Institute for Transport Studies (ITS)

Supervisor: Dr Greg Marsden

The current approach to transport system management is based around principles of equilibrium. Whilst such an approach allows for change in the system to lead to new equilibria, it is based on a premise that travel patterns are inherently stable and that a steady-state of transport flows is an efficient outcome. We know from observation that there is a lot more churn in the transport system than is typically accounted for. This comes from changing jobs, changing housing and complex patterns of activity. Climate change also brings increased risks of disruptions to transport systems. If urban areas are to be able to continue to flourish under more uncertain climate futures then it may be that new planning paradigms should be adopted that embrace uncertainty and enable people to respond better to it. If such approaches were adopted what would they look like and how different would they be from the current approaches? This project could be adapted to high quality students from a range of social science backgrounds. It will be linked to on-going research on energy demand reduction (www.disruptionproject.net).

Supervisor: Dr Frances Hodgson

The research focuses on understanding the potential for new and more equitable governance relationships, engagement and participation in transport planning, which is afforded by the technological developments and use of social media. Current uses of social media in transport governance focus around the provision of information e.g., ‘the informed traveller’ or canvassing opinions and the new ‘opendata’ initiatives at central Government. This research will explore this aspect but will develop further understanding in areas such as: user generated content for system maintenance and management; user generated provision to overcome policy and planning failures; and the potential for generating social networks and community. These developments have the potential to take transport service delivery and planning to a new era – a step-change in planning which has implications for socially excluded groups, governance and provision. The research will identify and understand the equity impacts of new governance relationships and the role and significance of social media in reducing social exclusion. The research will use a mixed methodological approach and will suit a candidate with a social science background, an interest social equity and in applied tools in planning.

Supervisor: Dr Yvonne Barnard

New transport systems, such as driver support systems, are usually of a highly complex nature and consist of both hardware and software that are not understandable for non-experts, however, some understanding of how the system works is necessary to be able to use them. Users of systems have mental models about how a system works and what it can and cannot do. A mental model is an internal representation employed to encode, predict, evaluate, and communicate the consequences of perceived and intended changes to the operator’s current state within the dynamic environment (Goodrich & Boer, 1998). Mental models include: goals that the driver wants to reach by using the system, the actions the driver believes to be able to perform with the system or, in the case of automated systems, the actions the system is able to perform, the results of the actions in different situations, and the way to assess those results. Wrong ways of using systems, or making errors in using them, may be the consequence of an inadequate model. Having inadequate models may also lead to users not wanting to use technologies that may in principle be beneficial to them. An example is elderly drivers not wanting or daring to use driver support systems.

The project will investigate mental models of users in relation to their intention to use new technologies. Use will be made of the Unified Theory of Acceptance and Use of Technology (Venkatesh et al. 2003). The project will relate to the EPSRC funded BRIDGE project (Building Relationships with the ‘Invisible’ in the Digital Global Economy), which is concerned with investigating the needs of people who do not adopt technologies. The project will make use of different research methods: focus groups, interviews, and experimental work.

Suggested reading:

Goodrich, M. A., & Boer, E. R. (1998). Semiotics and Mental Models: Modeling Automobile Driver Behavior. In: Proceedings of the 1998 IEEE ISIC/CIRA,/ISAS Joint Conference, p. 771-776. Gaithersburg, Maryland, USA.

Venkatesh V., Morris M.G., Davis G.B., & Davis F.D.. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3), 425-478.

Vlassenroot S., Brookhuis K. Marchau V. and Witlox F.(2008). Measuring acceptance and acceptability of ITS. TRAIL Research School, Delft.

Supervisor: Prof. Gerard de Jong

Electric and hybrid cars could contribute substantially to the required reduction in emissions and dependence on fossil fuels. The technology to do this is there. Crucial issues for the market penetration of this new technology are:

• How fast will consumers replace their current cars?
• How many of the replacement cars will be electric and hybrid cars?

Behavioural data on these important issues are largely missing. This project will develop a combined revealed/stated preference household survey which includes:

• Questions on actual attributes of he households, its persons and its cars;
• Retrospective questions on the car ownership history of the household;
• Stated choice experiments on car type choice, including attributes that are specially relevant for electric and hybrid cars, such as fuelling range, top speed and luggage space.

As part of this project, the questionnaire will be used to interview several hundreds of UK households. The resulting data will then be used to estimate models for:

• The timing of vehicle replacement (hazard-based duration models or Markov models) and other changes in the household car ownership status (e.g. moving to more cars or fewer cars);
• Vehicle type choice (discrete choice models, including mixed logit), focussing on electric and hybrid cars;
• Vehicle use.

Finally, the estimated models will be used to carry out policy simulations, such as on the effect of measures to accelerate replacement (e.g. scrappage schemes), subsidies on the purchase of electric and hybrid cars, and emission taxes.

Suggested reading:

Jong, G.C. de (1996); A disaggregate model system of vehicle holding duration, type choice and use; Transportation Research B, 30-4, pp 263-276.

Jong. G.C. de, J. Fox, A.J. Daly, M. Pieters and R. Smit (2004); Comparison of car ownership models, Transport Reviews, 24-4, pp 379-408, 2004.

Rashidi, T.H., K. Mohammadian and F. Koppelman (2009); A dynamic hazard-based structural equations model of vehicle ownership with endogenous residential and job location changes incorporating group decision making; Paper presented at the International Choice Modelling Conference 2009, Harrogate.

Supervisor: Dr Tony Fowkes

Many disciplines are now using hypothetical choice techniques that go under a variety of names and take a variety of forms. Having begun life in Market Research, one of the main areas of their early use was in Transportation Demand Forecasting, such as the prediction of the effects on rail passenger numbers of reducing journey times or running new or additional services. In Transport the methods developed were generally referred to as Stated Preference (or, more recently, Stated Choice). Some researchers saw that, when the experimental design was predetermined, some of the choices presented to respondents were not very helpful to the analyst. For example, if a respondent had said they were willing to pay an additional 10 pence per journey to have a new waiting room available, then we learn nothing from asking if they are willing to pay 5 pence for that facility. The idea arose that it might be possible to determine later questions on the basis of previous questions, eg. in this case, we might ask instead if they were willing to pay 15 pence.

However, when such approaches were tried, results appeared worse not better. A simulation study in a paper by Bradley and Daly (1993) found that such surveys were likely to generate systematic bias in estimated model parameters, and consequently any forecasts made therefrom. For many years, very few of these Adaptive Stated Preference (ASP) studies were undertaken. Nevertheless, in situations such as freight operator choice, where there are very few decision makers available for interview for each commodity of interest, the gain in information content of the responses from ASP was sometimes deemed sufficient to go to great lengths to attempt to ensure that that particular design was not subject to bias – as far as could be checked by simulation (Fowkes, 2007).

The present situation is that increases in computer power probably make it more easy to check for, and remedy, any tendency towards bias that ASP might engender. It is therefore an opportune time for a fresh look, from first principles, at ASP methods, and the possibilities for making them easier to use in a way that clients and others can be confident that their output is not seriously biased.

References:

Bradley M A and Daly A J (1993), New analysis issues in Stated Preference research. Proceedings of seminar D, PTRC-SAM, published as TRANSPORTATION PLANNING METHODS, Code P366, pp75-89, PTRC, London.

Fowkes A S (2007), The design and interpretation of freight stated preference experiments seeking to elicit behavioural valuations of journey attributes, TRANSPORTATION RESEARCH B, pp 966-980.

Supervisors: Dr Richard Batley, Dr Stephane Hess

Mathematical models representing human behaviour are used extensively in the field of transport and beyond. These models are used to analyse existing choices and forecast likely behaviour in a changing environment, e.g. the provision of new transport facilities, the introduction of new electricity pricing structures or the building of a new hospital.

To a large extent, these models are based on a compensatory approach, in which a person is assumed to make choices by trading off different attributes against one another. As an example, one mode of travel may be faster, but an alternative mode is cheaper; one train will get us to work on time, but the slightly later train is considerably less congested. The values of the different attributes of an alternative all affect that alternative's probability of being chosen, where the negative effect of one attribute may be cancelled out by the more positive effect of another attribute.

A different view of behaviour however exists in various strands of the mathematical psychology literature. Here, evidence suggests that at some people do not in fact engage in compensatory evaluation of alternatives, but make use of various alternative heuristics to arrive at their choices. This could for example involve lexicographic behaviour, the existence of reference points or the presence of thresholds in sensitivities or tolerances.

The aim of this PhD project is to first revisit the limited amount of existing work contrasting and combining the often disparate methodologies from the fields of economics and mathematical psychology. In-depth studies will then be conducted to investigate under which circumstances the assumptions made in traditional approaches may not be justified. Ultimately, the aim is to expand the existing methodological framework to be able to adequately represent decision making processes that are well established in the mathematical psychology literature, but which are largely ignored in the modelling field. By better understanding and representing the underlying behavioural structures, the project will seek to enhance the predictive power of models used to plan the provision and usage of scarce services and resources (such as healthcare, energy and transportation).

While the topic is concerned with the interface between psychology, economics and mathematics, the proposed research will be highly methodological in its nature, and a strong quantitative background will be expected from the student. Some programming skills will be also be desirable.

References – suggested reading

Batley, R. and Daly, A. (2006) On the equivalence between elimination-by-aspects and generalised extreme value models of choice behaviour. Journal of Mathematical Psychology, 50 (5), pp456-467.

Batley, R. and Toner, J. (2003) Elimination-by-aspects and advanced logit models of stated preferences for alternative-fuel vehicles. Proceedings of the European Transport Conference, Strasbourg, October 2003.

Hess, S., Rose, J.M. and Polak, J.W. (2009), Non-trading, lexicographic and inconsistent behaviour in stated preference data, Transportation Research Part B, forthcoming.

Simon, H.A. (1959) Theories of decision-making in economics and behavioral science. American Economic Review, 49 (1), pp253-283.

Train, K.E. (2003) Discrete choice methods with Simulation, Cambridge University Press, Cambridge, MA.

Tversky, A. (1972) Elimination by aspects: a theory of choice. Psychological Review, 79 (4), pp281-299.

Supervisors: Dr Stephane Hess, Dr Jeremy Toner

The analysis of travel behaviour requires as its main input data on travel decisions (choices) made by individual respondents. However, in many situations, data on real world choices is either not available or is not suitable for the purposes of the proposed analysis. As a result, an increasing number of studies rely on data collected through surveys which present respondents with hypothetical choice scenarios. Data from such stated preference (SP) surveys are used not only in academic work but also form the backbone of many studies advising policy makers in scenarios as wide ranging as the building of new roads, the introduction of road pricing or the investment in new rolling stock.

The majority of work using SP methods now makes use of stated choice (SC) surveys, in which respondents are asked to choose their most preferred option amongst a set of mutually exclusive alternatives. Approaches such as ranking or rating exercises have been largely discredited in a transport context, as have transfer price methods, which aim to directly obtain the willingness by respondents to pay for developments or improvements. However, outside a transport environment, these methods are experiencing a renaissance, and new developments, such as best-worst, a halfway measure between choice and full ranking, are gaining in popularity. At the same time, in transport and elsewhere, researchers are constantly devising new methods to improve the efficiency of the various available survey techniques. The net outcome is that there is substantial confusion at the user end, with practitioners often unsure which approach would be most applicable in their given context.

The aim of this PhD project would be to conduct an in depth comparison of the different available methods, highlighting which approaches are most adequate in what context. Additionally, the work would look at the potential for combining various existing methods. Finally, where appropriate, further methodological developments would be made.

Recommended reading:

Louviere, J.J., Hensher, D.A. and Swait, J.D. (2000) Stated Choice Methods: Analysis and Application. Cambridge University Press.

PTRC (2000) Stated Preference Modelling Techniques. A compilation of major papers from PTRC’s meeting and conference material. Edited by J de D Ortuzar. PTRC, London.

Supervisor: Prof. Peter Bonsall

Questionnaires are widely used as a source of data on travellers’ behaviour and of insight into their preferences. This information is an essential input to the methods currently used to analyse existing patterns of travel behaviour and to predict how people are likely to respond to changed travel conditions (new or re-scheduled rail services, new or improved roads, increased fuel costs, new parking charges, etc).

Unfortunately it is difficult to design a questionnaire which can provide an accurate and unbiased assessment of existing travel behaviour, let alone of attitudes or future intentions. This fact is often ignored or downplayed by practitioners – and even by researchers.

This project will include an assessment of the extent of bias and error in transport surveys – using relevant psychological theory (e.g. in respect of impression management and post-hoc rationalisation) and empirical evidence to identify the contexts in which it is most likely to occur.

The student will select one or more of these contexts for a detailed consideration of potential means of overcoming the problems and for demonstrating that this is achievable. The selection of context(s) for this detailed consideration will depend on an assessment of the prospects for success and on the student’s particular skills and interests.

The project will build on the wider literature as well as on previous work conducted by the supervisor as well as on.

It important that students contemplating this topic should have knowledge of relevant psychological theory and statistical methods, and an aptitude for design of questionnaires (including high level competence in the use of the English language).

Suggested reading:

Wiseman, F. (1972) Methodological bias in public opinion surveys. Public Opinion Quarterly, 36, 105-108.

Booth-Kewley S.; Edwards J. E.; Rosenfeld P. (1992) Impression management, social desirability, and computer administration of attitude questionnaires: does the computer make a difference? Journal of Applied Psychology 77, 4, 562-566

Sommer, B. and Sommer, R. (2001) A Practical Guide to Behavioural Research: Tools and Techniques. (5th Edition), Oxford University Press, New York.

Beale, JR and Bonsall P.W. (2007) Marketing in the bus industry: A psychological interpretation of some attitudinal and behavioural outcomes, Transportation Research Part F: Traffic Psychology and Behaviour, 104, 271-287.

Bonsall, PW and Shires J (2009) Estimating the robustness of questionnaire results: lessons from a mixed-mode survey of expectations for tele-working and road-based business travel”, Transportation 36:47–64

Supervisor: Prof. Peter Bonsall

Recent developments in GPS and mobile phone technology mean that it is now possible to collect data on individual travel behaviour without the active involvement of the individual whose movements are being logged.

This project will begin with an assessment of the state of the art of the relevant technologies and of the worldwide use of this kind of data to gather travel data. It will then explore and analyse the various barriers to the wider use of these technologies (achievable accuracy, reliability in difficult terrain, privacy concerns, sampling biases, data processing issues).

It is envisaged that the student will select one or more of these issues for detailed consideration of potential means of overcoming the barriers and for demonstrating that this is achievable. The selection of issue(s) for this detailed consideration will depend on an assessment of the potential for success and on the student’s particular skills and interests.

The project will build on previous work (e.g. a project undertaken by the supervisor, for the Department for Transport, to consider the role of such technologies as a replacement for the paper questionnaires used in the UK National Travel Survey) and an ongoing EU-funded project (SHANTI) which is examining the role of new technology sources of travel data.

The studentship may include international study visits funded under the SHANTI project.

Suggested reading:

http://www.cost.esf.org/domains_actions/tud/Actions/SHANTI

Bonsall, P., Wolf, J. and Holroyd, S. “Review of the potential role of 'new technologies'. in the National Travel Survey”; Final report on DfT website in October 2006:
http://www.dft.gov.uk/pgr/statistics/datatablespublications/personal/methodology/ntsreports/ntsnewtechnologies.pdf

Du, J and Aultman-Hall, L (2007) Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues Transportation Research Part A: Policy and Practice, 41, 3, 220-232

http://www.itls.usyd.edu.au/downloads/working_papers/itls-wp-08-06.pdf

http://rosowww.epfl.ch/mbi/papers/schoenfelder.pdf

Supervisor:Prof. Gerard de Jong

Mode choice in freight transport is usually studied in isolation. However, mode and shipment size are closely linked decisions. Large shipment sizes usually coincide with higher market shares for non-road transport, whereas there is a high correlation between road transport and small shipment sizes. Decisions on shipment size (or delivery frequency) need to be studied taking a logistics approach (e.g. reducing inventories by more frequent, just-in-time deliveries) that encompasses the more limited transport costs approach.

The Swedish 2004-2005 Commodity Flow Survey (CFS) is a unique data source in Europe. It details about more than 2.5 million individual shipments to or from a company in Sweden, with information on origin, destination, modes used, weight and value of the shipment, sector of the sending firm, commodity type, access to rail tracks and quays, etc.. Whilst the US Commodity Flow Survey has been analysed several times, its Swedish counterpart has barely been used for model estimation so far. Using this Swedish CFS, mode and shipment size choice at the individual shipment level can be explained from characteristics of the shipper, the shipment and transport time and cost on the networks.

Earlier work at ITS Leeds used the CFS 2001 to estimate mode and shipment size models. Multinomial land nested logit models were estimated on the CFS 2004-2005 in a Master Thesis project at Delft University of Technology in The Netherlands.

This PhD project will extend the models estimated so far on the CFS 2004-2005 in many ways:
- estimation of different models for different commodity classes (observed heterogeneity)
- estimation of models with different transport and logistics costs functions
- estimation of mixed logit models following the random coefficients specification to account for unobserved heterogeneity
- estimation of models where shipment size is treated as a continuous variable instead of discrete size classes, simultaneously with (discrete) mode choice.

Furthermore the project will look into the implications of these modelling options for the value of time and freight demand elasticities - the model outputs that are typically used to evaluate transport policies.

Suggested reading:

Jong, G.C. de and M.E. Ben-Akiva (2007) “A micro-simulation model of shipment size and transport chain choice”, Special issue on freight transport of Transportation Research B, 41, pp. 950-965, 2007.

Supervisors: Dr Stephane Hess, Dr Susan Grant-Muller

With the opening of several new lines in Europe in recent years, and the introduction of integrated ticketing as well as several through services, high speed rail is increasingly competing with air on short haul routes, with examples including London to Paris and Brussels, Madrid to Barcelona, and Paris to Marseilles to name but a few. Alongside greater comfort and ease of access (e.g. city centre terminals), the growing concerns about the environmental impacts of air travel arguably also play a significant role in this trend. Various initiatives aimed at raising public awareness of the low carbon agenda have been launched with the result of a heighted profile for the environmental cost of air travel in particular. Specific examples include the ‘carbon calculator’ increasingly available for air journeys and the opportunity some airlines offer for passengers to pay to offset this.

Traditionally, the choice between high speed rail and air has also involved a trade-off between time and cost. While air travel retains a speed advantage (even after factoring in access time and pre-boarding waiting time), high speed rail fares have tended to be cheaper. In fact, on some routes, such as Paris to Amsterdam, the difference in fare can be as high as a factor of 4:1, while on other routes, such as Paris to Lyon, airlines have ceased operations.

Increasingly, the fare advantage of high speed rail is being reduced by the rapid extension of the low-cost air network in Europe, as well as a slashing of fares by many legacy airlines. New routes are also constantly starting up, with an ease that can clearly not be met by high speed rail, facing infrastructure concerns that do not exist for air travel.

Understanding passengers’ choices between air travel and high speed rail is a crucial component of any ability to forecast future demand under various hypothetical scenarios, such as changes in regulation or the opening of new high speed rail lines and air routes. The choice between air and high speed rail is a complicated one, involving sensitivities along various dimensions, including not just fares and journey times, but also various more qualitative attributes such as comfort and ease of access. It is conjectured that different sectors of society place a range of values on some of these qualitative attributes that have yet to be established. Indeed there may for example be gender specific aspects to choice, related for example to perceptions of personal security or to the degree of acceptable discomfort, exposure to risk or inconvenience.

The aim of this PhD is to conduct state-of-the-art research into travellers’ mode choices in the short to medium distance context. The work is expected to make use of advanced discrete choice methodology from the family of random utility models, and will also involve the development of innovative survey approaches for capturing travellers’ choices. The outcome of this PhD will be a highly advanced framework allowing an analyst to gauge the impact of proposed policy, service and infrastructure changes.

Recommended reading:

González-Savignat, M. (2004), ‘Competition in air transport: The case of high speed train’, Journal of Transport Economics and Policy 38(1), 77–108.

Hess, S., Adler, T. & Polak, J.W. (2007), Modelling airport and airline choice behaviour with the use of stated preference survey data, Transportation Research Part E, 43, pp. 221-233.

Supervisors: Dr Astrid Gühnemann, Dr Richard Batley

There is increasing interest in applying permit trading schemes to tackle environmental impacts from transport. Examples are the planned inclusion of aviation into the European Emission Trading System (ETS) and a wide range of research on individual carbon permits in the UK. However, as hardly any empirical evidence exists on the application of such systems, the impacts of the policy instruments due to heterogeneity in responses by firms and consumers need further investigation.

The aim of this project is, therefore, to analyse variations in behavioural responses to environmental instruments and assess with the used of agent-based simulation models how these would influence the success of the implementation of instruments as a whole. A potential focus is on the comparison of permit trading and taxation measures.

Scope:
- develop behavioural rules for responses to environmental instruments (in particular trading), e.g.

1. influences of uncertainty of availability of permits and
2. influence of price expectations and attitudes towards risks in trading on
1. abatement versus paying the price / trading
2. inter-temporal choice of consumption within a trading period
3. off-setting between transport and non-transport energy consumption
3. segmentation of behavioural groups (eg. socio-demographic vs. attitudinal)

- test and verify these rules in experimental settings
- develop agent based models to simulate system behaviour based on variation in individual responses

The project can build on previous work as part of European research projects (e.g. MIME on trading noise at airports) and a wide range of expertise on CO2 emission trading in the Institute. In addition ITS has is internationally recognised for its decision research work and has access to laboratory facilities.

Supervisor: Dr Greg Marsden

Travel plans for businesses and schools are at the forefront of measures to try and reduce the single car occupant mode share for journeys to school. Evaluations have suggested that good plans can be extremely effective with mode shifts of 15% and sometimes more achieved.

The main mechanism for the introduction of travel plans is the planning system, although some are voluntary. Within this, any application of a major new development site or an extension to existing premises should include a travel plan. As part of this process it is necessary for the planning authority to take a view as to the likely effectiveness of the plan in reducing car use and the suitability of the measures proposed. However, there are no tools available which allow local authorities to do this. Only now are standardised monitoring and measurement methodologies starting to appear.

This project will build on work being undertaken as part of the TRICS trip generation software database and assess a range of travel plans with the aim of understanding what the impacts of the travel plans have been on mode share and which underpinning factors have been most important. The aim will be to produce a predictive tool, which is subsequently validated, which will allow local authorities to predict the impacts of travel plans at the assessment stage.

The project will require the student to collect data, conduct statistical analysis and interview practitioners and planners involved in the development of travel plans.

Suggested reading:

DfT (2002b) Making travel plans work: lessons from case studies. DfT, London.

DfT (2004) Smarter Choices – Changing the Way We Travel - Chapter 3 Workplace travel plans. Department for Transport (DfT), London, 2004.

Rye, T. (2002) "Travel plans: do they work?", Transport Policy, 9(4), 287-298

Supervisor: Dr Greg Marsden

Decarbonising of the private car fleet is a key plank in the UK Government’s strategy for delivering a low carbon transport system. A major part of this strategy is to encourage cities and regions to develop electric vehicle charging infrastructure and a core network of electric vehicles. This is being facilitated through grants and incentives and relies on local authorities, as major fleet operators, to implement their own electric fleets.

In such an experimental environment there will be policy implementations which work and those which fail. A key question to investigate is how well the lessons (good and bad) are transferred from the implementers to the potential adopters. A recent review of policy transfer between cities in Northern Europe (within the field of transport) suggests that lesson learning is ad-hoc and that authorities may not provide the right conditions to promote effective learning.

This study will trace the adoption of electric vehicles in a small sample of authorities and trace how the information about the adoption is used and transferred both internally and external to the organisation. It will also examine how potential adopters approach the task of learning from those sites that have operational experience. The aim would be to develop a programme for effective knowledge exchange which could maximise the benefits of UK investment in the area.

Suggested reading:

DfT, BERR, DIUS (2009) Ultra low carbon vehicles in the UK. DfT, London. http://www.dft.gov.uk/adobepdf/187604/ultralowcarbonvehicle.pdf

Marsden, G., Frick, K.T., May, A.D. and Deakin, E. (2009) Good Practice in the Exploitation of Innovative Strategies in Sustainable Urban Transport: City Interview Synthesis, Project Report Volvo Research and Educational Foundation, www.its.leeds.ac.uk/projects/policylearning

Rose, R. (2005) Learning from Comparative Public Policy: A practical guide, Routledge, Oxon, ISBN 0-415-31741-8

Supervisors: Dr Stephane Hess, Dr Greg Marsden

It is unquestionable that climate change is one of the key issues facing policymakers across the world. Much of the policy attention to date has surrounded understanding the costs of taking action and the damage costs of failing to take action. The Stern review is seminal in considering this and estimated a middle value of the social cost of carbon to be $85 per tonne.

A raft of policy measures exist which could be employed to reduce the carbon intensity of our transport activities. These include regulation, fiscal instruments and information - all with the aim of stimulating lower carbon travel choices.

In this context, the crucial question that needs to be answered is travellers’ reaction to any such policy measures and their willingness to either change their behaviour or accept changes to the conditions under which they travel. Examples of this include the willingness to pay for carbon reductions (e.g. through offsetting), the willingness to accept longer journeys in return for such reductions (e.g. by choosing rail instead of air travel), or indeed the absence of such behavioural change by some travellers, even in the face of increasing taxation.

Given the importance of the topic, it is surprising that there is hardly any literature in the transport which explores the willingness of consumers to pay for carbon reductions (see Brouwer et al., 2008 for an exception). There is likewise a limited but growing literature on the acceptability of different policy interventions (see King et al. 2008 for an example).

This studentship will address these research gaps by developing and testing methods to identify consumers’ acceptance of and willingness to pay for carbon reductions. This will require investigation of a number of key research areas:

1. Exploring evidence from existing revealed preference data sources (such as low carbon vehicle purchase)
2. Determining the extent to which the presentation of complex environmental information impacts on understanding and use of the data collection tools
3. Collecting new data through stated preference surveys
4. Exploring the key factors which influence the variation of willingness to pay across different market segments

Suggested reading:

Brouwer, R., Brander, L. and van Beukering, B. (2008) “A convenient truth”: air travel passengers’ willingness to pay to offset their CO2 emissions, Climatic Change, 90, 299-313

Anable, J. Lane, B. and Kelay, T. (2006) An evidence base review of public attitudes to climate change and transport behaviour, Final Report for Department for Transport, www.dft.gov.uk

King, S., Marsden, G., Dyball, M., Jopson, A., Harwatt, H. and Kimble. M. (2008) Exploring public attitudes to climate change and the barriers and motivators to behaviour change: Final Report, Report to Department for Transport, www.dft.gov.uk (October 2008)

Microscopic traffic simulation models1 have become extremely popular and widely-used in recent years. Such models are very detailed, as they track the movement and predict the driver behaviour for each individual vehicle in a road network on a second-by-second basis. They are Monte Carlo in nature and allow for a random mix of vehicle types, vehicle performance and driver behaviour. As a consequence they are capable of accurately describing real-world traffic behaviour; however, they are very computationally intensive, and produce outputs of system performance (such as total vehicle travel time or delay) that are "noisy": that is, subject to random error.

Determining the optimal traffic signal settings in an urban road network (that is, the timings that will minimise the total delay) is far from straightforward; there are typically a huge number of local minima so that any simple-minded search procedure will tend to become trapped in the nearest local minimum. When the traffic model that estimates the total delay for any given set of timings is so detailed and is also noisy, this combinatorial optimisation problem becomes even more challenging.

A number of heuristic approaches have been tried, including one known as the cross entropy method2. Work on this was carried out in a recent ITS research project, funded by the Leverhulme Trust, and was shown to provide an efficient, systematic iterative approach to the problem of finding the optimal timings3. Nevertheless the problem is computationally demanding. Other approaches are possible, including one known as a Trust Region method and there are early indications that this might prove to be even more efficient, and allow problems of practical size to be solved. If so, this would be a significant advance in the usefulness of microscopic traffic simulation models.

The proposed PhD research would investigate the formulation and application of the trust region approach, comparing its performance with that of other techniques such as the cross entropy method. The project would therefore build on the findings from the Leverhulme project, and would aim to develop a method that would enable the optimal signal timings to be found, using a microscopic traffic simulation model of road networks of practical size.

It should be noted that, although the problem has been posed here in terms of signal optimisation, there are many optimisation problems in transport that involve the use of Monte Carlo models. The methods to be developed in the project, therefore, will have wide application, beyond that of signal optimisation.

The project would suit someone with strong mathematical skills, and with a keen interest in the application of those skills through modelling and the development of software. For further information, contact either Prof. Mike Maher (m.j.maher@its.leeds.ac.uk) or Dr. Ronghui Liu (r.liu@its.leeds.ac.uk).

References

1. SIAS. Available at http://www.sias.com/ng/spoverview/spintroduction.htm

2. Rubinstein, R. Y. and Kroese, D.P. (2004) The Cross-Entropy Method: a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning. Springer.

3. Maher M.J., Liu R. and Ngoduy D. (2011). Signal optimisation using the cross entropy method.Transportation Research Series C: Emerging Technologies, http://dx.doi.org/10.1016/j.trc.2011.05.018.

4. Ngoduy D. And Maher M.J. (2012). Calibration of second order traffic models using continuous cross entropy method. Transportation Research Series C: Emerging Technologies, 24, 102-121.

Supervisor: Dr Richard Connors

Traffic networks are subject to several sources of variability both within day and day-to-day; the level of travel demand fluctuates while the effective network capacity can be degraded by weather, accidents, road works etc. This stochasticity in both demand and supply means that travellers experience variable travel times, even on repeated journeys.

Several researchers have given attention to the costs associated with travel time variability (TTV) and the subsequent unreliability experienced by travellers. Network equilibrium models have been proposed that include the costs associated with TTV into individuals’ utility functions.

This study focuses instead on the way network flows evolve due to travellers’ experiences, learning behaviour and their resulting travel choices. This requires the development and integration of several model components:

1. Modelling the choice between travel alternatives with uncertain outcomes, where each route and departure time offers a distribution of travel times/costs
2. Representation of individual risk attitudes and perceptions of travel time distributions
3. Models of accrued experience and learning mechanisms
4. Model of traffic flow dynamics and congestion in the network

This modelling framework can then be used to examine not only any equilibrium states that may occur, but also to investigate the evolution of network flows from non-equilibrium conditions and the stability of the system. Different assumptions about the way individual travellers perceive the travel time distributions, learn and make travel choices will affect the resulting network flows.

This studentship will address these issues through a combination of mathematical modelling, analysis and numerical experiments to simulate these network phenomena.

Suggested reading:

Please email Richard Connors to be sent copies of relevant research papers.
R.D.Connors@its.leeds.ac.uk

Supervisor: David Watling

Effective transport planning depends on a detailed understanding of why, when, how and how frequently individuals actually choose to travel, in a range of circumstances, and in representing how the flow of traffic in various transport modes moves in time and space. Typically the surveillance information we have had available, while potentially extensive, does not provide direct evidence of such phenomena. In several countries of the world, then, researchers have looked to new forms of data to address this issue, especially mobile phone traces and global positions systems, yet they have still adopted traditional modelling methods when using the data. The purpose of this project would be to design new mathematical forms of transport and traffic model that specifically aim to exploit such sources. It might be expected that such models, for example, explicit represent the day-to-day adaptive behaviour of travellers, rather than some long-run ‘average’ behaviour, but also would be able to trace the spatial and temporal of traffic. They would also represent the realistic possibility to cancel or re-schedule activities, through mobile communication, if unexpected delays or other events occur. Finally such models would need to be in a form that could exploit data from both new and traditional data sources in a common framework, exploiting the theory of statistical inference. It is intended that this study is mainly a theoretical exercise, developing methodology and testing it on simulated data, but a pilot study (say, among staff/students of the university) could also be feasible if the student were interested.

The project will build on previous research undertaken in several studies, including EPSRC-funded research on representing and analysing the day-to-day dynamic behaviour of individuals in transport systems, work for the Highways Agency on analysing the reliability of traffic flow and travel journeys, and would also build on collaborative work/relationships with several Japanese groups on exploiting mobile phone data and on developing statistical inference methods. As the precise direction of the project unfolds, the supervisor may seek funding to support a research visit of the student to one of the Japanese (and/or other international) groups active in this area.

Suggested reading:

Asakura Y, Hato E & Sugino K (2005). Simulating Travel Behaviour using Location Positioning Data Collected with A Mobile Phone System. In: Simulation Approaches in Transport Analysis (editors Kitamura R & Kuwahara M), Springer, 193-204.

Watling D.P. & Hazelton M. (2003). The dynamics and equilibria of day-to-day traffic assignment models. Networks and Spatial Economics 3, 349-370.

Supervisors: Dr Ronghui Liu

This project will be in collaboration with Transport for London (TfL) under an Academic Research Partnership agreement.

Although a large proportion of public transport trips in London rely on more than one mode, historically each network has used separate performance and network reliability indicators. The Public Performance Measure (PPM) adopted by overground railways measures the percentage of service-trips arriving at their destination under five minutes late, with a focus on service reliability. London bus and underground networks, by contrast, focus on the passenger experience by using the concepts of Excess Wait and Journey Time (EWT and EJT) measured with respect to the scheduled timetable. An earlier study has highlighted the discrepancy in the reliability indicators currently in use and has also highlighted difficulties in obtaining accurate estimates for multi-modal trips.

This project will take a holistic approach to the measures of network performance and reliability for the whole London public transport networks and to develop a comparable procedure for assessing a multi-modal journey performance. The London Oyster card records and service performance monitoring data (i.e. AVL data) will be analysed to create realistic map of the service reliability (using firstly different reliability indicators) and the relative effectiveness of and correlation among the different indicators. The Oyster records provide accurate accounts of individual travellers' origin-destination stations and times.

The first stage of the project will further develop existing techniques for mapping network performance by combining Oyster records, rail service, bus timetable and AVL data. Oyster records can give us demand volumes and individual journey times between points of entry/exit into the system and can also be used to estimate vehicle journey times by network section in combination with other data sources. We will then also be able to compare the effectiveness of different reliability measures and look for any correlations. This will draw on the experience from an earlier DfT-sponsored study which has tackled a number of issues including the definition of Expected Wait Time reliability measure, and the development of aggregate reliability measures and the analysis of services.

The second stage of the project will deal with Oyster records/journeys allowing two or more alternative routes and will consist of creating a method for identifying a pseudo-network of alternative routes (based on Oyster records, existing network models and timetable data). An added benefit of this work will be an improved ability to better predict passenger flows and could help anticipate capacity constraints due to, e.g., network constraints.

Suggested reading, or email R Liu (r.liu@its.leeds.ac.uk) for the papers:

Liu, R. et al (2005) A Model to Assess Public Transport Reliability. Research project report to DfT.
Liu, R. and Sinha, S. (2007) Modelling urban bus service and passenger reliability. Paper presented at the International Symposium on Transportation Network Reliability, The Hague, July 2007.Sorratini, J., Liu, R. and Sinha, S (2008) Assessing bus transport reliability using micro-simulation. Transport Planning & Technology, 31(3), 303-324.

Supervisors: Dr Ronghui Liu

Digital communication technology is changing our lives, the way we drive and the cars we use, at a fast rate never seen before. More and more electronics are being built in our vehicles and vehicles are more connected than ever to remote systems for navigation and information and will soon to each other in what is called cooperative vehicle systems. Adapting advanced mobile or wireless communication technologies, such systems allow linking of infrastructure to vehicle (i2v) and vehicle to vehicle (v2v) at individual level (as opposed to the current average traffic behaviour), in real-time, and covering the entire road network.

Messages pass in i2v and v2v can help motorists coordinate and adapt to surrounding traffic and defuse dangerous situations. For example, when a traffic jam lies ahead and around a bend, the system can automatically alert to the forthcoming vehicles - within a fraction of a second using mobile communication. With wireless communication, the message transmits even faster and further.

The technology enables a completely new range of applications for Intelligent Transport Systems (ITS) applications, “to the extent that they represent a revolution in both the way the transport system could work in the future, and in the scale of benefits available to infrastructure owners and operators and to individual road users” (EU CVIS project).

The project uses a simulation-based methodology for large-scale evaluation of wireless technologies in future ITS application scenarios and to quantify specifically their impact on network congestion, traffic flow stability, safety and potential incident detection, as well as on pollutant emissions and fuel consumptions.

This project will be in close collaboration with communications industry., in particular the BT Mobility Research Centre.

Suggested reading:

Please contact Ronghui Liu (r.liu@its.leeds.ac.uk) to be sent up-to-date relevant reading materials.

Supervisors: Dr Ronghui Liu, Dr Simon Shepherd

Traffic flows through an urban area are determined by drivers' desires in terms of origin and destination and their choice of routes in response to perceived costs of using alternative routes. These costs can be thought of as a combination of time and distance with other influences such as restricted knowledge of the network and user preferences for certain route attributes e.g. avoid right turns or give-ways as much as possible. These costs are influenced by the flow on each route and in general the only way in which a traffic engineer can affect the costs is by the introduction of physical measures such as traffic calming or new capacity or by using the signalised junctions within an area to directly change the costs of using certain routes. This research is concerned with the use of responsive signal control systems and their use to optimise the traffic flows within a network.

Traffic signals have long been used at operational level as a tool to manage the conflicts in and balance the delays to traffic streams at individual intersections as well as at a network level. In contrast to this operational role, traffic signals have been recognised to have a strategic role in that they affect drivers' long-term route choice in a network. Although there is much practical evidence at the operational level and some research experience at the strategic level, these two functions of traffic signals have to date not been integrated.

The aim of this research project is to develop network-wide traffic signal control systems which take into account of day-to-day variability (in demand and supply), of driver learning/adaptation, and of multi-class traffic flow conditions, and which produce stable network conditions and reduced in CO2 emissions in over-saturated conditions.

During the course of the research interactions and constraints on the sub-components of the algorithms forming the Urban Traffic Control framework will be investigated. These include the predictive model performance and implications of feedback errors, calibration issues, implementation of new objective functions, the effect of constraints and possibilities of including demand management as a strategic policy.

Suggested reading, or email R Liu (r.liu@its.leeds.ac.uk) or S Shepherd (s.shepherd@its.leeds.ac.uk) for the papers:

1. Allsop, R E. and Charlesworth J.A. (1977). traffic in a signal controlled road network : an example of different signal timings inducing different routeings. Traffic Engineering and Control Vol 18 (5) pp 262-264.
2. Gartner, N.H.(1989). OPAC: Strategy for demand responsive decentralised traffic signal control. IFAC Control, Computers, Communications in Transportation, Paris, France 1989 pp 241-244.
3. Liu, R., van Vliet, D. and Watling, D. (2006) Microsimulation models incorporating both demand and supply dynamics. Transportation Research, 40A, 125-150.
4. Shepherd, S.P. (1994). Traffic control in over-saturated conditions. Transport Reviews, Vol14, no 1, pp13-43. January 1994.

Supervisor: Dr James Tate

Microscopic traffic simulations coupled with instantaneous emission models have the potential to provide improved assessments of the environmental impact of traffic networks, management strategies and technology implementations. Predictions of vehicle emissions (CO2, NOX, CO etc) using current ‘average-speed’ tools (e.g. www.NAEI.org.uk) do not adequately consider: local speed profiles/ driver behaviour or the benefits of smoothing traffic flow by environmental traffic management strategies e.g. green wave strategies and gating policies. Emerging instantaneous emission models are able to consider for any given speed profile, the influence of road gradient, vehicle loading, engine speed and gear selection on fuel consumption and emissions for Heavy- and Light-duty vehicles. Instantaneous emission models require speed trajectories (or profiles) as input, which can either be obtained from tracking systems (measured) or are an output from traffic micro-simulation models.

The Institute for Transport Studies (ITS) is collaborating with the Institute for internal combustion engines and thermodynamics, Technical University of Graz (TUG, Austria) and has developed, evaluated and applied a coupled traffic micro-simulator and instantaneous emission model (Zallinger, Tate et al, 2008 and 2009). Supported by detailed observations, there is now an opportunity to use these tools to provide improved environmental assessments of traffic networks and environmental management strategies using the best available/ internationally leading Scientific software tools. It is suggested the Headingley district in Leeds is used as demonstration site as a wealth of traffic flow, congestion, driver behaviour and environmental data is available for this area, to support the rigorous calibration and validation of the traffic micro-scopic simulations. There would be an opportunity to under-take a 3-month research placement at the TUG, Austria as part of this PhD project.

References:

http://www.smoothingtrafficflow.org.uk/

Zallinger, M., Tate, J., Hausberger, S. 2008. An Instantaneous Emission Model for the Passenger Car Fleet. Transport and Air Pollution Conference, ISBN 987-3-85125-016-9.

Zallinger, M., Tate, J., Hausberger, S. and Goodman, P. 2009. Evaluation of a coupled micro-scopic traffic simulator and instantaneous emission model. Air Quality Conference 2009, Istanbul, March 2009.

Supervisors: Dr Andrew Smith (Institute for Transport Studies), Dr Roberta Longo (Unit of Health Economics)

Details of Project

Novelty and timeliness
The economic downturn has placed unprecedented pressure on the NHS to bring costs down through efficiency savings (whilst maintaining “front-line” services). The NHS is committed to make efficiency savings of £20 Billion by 2015. There is therefore considerable interest in the magnitude and location of inefficiency within the NHS. The challenging nature of the economic outlook means this focus on the reducing inefficiency within the NHS, one of the largest departmental budgets, is likely expected to persist.
The specific and unique contribution of the proposed research is that it adopts a more sophisticated cost model which seeks to identify where inefficiency resides within the system. The tiered nature of funding, commissioning and delivery means that inefficiency is likely to reside at various levels within the NHS; i.e. Primary Care Trusts, GP practices, hospitals and clinical departments within hospitals. The tiered nature of the NHS structure will remain after the current reorganisation, where GP consortia will replace Primary Care Trusts.
The deeper understanding offered by our proposed model is important, as some parts of the system may be efficient, others less so. Based on this detailed understanding of system inefficiency, budgetary pressure can therefore be targeted at relatively inefficient areas, ensuring that more efficient operations are not unduly penalised, so that quality is maintained. The work thus has clear policy relevance. The methodology has been applied by the lead supervisor in an economic regulatory context for the British Office of Rail Regulation) – see e.g. Smith et. al. (2008) and Smith et. al. (2010). However, it has not been utilised in the health sector, where it can offer innovative, timely and policy relevant insights.

Objectives
The objective is to quantify cost inefficiency (taking account of exogenous factors and quality variation) within the National Health Service. Importantly the research will seek to identify where inefficiency resides within the system – that is, within Primary Care Trusts (and successor organisations) and / or individual GP practices, hospitals (and / or clinical departments within hospitals).

Supervisory team
This proposal brings together of expertise and understanding of the health sector (Dr Longo) with the expertise in efficiency estimation techniques provided by the lead supervisor (Dr Smith).

Training provision for student
Apart from the expertise of both supervisors in their respective areas, the University of Leeds offers a range of relevant masters level modules (e.g. transport and health economics; regulatory economics; econometric methods)

Funding
For those with UK fees status, a full standard studentship consists of academic fees of £3,732, together with a maintenance grant of £13,590. For EU candidates the studentship provides fees only, plus a training support grant. Please note, this particular studentship is not available to applicants with International fees status.

www.its.leeds.ac.uk/courses/phd/funding/

Closing date for applications: 18th April 2011

How to apply
Studentship application form
Academic application form

Queries related to the research topic should be forwarded to Dr Smith or Dr Longo (details above).

For queries related to the application process please contact: Ms Jo Davies, Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, Tel: + 44 (0)113 343 5326, Fax: + 44 (0)113 343 5334, Email: jdavies@its.leeds.ac.uk or Courses@its.leeds.ac.uk

Supervisor: Dr Natasha Merat

This project will build upon some of the current work conducted in this area, using the University of Leeds Driving Simulator.

The driving task is becoming more and more automated and it is now possible for various aspects of driving to be controlled by a range of automation and assistance systems. Examples of such systems include Adaptive Cruise Control (ACC), Intelligent Speed Adaptation (ISA) and Lane Keeping Assistance System (LKAS), as well as various collision warning and avoidance systems, which use radar detection devices. The idea behind the implementation of most such systems is that they will provide assistance and comfort to the driver, reducing the number of road accidents by increasing safety. Indeed, in the case of a highly automated driving scenario, there is no longer a need for the driver to be involved in the driving task, and his/her role moves from one of an operator to a system supervisor, simply monitoring the functioning of the automated vehicle.

This project will investigate driving behaviour in such highly automated vehicles, assessing how automation affects factors such as loss of skill, situation awareness, distraction and workload.
Drivers’ understanding of the automated system and related HMI can also be investigated.

This project is suitable for candidates with a background or interest in the behavioural sciences.

Further reading:

Flemisch, F., Kelsch, J., Löper, C., Schieben, A., & Schindler, J. (2008). Automation spectrum, inner/outer compatibility and other potentially useful human factors concepts for assistance and automation. In D. de Waard, G.R.J. Hockey, P.Nickel, and K.A. Brookhuis (Eds). Human Factors Issues in Complex System Performance (pp. 257-272). Maastricht, The Netherlands: Shaker Publishing.

Martens, M. Pauwelussen, J., Schieben, A., Flemisch, F., Merat, N., Jamson, S. & Caci, R. (2007. Human Factors’ aspects in automated and semiautomatic transport systems: State of the art. Deliverable 3.2.1: http://www.citymobil-project.eu/

Merat, N & Jamson, H. (2009). How do drivers behave in a highly automated car? Proceedings of the Fifth International Driving Symposium on Human Factor in Driver Assessment, Training and Vehicle Design, Montana, USA.

Merat, N. & Jamson, A.H. (2009). Is drivers’ situation awareness influenced by a highly automated driving scenario? In D. de Waard, J. Godthelp, F.L. Kooi, and K.A. Brookhuis (Eds.) (2009). Human Factors, Security and Safety (pp. 1 - 11). Maastricht, the Netherlands: Shaker Publishing.