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This study is concerned with the analysis of the long-run substitution pattern of primary energy sources for the Italian economy, over the period of 1960-1994. A neoclassical model, set in the cost function approach, has been used to retrieve the energy inputs derived demand functions, via Shephard's lemma, using a translog cost function specification. Four primary energy sources have been considered, namely, oil, electricity, natural gas and coal. Recent advances in time series econometric theory have provided tools devices for modelling long-run equilibrium relationships and their associated short-run dynamics jointly. The Engle and Granger (1987) and the Engle and Yoo (1989) cointegration approach has been utilised in this study to estimate the long-run share relationships, while the general to specific methodology has been followed to derive error correction formulations for the adjustment processes. Extensions to time-varying parameter cointegration, carried out in the framework of the structural time series approach, have also been considered. The applications of traditional and time-varying parameter cointegration to the Italian energy market are the main sources of originality of this work. The study is divided into three main parts. The first part introduces the economic and econometric frameworks employed in the analysis. The second part is concerned with the actual empirical analysis. This consists of data description, the structural time series approach and the application of traditional and time-varying parameter cointegration theory to estimate a derived factor demand model. Finally, the third part summarises and discusses the results of the analysis.

Travel to outdoor recreational spaces belongs to a general class of research questions for understanding destination and travel mode choices. In travel demand modelling, discrete choice models (DCMs) have been applied to understand and predict a wide range of choices, such as how people choose among alternative destinations for jobs, homes, shopping, personal services etc. Surprisingly, DCMs have rarely been used to understand and model travel to outdoor recreational spaces. In the current literature for modelling travel to outdoor recreational spaces, the established models are Negative Binomial Regression (NBR) models, such as what was used in the UK NEA studies. However, these NBR models were developed to assess the effects of travel to outdoor recreational spaces at a national level, and they are not intended for assessing choices of individual sites. One reason for this is, as identified by previous studies, is that compared with the DCMs, the NBR models have certain limits on estimating people's choice behaviours. There is, therefore, no existing model that can represent and predict how people choose to travel to outdoor recreational spaces. Given the importance of outdoor recreational activities to urban land use planning and public health, this is a clear gap in the field. The aim of this study is to develop a new travel demand model capable of representing and predicting travel to individual outdoor recreational sites. This is achieved by answering four main research questions: First, how to build the new model for outdoor recreational travel? Secondly, is the estimation accurate enough? Thirdly, to what extent can the new model be transferred to destinations outside the case study area? And, finally, how can city planners and designers use this new method? The new model draws upon ideas from random utility theory that underlies the conventional travel demand models to represent trip generation, trip distribution and mode choice. This research follows the standard modelling procedure: data collection and preliminary analysis, model calibration, model validation and model application. The data are collated from a wide range of sources that, importantly for model transferability, cover all areas in England. The new model has been calibrated for a case study area which spanned 14 selected districts in the North-West region. Validation of the new model is based on estimating the numbers of trips to two outdoor recreational sites (Wigg Island and Wigan Flashes) and to nine English National Parks where data on visitor trips exist. In the final stage of the research, the new model is applied to estimate the changes that would arise from planning and design interventions in existing (Wigg Island and Moore Nature Reserve) and proposed (Arpley Country Park) sites. At the end of this process, it is possible to show that the new model can predict the number of trips to individual destinations and that the model can be transferred to other outdoor recreation sites. Furthermore, the new model presented here is capable of predicting the changes in the volume and catchment of visits to an existing green space after land use planning or urban ecological interventions. This is a completely new theoretical model that is focused on understanding and quantifying the travel choices to outdoor recreation sites, which can inform decision makers by forecasting changes in outdoor recreational travel demand, according to different planning scenarios.

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Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

Past political regimes and socio-economic imbalances have led to the formation of a transport system in the Republic of South Africa (RSA) that is unique to the developing world. Affluent communities in metropolitan cities are situated close to economic activity, whereas the people in need of public transport are situated on the periphery of the cities. This demographic structure is opposite to that of developed countries and complicates both the provision of transport services and the planning process thereof. Multi-Agent Transport Simulation (MATSim) has been identified as an Agent-Based Simulation (ABS) approach that models individual travellers as autonomous entities to create large scale traffic simulations. The initial implementation of MATSim in the RSA successfully simulated private vehicle trips between home and work in the province of Gauteng, proving that there is enough data available to create a realistic multi-agent transport model. The initial implementation can be expanded to further enhance the simulation accuracy, but this requires the incorporation of additional primary and secondary activities into the initial transport demand. This study created a methodology to expand the initial implementation in the midst of limited data, and implemented this process for Gauteng. The first phase constructed a 10% synthetic population that represents the demographic structure of the actual population and identified various socio-demographic attributes that can influence an individual's travel behaviour. These attributes were assigned to the synthetic agents by following an approach that combines probabilistic sampling and rule-based models. The second phase used agents' individual attributes, and census, National Household Travel Survey (NHTS) and geospatial data to transform the synthetic population into a set of daily activity plans - one for every agent. All the agents' daily plans were combined into a plans.xml file that was used as input to MATSim, where the individuals' activity plans were executed simultaneously to model the transport decisions and behaviour of agents. Data deficiencies were overcome by contemplating various scenarios and comparing the macroscopic transport demand patterns thereof to the results of the initial implementation and to actual counting station statistics. This study successfully expanded the initial home-work-home implementation of MATSim by including additional non-work activities in the transport demand. The addition of non-work activities improved the simulation accuracy during both peak and off-peak periods, and the initial demand therefore provides an improved representation of the travel behaviour of individuals in Gauteng. / Dissertation (MEng)--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

This work describe a theoretical model to integrate the parking traffic into the traffic demand modelling. Placed at program VISUM served from the PTV AG Karlsruhe for the traffic system modelling and rating, as well as the EVA demand model developed by LOHSE. / In dieser Arbeit wird ein modelltheoretischer Ansatz beschrieben, der neben dem fließenden Verkehr, auch den ruhenden Verkehr innerhalb der Verkehrsangebots- und Verkehrsnachfragemodellierung berechnet. Dazu dient das von der PTV AG Karlsruhe zur Verfügung gestellte Programm VISUM zur Verkehrsnetzmodellierung und Umlegung, sowie das von LOHSE entwickelte EVA-Modell zur Verkehrserzeugung, -verteilung und -aufteilung als Grundlage.

During the last century, the number of car users has increased as an effect of the increasing population growth. To manage the environmental and infrastructural challenges that comes with a more congested traffic network, traffic planning has become of higher importance to analyze the current traffic state and to predict future capacity challenges and effects of investments. These analysis and evaluations are commonly performed in different traffic analysis tools, where updated and realistic traffic demand needs to be provided to ensure reasonable results. In this thesis, a macroscopic model of Norrköping municipality constructed in the traffic demand modelling software Visum and a daily Origin-Destination(OD)-matrix is considered. The goal of this thesis is to produce a method that modify the current daily demand matrix into hourly demand matrices, called hourly target matrices, that represents a typical weekday. The goal is also to implement and evaluate the OD-estimation algorithm Simultaneous Perturbation Stochastic Approximation (SPSA) to obtain updated and valid demand matrices for the network model of Norrköping. The method of dividing the daily demand matrix into hourly target matrices is based on the paper by Spiess %26 Suter (1990). The method makes use of the available daily trip purpose matrices combined with hourly link flow observations from 96 links in a multiple linear regression model to obtain 24 hourly demand matrices. The resulting matrices are compared with the link flow observations and has different levels of R^2-fit, the maximum fit is 85.79 % and the minimum fit is 55.89 %. The average R^2-value is 72 %. The OD-estimation based on SPSA is performed on the AM and PM peak hours. The algorithm is implemented in Python scripts that are called from Visum where the traffic assignments is calculated. The result is an increase in R^2-value since the link flow difference between estimated and observed link flow is decreased. In total, the estimated link flows are improved by 7.4 % in the AM peak hour and 15.6 % in the PM peak hour. The total absolute change in OD-demand is 3 871 trips for AM peak hour and 6 452 trips for the PM peak hour. The estimated OD-matrices are evaluated by qualitatively visualizing the difference in heat maps and in the quantitative measure structural similarity index. The result is no major structural change from the hourly target matrices which verifies that the information used when the target matrices is produced still is considered. The total demand increased in both hours, with 505 respectively 2 431 trips and flows in some OD-pairs has a very high percental change. This was restricted by adding a penalty term to the SPSA-algorithm on the PM peak hour. The result of penalized SPSA is a much less increase of total demand as well as less percental change of the OD-flows. Though, this to a cost of not decreasing the link flow difference in the same magnitude.