The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

Microscopic traffic simulation is useful tool in analysing traffic and estimating the capacity and level of service of road networks. In this thesis, the four legged Idrottsparken roundabout in the city of Norrkoping in Sweden is analysed by using the microscopic traffic simulation package AIMSUN. For this purpose, data regarding traffic flow counts, travel times and queue lengths were collected for three consecutive weekdays during both the morning and afternoon peak periods. The data were then used in model building for simulation of traffic of the roundabout. The Root Mean Square Error (RMSE) method is used to get the optimal parameter value between queue length and travel time data and validation of travel time data are carried out to obtain the basic model which represents the existing condition of the system. Afterward, the results of the new models were evaluated and compared to the results of a SUMO model for the same scenario model. Based on calibrated and validated model, three alternative scenarios were simulated and analysed to improve efficiency of traffic network in the roundabout. The three scenarios includes: (1) add one free right turn in the north and east sections; (2) add one free right turn in the east and south sections; and (3) addition of one lane in roundabout. The analysis of these scenarios shows that the first and second scenario are only able to reduce the queue length and travel time in two or three legs, while the third scenario is not able to improve the performance of the roundabout. In this research, it can be concluded that the first scenario is considered as the best scenario compared to the second scenario and the third scenario. The comparison between AIMSUN and SUMO for the same scenario shows that the results have no significance differences. In calibration process, to get the optimal parameter values between the model measurements and the field measurements, both of AIMSUN and SUMO uses two significantly influencing parametersfor queue and travel time. AIMSUN package uses parameter of driver reaction time and the maximum acceleration, while SUMO package uses parameter of driver imperfection and also the driver rection time.

The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

Overuse of the Interstate and National Highway Systems has led many urban freeways to suffer from recurrent congestion and high crash rates. One method of ameliorating these problems is through the use of Active Traffic Management (ATM). Within ATM, the practice of speed harmonization is well suited to improving safety and reducing delay. In this study, speed harmonization is applied to a segment of Interstate Highway 35, just south of downtown Austin, Texas. First, the need for congestion and safety improvements will be established. Then, the framework of a speed harmonization system will be developed through a synthesis of speed harmonization best practice. Next, the speed harmonization framework will be evaluated for its impact on efficiency through the development of before and after micro-simulation models. Finally, the trajectory files generated from simulation will be analyzed using surrogate safety measures to assess the safety impact of the proposed speed harmonization system. / text

This research demonstrated the effectiveness of Transit Signal Priority (TSP) in improving bus corridor travel time in a simulated environment using real world data. TSP is a technology that provides preferential treatment to buses at signalized intersections. By considering different scenarios of activating bus signal priority when a bus is 3 or 5 minutes behind schedule, it was demonstrated that bus travel times improved significantly while there is little effect on delays for crossing street traffic. The case of providing signal priority for buses unconditionally resulted in significant crossing street delays for some signalized intersections with only minor improvement to bus travel time over both scenarios of Conditional priority. Evaluation was conducted by using micro-simulation and statistical analysis to compare Unconditional and Conditional TSP with the No TSP scenario. This evaluation looked at performance metrics (for buses and all vehicles) including average speed profiles, average travel times, average number of stops, and crossing street delay. Different Conditional TSP scenarios of activating TSP when a bus is 3 or 5 minutes behind schedule were considered. The simulation demonstrated that Conditional TSP significantly improved bus travel times with little effect on crossing street delays. The results also showed that utilizing TSP technology reduced the environmental emissions in the I-Drive corridor. Furthermore, field data was used to calculate actual passenger travel time savings and benefit cost ratio (7.92) that resulted from implementing conditional TSP. Conditional TSP 3 minutes behind schedule was determined to be the most beneficial and practical TSP scenario for real world implementation at both the corridor and regional levels.

We are recently witnessing an increase in large-scale micro/individual/- granular level behavioural data. Such data has been proven to have the capacity to aid the development of more accurate simulations that will ef- fectively predict the behaviours of complex systems. Despite this increase, the literature has failed to produce a structured modelling approach that will effectively take advantage of such granular data, in modelling com- plex systems that involve social phenomenons (i.e. social complex sys- tems). In this thesis, we intend to bridge this gap by answering the question of how novel structural frameworks, that systematically guides the use of micro-level behaviour and attribute data, directly extracted from the ba- sic entities within a social complex system can be created. These frame- works should involve the systematic processes of using such data to di- rectly model agent attributes, and to create agent behaviour rules, that will directly represent the unique micro entities from which the data was ex- tracted. The objective of the thesis is to define generic frameworks, that would create agent based micro simulations that would directly reflect the target complex system, so that alternative scenarios, that cannot be inves- tigated in the real system, and social policies that need to be investigated before being applied on the social system can be explored. In answering this question, we take advantage of the pros of other model- ing techniques such as micro simulation and agent based techniques in cre- ating models that have a micro-macro link, such that the micro behaviour that causes the macro emergence at the simulation’s global level can be easily investigated. which is a huge advantage in policy testing. We also utilized machine learning in the creation of behavioural rules.This created agent behaviours that were empirically defined. Therefore, this thesis also answers the question of how such structural framework will empirically create agent behaviour rules through machine learning algorithms. In this thesis we proposed two novel frameworks for the creation of more accurate simulations. The concepts within these frameworks were proved using case studies, in which these case studies where from different so- cial complex systems, so as to prove the generic nature of the proposed frameworks. In concluding of this thesis, it was obvious that the questions posed in the first chapter had been answered. The generic frameworks had been created, which bridged the existing gap in the creation of accurate mod- els from the presently available granular attribute and behavioral data, al- lowing the simulations created from these models accurately reflect their target social complex systems from which the data was extracted from.

La TVA est une taxe ad valorem facturée par les entreprises à leurs clients mais dont seule la différence entre la taxe collectée par l’entreprise et celle qu’elle a supportée sur ses achats est reversée à l’ Etat. Selon ce mécanisme, la TVA repose uniquement sur la consommation finale et semble adaptée aux principes d’une taxe indirecte optimale pour la maximisation du bien-être collectif. Cependant, dans les PED, la taille de l’assiette, les problèmes de remboursement des crédits de TVA et le non assujettissement de certains producteurs, modifient le fonctionnement de la taxe. Afin d’appréhender l’impact de la TVA dans les PED avec un nouveau regard, les effets de la taxe sur l’allocation des ressources et la distribution des revenus sont étudiés à l’aide d’un Modèle d’ Equilibre Général Calculable ad hoc appliquée à des problématiques spécifiques au Niger et au Sénégal. Les résultats des simulations montrent que l’analyse de l’impact de la TVA ne peut pas se contenter d’un discours commun, ni du point de vue du consommateur, ni de celui du producteur. / In theory, VAT has always been considered as a consumption tax (Lauré, 1957). Liable producers transfer to the government the difference between the VAT collected on sales and the VAT paid on their inputs. VAT is therefore a tax on final consumption born by the consumer and collected by the producer. With tax abatement principle, VAT seems adapted to the principals of an optimal indirect tax for the maximization of the collective wellbeing. However, if VAT exemptions are implemented or if the tax administration is inefficient in issuing refunds for VAT credits or simply due to non-liable producers, VAT increases producer’s tax burden and viewing the VAT only as a consumption tax becomes inaccurate. In order to take into account these complexities, we built the first Computable General Equilibrium Model in order to shed some light on resources allocation and income distributions of the tax in Niger and Senegal. Simulation results show that an analysis of the VAT’s impact cannot rely only on a common line, neither from the consumer’s nor the producer’s point of view.

Depuis quelques années, investir dans le secteur agricole est devenu un des objectifs prioritaires des politiques économiques de lutte contre la pauvreté en Afrique Subsaharienne. Or, dans la perspective des Objectifs du Millénaire pour le Développement, les pays doivent également faire face à la nécessité croissante d’orienter leurs ressources budgétaires limitées vers le financement de leurs dépenses sociales. Dans ce contexte, en prenant appui sur le cas spécifique de l’Ouganda, l’objectif de cette thèse est de savoir dans quelle mesure il est possible de concilier simultanément ces impératifs sociaux et économiques en exploitant les effets de croissance de certaines dépenses sociales dans la sphère agricole. La santé des ménages agricoles joue alors le rôle de variable stratégique de l’analyse parce qu’elle est à la fois une dimension du bien être dont sont privés les plus pauvres et parce qu’elle détermine également leur niveau de productivité. Dans un premier temps, les facteurs socioéconomiques qui influencent la dépense en santé des Ougandais ont été identifiés. Ils révèlent la complexité de ce processus de dépense, ses relations avec la morbidité et le choix du type de fournisseurs de soins ou les interdépendances entre les différentes catégories de dépenses en soins. Dans un deuxième temps, l’analyse porte une seconde composante essentielle de la santé : la diversité alimentaire. Elle révèle une hétérogénéité inobservée a priori entre les ménages ougandais ainsi que les principaux déterminants socio-économiques de leur demande de diversité alimentaire. Dans un troisième temps, l’accent est mis sur la nature et l’intensité du lien entre les différentes catégories de dépense en santé et la productivité des agriculteurs ougandais. Enfin, une partie de ces résultats micro-économétriques est replacée dans un cadre d’Equilibre Général pour simuler les impacts macroéconomiques et microéconomiques d’une subvention publique du prix des produits de santé non marchands en Ouganda. Les résultats obtenus montrent alors qu’il semble possible de mener dans ce pays des politiques répondant simultanément aux besoins sociaux de court terme de la population et aux impératifs économiques de plus long terme de croissance pro-pauvres, et d’en maximiser les effets à moindre coût en ciblant certains types de ménages et/ou certains types de services de santé. / In recent years, investment in agriculture has become one of the priority objectives of economic policies to fight against poverty in Sub-Saharan Africa. However, from the perspective of Millennium Development Goals, African countries must also deal with the increasing need to focus their scarce budgetary resources towards social spending. In this context, taking the specific case of Uganda, the objective of this thesis is to understand how it is possible to simultaneously achieve these social and economic goals by exploiting the growth effects of some social spending in the agricultural sector. Farmers ‘health plays a critical role of the analysis because it is both a dimension of well-being which are denied to the poor and a determinant of their productivity. At first, socio-economic factors that influence health spending of Ugandans have been identified. They reveal the complexity of this expenditure process, its relationship with morbidity and the choice of healthcare providers, or the interdependencies between the different types of healthcare expenditures. Next, the analysis focuses on a second essential component of health: food diversity. It reveals an a priori unobserved heterogeneity between Ugandan households and the main socio-economic determinants of their demand for food diversity. In a third step, emphasis is placed on the nature and intensity of the link between the different categories of health expenditure and the productivity of Ugandan farmers. Lastly, some of these previous micro-econometric results are replaced in a general equilibrium framework to simulate the macroeconomic and microeconomic impacts of government subsidies on health in Uganda. Results show that it seems possible to design policy in this country which meets simultaneously the short-term social needs of population and the longer-term economic imperatives of pro-poor growth. They also show that it is possible to maximize the effects of these policies at lower cost by targeting certain types of households and / or certain types of health services.

According to a recent report by the U.S. Senate Joint Economic Committee, the costs of domestic air traffic delays were estimated to be a staggering $41 billion in 2007. Of this, $19.1 billion was attributed to airline operating costs and $12 billion was attributed to passenger delays. In instances of irregular operations, an airline's primary objective is to get operations back to normal as quickly as possible while incurring minimal cost. Historically, airlines have prioritized recovering schedules for aircraft and crew before attempting to reaccommodate disrupted passengers. The literature has not examined how proactive movement of passengers can be used to mitigate the impact of irregular operations. This work bridges this gap by exploring how early standby fees (which influence customer behavior) can be utilized to improve operations. This research uses a variety of public and private data sources to construct a micro-level simulation to explore the impact that early standby pricing policies have on aggregate and average delay for disrupted passengers. Three distinct modules developed for use in the simulation have broader applications to the research community: (1) logic for a passenger reaccommodation algorithm; (2) a model that predicts the number of early standby passengers; and (3) a model to predict the probability a passenger misses a connecting flight. Preliminary results confirm the hypothesis that early standby pricing policies affect both profitability and operational performance. A simple example illustrates that an airline faces an important tradeoff when setting an early standby pricing policy: the early standby fee that maximizes an airline's profitability is different from the early standby fee that yields the lowest aggregate and average delay metrics for customers. This dissertation proposes a new product that remedies these competing objectives by improving them both simultaneously.

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