Modelling behavioural responses to tolling by microsimulation

Over the past few decades there has been a renewed interest in road pricing. This has come about due to the increasing realisation of the negative effects of unrestrained car use, such as, the impact of congestion on the economy and pollution on the environment, to name a few. In this respect, road pricing offers a mechanism for controlling demand. To date, road pricing has been applied to city centres, sections of motorways, individual lanes, bridges, tunnels to name but a few examples. Charges can also be further refined and varied according to the time of day, day of the week, traffic volumes, vehicle types, vehicle occupancy, etc.Moreover, the evaluation of transport schemes has become reliant on the careful consideration of all possible outcomes. An important technology which has been developed is traffic microsimulation modelling. This enables transport professionals to replicate by computer simulation the behaviour of individual vehicles within an exact representation of the actual road network. The robustness of microsimulation modelling, nevertheless, depends on the accuracy with which actual traffic behaviour is represented. In the case of road pricing the key element lies in predicting motorist’s behavioural responses when confronted with tolls.There are various scenarios in which tolls could be applied and some may offer alternative routes, alternative modes, etc. Yet, these all depend on an individual’s willingness to pay to avoid a congested trip that comprises either increased journey times (measured as ‘Value Of Time’) or a more unpredictable journey time (measured as ‘Value Of Reliability’).The purpose of this research is to advance the modelling of trip-makers behavioural responses to tolls in a PC-simulated environment. The objectives are therefore: (1) to determine the modelling procedure that proves most adequate to the requirements of the modelling of tolls, (2) to establish the necessity of including a VOT and VOR element in the route choice system of a model, (3) to review VOT and VOR values in the literature and to identify the variables that account for different valuations, (4) to assess whether values from literature are applicable to a UK context, and in case they are not (5) to develop a calibrated and validated microsimulation model that can be used in future research to derive UK values. From this modelling exercise, conclusions are derived about the challenges of modelling congested networks with highly variable travel times and its implications in the inclusion of VOT and VOR in simulation. Finally, recommendations for future research are presented based on the findings of this research

Combining robustness and recovery for airline schedules

In this thesis, we address different aspects of the airline scheduling problem. The main difficulty in this field lies in the combinatorial complexity of the problems. Furthermore, as airline schedules are often faced with perturbations called disruptions (bad weather conditions, technical failures, congestion, crew illness…), planning for better performance under uncertainty is an additional dimension to the complexity of the problem. Our main focus is to develop better schedules that are less sensitive to perturbations and, when severe disruptions occur, are easier to recover. The former property is known as robustness and the latter is called recoverability. We start the thesis by addressing the problem of recovering a disrupted schedule. We present a general model, the constraint-specific recovery network, that encodes all feasible recovery schemes of any unit of the recovery problem. A unit is an aircraft, a crew member or a passenger and its recovery scheme is a new route, pairing or itinerary, respectively. We show how to model the Aircraft Recovery Problem (ARP) and the Passenger Recovery Problem (PRP), and provide computational results for both of them. Next, we present a general framework to solve problems subject to uncertainty: the Uncertainty Feature Optimization (UFO) framework, which implicitly embeds the uncertainty the problem is prone to. We show that UFO is a generalization of existing methods relying on explicit uncertainty models. Furthermore, we show that by implicitly considering uncertainty, we not only save the effort of modeling an explicit uncertainty set: we also protect against possible errors in its modeling. We then show that combining existing methods using explicit uncertainty characterization with UFO leads to more stable solutions with respect to changes in the noise's nature. We illustrate these concepts with extensive simulations on the Multi-Dimensional Knapsack Problem (MDKP). We then apply the UFO to airline scheduling. First, we study how robustness is defined in airline scheduling and then compare robustness of UFO models against existing models in the literature. We observe that the performance of the solutions closely depend on the way the performance is evaluated. UFO solutions seem to perform well globally, but models using explicit uncertainty have a better potential when focusing on a specific metric. Finally, we study the recoverability of UFO solutions with respect to the recovery algorithm we develop. Computational results on a European airline show that UFO solutions are able to significantly reduce recovery costs

Economics of drought preparedness and response in irrigated agriculture

The impact of recent severe droughts throughout the United States, the potential for climate change to intensify the frequency and severity of drought, and discussion about the future of government assistance in agriculture highlight the need for a transition from drought as ‘disaster’ to drought as ‘managed risk’. However, guidance for agricultural producers about optimal drought preparedness and response is insufficient. It is particularly unclear what optimal drought preparedness and response should look like, in practice, for farm systems with uncertain water supplies and intra- and inter-year dynamics. A mathematical programming model that captures the stochastic and dynamic aspects of an irrigated row crop farm is developed and used to explore the nature of optimal drought preparedness and response. Results indicate several important characteristics. First, drought has the potential to generate heterogeneous impacts, even across a set of homogeneous farms. Second, a farm system with inter-year dynamics can continue to experience the effects of drought after the drought itself subsides; additionally, the effects of drought in one year can intensify the impact of drought in subsequent years. Third, in the presence of discount and interest rates, crop diversification does not maximize expected profit, even though it is often considered a drought management tool. Fourth, the primary effect of water supply uncertainty is the abandonment of more fall-prepared fields. Hence, the multi-peril crop insurance program’s prevented planting provision is identified as an optimal drought preparedness tool, even if unsubsidized. Finally, the predicted effects of climate change for snowmelt-dependent farm systems require distinctly different forms of adaptation, and cause profit losses of different magnitudes. Because the model captures both intra- and inter-year dynamics, it provides 1) a more thorough understanding of the complex tradeoffs that producers face when preparing for and responding to drought, 2) a more complete picture of the dynamic impacts of drought, and 3) important insights about the administration of drought assistance programs. Lastly, it elucidates the meaning of optimal drought preparedness; a notion that has received increased attention in the policy arena, but whose practical form has been only vaguely alluded to

Modelling, solution and evaluation techniques for Train Timetable Rescheduling via optimisation

It is common on railways for a single train delay to cause other trains to become delayed, multiplying the negative consequences of the original problem. However, making appropriate changes to the timetable in response to the initial delay can help to reduce the amount of further delay caused. In this thesis, we tackle the Train Timetable Rescheduling Problem (TTRP), the task of finding the best combination of timetable changes to make in any given traffic scenario. The TTRP can be formulated as an optimisation problem and solved computationally to aid the process of railway traffic control. Although this approach has received considerable research attention, the practical deployment of optimisation methods for the TTRP has hitherto been limited. In this thesis, we identify and address three outstanding research challenges that remain barriers to deployment. First, we find that existing TTRP models for large station areas are either not sufficiently realistic or cannot be solved quickly enough to be used in a real-time environment. In response, a new TTRP model is introduced that models the signalling system in station areas in fine detail. Using a new set of real instances from Doncaster station, we show that our tailored solution algorithm can obtain provably optimal or near-optimal solutions in sufficiently short times. Second, we argue that existing ways of modelling train speed in TTRP models are either unrealistic, overly complex, or lead to models that cannot be solved in real-time. To address this, innovative extensions are made to our TTRP model that allow speed to be modelled parsimoniously. Real instances for Derby station are used to demonstrate that these modelling enhancements do not incur any extra computational cost. Finally, a lack of evidence is identified concerning the fairness of TTRP models with respect to competing train operators. New evaluation techniques are developed to fill this gap, and these techniques are applied to a case study of Doncaster station. We find that unfairness is present when efficiency is maximised, and find that it mostly results from competition between a small number of operators. Moreover, we find that fairness can be improved up to a point by increasing the priority given to local trains. This work represents an important step forward in optimisation techniques for the TTRP. Our results, obtained using real instances from both Doncaster and Derby stations, add significantly to the body of evidence showing that optimisation is a viable approach for the TTRP. In the long run this will make deployment of such technology more likely

Socio-Hydrology: The New Paradigm in Resilient Water Management

During the third decade of the 21st century, human societies across the world are facing significant water-related problems, such as ecosystem degradation, groundwater depletion, natural and anthropogenic droughts and floods, water-borne health issues, and deforestation. These problems are exacerbated by climate change, a phenomenon that has been accelerated due to human intervention in natural systems since the industrial revolution. There is an urgent need to better understand the interaction of hydrological systems in terms of climate variability and the anthropogenic factors that contribute to the dynamics and resilience of coupled human–water systems and effective risk management in the area of water resource management. Socio-hydrology is an interdisciplinary field that integrates natural and social sciences and aims to study the long-term dynamics of bidirectional feedback in coupled human–water systems. This book on socio-hydrology aims to compile cross-disciplinary scientific endeavors and innovations in research on the development, education, and application of coupled human–water systems. The articles published in this book represent diverse and broad aspects of water management in the context of socio-hydrology systems around the globe. The articles and ideas presented in this book represent a significant source of references for interdisciplinary water science programs and provide an excellent guide for experts involved in the future planning and management of water resources. This book is dedicated to friends of the Green Water-Infrastructure Academy and those who pursue cross-disciplinary water research, education, and management

Wildfires in Galicia: causality, forest policy and risk in forest management

Forestlands are one of the most important environmental resources. If a better conservation and management was achieved, then the social welfare and economic wealth would be higher. To obtain a better forest conservation, wildfires can be prevented by public policies. The first chapter of this thesis is focused on providing suggestions to improve the current public policies in order to reduce the wildfire occurrence. In this chapter spatial econometric models are developed in order to analyse the relations between socioeconomics, environmental and climatic variables with wildfires occurrence. In particular, Chap. I studies the heterogeneous behaviour of wildfire patterns within the Galician Forest Districts. The main results of this chapter highlight the importance of the role of socioeconomic factors in explaining wildfire occurrence. Based on these results, some policy guidelines are suggested. Chaps. II and III study the forest insurance as a tool for reducing the economic risk and guarantee the production of environmental services. The proposed insurance model includes the coverage of restoration costs and timber damages after wildfires. Hence, the production of environmental services will be guaranteed and the forest investment will reduce their volatility. Thus, in Chap. II the influence of forest insurance is analysed by employing Net Present Value model (NPV). In the Chap. III, forest insurance is studied as a tool to incentive the landowner to produce environmental services. Therefore, private and social forest valuation is conditioned by this incentive; so that the optimal forest rotation considers the valuation of environmental goods and services. In the last chapter, Chapter IV, the demand for forest insurance contracts is studied. A survey is conducted among 210 landowners and forest managers. A choice experiment of some possible insurance policies is included in this survey. The proposed insurance attributes contain both timber and restoration cost coverage; and forest certification, included as a requirement. The insurance demand according to landowners or forest managers’ preferences and their socioeconomic features is estimated. From these results, the willingness to pay for the forest insurance program is obtained (3.64 €/Ha). Finally, it can be concluded that the insurance demand is affected by both, insurance attributes and socioeconomic features of the forest managers

The economics of abortion: costs, impacts, values, benefits, and stigma

Objective: To systematically search for and synthesize the social science literature on the consequences of abortion-related care, abortion policies, and abortion stigma on economic costs, benefits, impacts, and values at the micro- (i.e., abortion seekers and their households), meso- (i.e., communities and health systems), and macro- (i.e., societies and nation states) levels. Methods: We conduct a scoping review using the PRISMA extension for Scoping Reviews (PRISMA-ScR) tool. Studies reporting on qualitative and/or quantitative data from any world region are considered. For inclusion, studies must examine one of the following economic outcomes at the micro- , meso-, and/or macro-levels: costs, benefits, impacts, and/or value of abortion-related care or abortion policies. Results: Our searches yielded 19,653 unique items, of which 365 items were included in our synthesis. The economic levels are operationalized as follows: at the micro-level we examine individual decision making, at the meso-level we consider the impact on abortion services and medical systems in context, and at the macro the impact of access to abortion services on broader indicators (e.g., women’s educational attainment). At the micro-economic level, results indicate that economic costs and consequences play an important role in women’s trajectories to abortion-related care. However, the types of costs that are studied are often unclear and tend to focus narrowly on costs to and at health facilities. Our evidence suggests that a much broader range of economic costs, impacts and values are likely to be important in a wide range of contexts. At the meso-economic level, we find that adapting to changes in laws and policies is costly for health facilities, and that financial savings can be realized while maintaining or even improving quality of abortion care services. At the macro-economic level, the evidence shows that post-abortion care services are expensive and can constitute a substantial portion of health budgets. Public sector coverage of abortion costs is sparse, and women bear most of the financial costs. Conclusions: This scoping review has uncovered a wealth of information about the economic costs, impacts, value, and benefits of abortion services and policies. The review also points to knowledge gaps, such as the ways in which women perceive the intersections between costs and quality of care, safety, and risk. Similarly, there is a dearth of methodological variation and innovation, with an abundance of studies using costing methods and regression analysis while other tools seen elsewhere in behavioral studies (such as discrete choice experiments and randomized control trials) are underexploited. This study provides a conceptual mapping of the economics of abortion in a new way, reinforcing some findings already well known while uncovering underexplored questions and methods

Dementia in Parkinson’s Disease

An estimated 50% to 80% of individuals with Parkinson’s disease experience Parkinson’s disease dementia (PDD). Based on the prevalence and clinical complexity of PDD, this book provides an in-depth update on topics including epidemiology, diagnosis, and treatment. Chapters discuss non-medical therapies and examine views on end-of-life issues as well. This book is a must-read for anyone interested in PDD whether they are a patient, caregiver, or doctor