reporters, but also choice behaviour of all respondents. We contrast this approach empirically with imputation as well as simpler approaches in two case studies, one with stated preference data and one with revealed preference data. Our findings suggest that, at least with the data at hand, the latent variable approach produces similar results to imputation, possibly an indication of non-reporters of income having similar income distributions from those who report it. But in other data sets the efficiency advantage over imputation could help in revealing issues in the complete and accurate reporting of income.

Hess, S., Sanko, N., Dumont, J. & Daly, A.J. (2014), Contrasting imputation with a latent variable approach to dealing with missing income in choice models. Journal of Choice Modelling, 12, pp 47-57.

Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives.

An increasing number of studies of choice behaviour are looking at Random Regret Minimisation (RRM) as an alternative to the well established Random Utility Maximisation (RUM) framework. Empirical evidence tends to show small differences in performance between the two approaches, with the implied preference between the models being dataset specific. In the present paper, we discuss how in the context of choice tasks involving an opt out alternative, the differences are potentially more clear cut. Specifically, we hypothesise that when opt out alternatives are framed as a rejection of all the available alternatives, this is likely to have a detrimental impact on the performance of RRM, while the performance of RUM suffers more than RRM when the opt out is framed as a respondent being indifferent between the alternatives on offer. We provide empirical support for these hypotheses through two case studies, using the two different types of opt out alternatives. Our findings suggest that analysts need to carefully evaluate their choice of model structure in the presence of opt out alternatives, while any a priori preference for a given model structure should be taken into account in survey framing.

Hess, S., Beck, M. & Chorus, C. (2014), Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives. Transportation Research Part A, 66, pp 1-12.

Understanding the formation and influence of attitudes in patients’ treatment choices for lower back pain: testing the benefits of a hybrid choice model approach.

A growing number of studies across different fields are making use of a new class of choice models, labelled variably as hybrid model structures or integrated choice and latent variable models, and incorporating the role of attitudes in decision making. To date, this technique has not been used in health economics. The present paper looks at the formation of such attitudes and their role in patients’ treatment choices in the context of low back pain. We use stated choice data collected from a sample of 561 patients with 348 respondents referred to a regional spine centre in Middelfart, Denmark in spring/summer 2012. We show how the hybrid model structure is able to make a link between attitudinal questions and treatment choices, and also explains variation of these attitudes across key socio-demographic groups. However, we also show how, in this case, only a small share of the overall heterogeneity is linked to the latent attitude construct. Despite their growing popularity, the key findings of the advanced model, despite a greater insight into the drivers of attitudes and small gains in efficiency, are no different from standard approaches which remain easier to apply.

Kløjgaard, M. & Hess, S.  (2014), Understanding the formation and influence of attitudes in patients’ treatment choices for lower back pain: testing the benefits of a hybrid choice model approach. Social Science & Medicine, 114, pp 138-150.

Temporal transfer of models of mode and destination choice for the Greater Toronto and Hamilton area.

Transport planning relies extensively on forecasts of traveler behavior over horizons of 20 years and more. Implicit in such forecasts is the assumption that travelers’ tastes, as represented by the behavioral model parameters, are constant over time. In technical terms, this assumption is referred to as the “temporal transferability” of the models. This paper summarizes the findings from a literature review that demonstrates there is little evidence about the transferability of mode-destination models over typical forecasting horizons. The literature review shows a relative lack of empirical studies given the importance of the issue. To provide further insights and evidence, models of commuter mode-destination choice been developed from household interview data collected across the Greater Toronto and Hamilton Area in 1986, 1996, 2001, and 2006. The analysis demonstrates that improving model specification improves the transferability of the models, and in general the transferability declines as the transfer period increases. The transferability of the level-of-service parameters is higher than transferability of the cost parameters, which has important implications when considering the accuracy of forecasts for different types of policy. The transferred models over-predict the key change in mode share over the transfer period—specifically, the shift from local transit to auto driver between 1986 and 1996—but under-predict the growth in commuting tour lengths over the same period.

Fox, J., Daly, A., Hess, S. & Miller, E. (2014), Temporal transfer of models of mode and destination choice for the Greater Toronto and Hamilton area. Journal of Transport and Land Use, 7 (2), pp 41-62.

Heterogeneity assumptions in the specification of bargaining models: a study of household level trade-offs between commuting time and salary.

With many real world decisions being made in conjunction with other decision makers, or single agent decisions having an influence on other members of the decision maker’s immediate entourage, there is strong interest in studying the relative weight assigned to different agents in such contexts. In the present paper, we focus on the case of one member of a two person household being asked to make choices affecting the travel time and salary of both members. We highlight the presence of significant heterogeneity across individuals not just in their underlying sensitivities, but also in the relative weight they assign to their partner, and show how this weight varies across attributes. This is in contrast to existing work which uses weights assigned to individual agents at the level of the overall utility rather than for individual attributes. We also show clear evidence of a risk of confounding between heterogeneity in marginal sensitivities and heterogeneity in the weights assigned to each member. We show how this can lead to misleading model results, and argue that this may also explain past results showing bargaining or weight parameters outside the usual [0,1] range in more traditional joint decision making contexts. In terms of substantive results, we find that male respondents place more weight on their partner’s travel time, while female respondents place more weight on their partner’s salary.

O’Neill, V.L. & Hess, S. (2014), Heterogeneity assumptions in the specification of bargaining models: a study of household level trade-offs between commuting time and salary. Transportation, 41 (4), pp 745-763.

 

Incorporating needs-satisfaction in a discrete choice model of leisure activities

In this paper we extend the behavioural scope of discrete choice models for leisure activity-travel choices. More specifically, we investigate to what extent choices for leisure activities and related travels are driven by the satisfaction of needs. In addition to conventional attributes (such as activity costs), our regret based discrete choice model incorporates latent variables representing the anticipated level of individual needs-satisfaction by a particular leisure activity. The latent variables are calibrated with the help of subjective indicators of needs-satisfaction associated with the leisure activities. Results show that needs-satisfaction allows us to decompose a substantial share of the unobserved heterogeneity in leisure activity-travel decisions across respondents. Identifying the structural drivers of anticipated needs-satisfaction also enables a better prediction of leisure activity choice.

Dekker, T., Hess, S., Arentze, T. & Chorus, C.G. (2014), Incorporating needs-satisfaction in a discrete choice model of leisure activities, Journal of Transport Geography, 21, pp 36-41.

Impact of unimportant attributes in stated choice surveys.

Despite growing interest in the notion that respondents in stated choice surveys may make their decisions on the basis of only a subset of the presented attributes, the impact of any unimportant attributes on the estimates of other valuations is somewhat unclear. This paper presents evidence from a two stage survey where the second stage eliminates attributes deemed unimportant in the first stage. Our analysis shows no evidence of systematic differences between the results of the two stages. This leads to the conclusion that, up to a point where respondent burden may become an issue, analysts should include all attributes that may be relevant, and allow the respondent to filter out those that play no role.

Hess, S. (2014), Impact of unimportant attributes in stated choice surveys. European Journal of Transport and Infrastructure Research, 14 (4), pp 349-361.

A question of taste: recognising the role of latent preferences and attitudes in analysing food choices.

There has long been substantial interest in understanding consumer food choices, where a key complexity in this context is the potentially large amount of heterogeneity in tastes across individual consumers, as well as the role of underlying attitudes towards food and cooking. The present paper underlines that both tastes and attitudes are unobserved, and makes the case for a latent variable treatment of these components. Using empirical data collected in Northern Ireland as part of a wider study to elicit intra-household trade-offs between home-cooked meal options, we show how these latent sensitivities and attitudes drive both the choice behaviour as well as the answers to supplementary questions. We find significant heterogeneity across respondents in these underlying factors and show how incorporating them in our models leads to important insights into preferences.

O’Neill, V.L., Hess, S. & Campbell, D. (2014), A question of taste: recognising the role of latent preferences and attitudes in analysing food choices. Food Quality and Preferences, 32C, pp 299-310.

 

 

Practical solutions for sampling alternatives in large scale models.

Many large-scale real-world transport applications have choice sets that are so large as to make model estimation and application computationally impractical. The ability to estimate models on subsets of the alternatives is thus of great appeal, and correction approaches have existed since the late 1970s for the simple multinomial logit (MNL) model. However, many of these models in practice rely on nested logit specifications, for example, in the context of the joint choice of mode and destination. Recent research has put forward solutions for such generalized extreme value (GEV) structures, but these structures remain difficult to apply in practice. This paper puts forward a simplification of the GEV method for use in computationally efficient implementations of nested logit. The good performance of this approach is illustrated with simulated data, and additional insights into sampling error are also provided with different sampling strategies for MNL.

Daly, A., Hess, S. & Dekker, T. (2014), Practical solutions for sampling alternatives in large scale models. Transportation Research Record, 2429 (1), pp 148-156.

 

Understanding air travellers’ trade-offs between connecting flights and surface access characteristics.

This paper reports on a study which seeks to improve our understanding of how people choose between different kinds of flight at competing airports, and how their choices are affected by access conditions. In particular, using stated choice data collected in Scotland, it investigates whether improving surface access to regional airports that are in relatively close proximity to one another (Glasgow and Edinburgh) leads people to avoid taking indirect flights from their nearest airport in favour of direct flights from an alternative airport. In line with expectations, our estimation results from Cross-Nested Logit models show strong aversion to connecting flights, resulting in a willingness to either pay higher fares for direct flights or accept non-trivial increases in access time. For the latter, even without the potential new direct rail link between the two airports, current access times are such that a scenario where direct flights were only available at the non-home airport, a substantial share of passengers would choose to travel from the alternative airport.

Johnson, D., Hess, S. & Matthews, B. (2014), Understanding air travellers’ trade-offs between connecting flights and surface access characteristics. Journal of Air Transport Management, 34, pp 70-77.