With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using a Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand.

Recent advances in the optimization of fixed time traffic signals have demonstrated a move towards the use of genetic algorithm optimization with traffic network performance evaluated via stochastic microscopic simulation models. This dissertation examines methods for improved optimization. Several modified versions of the genetic algorithm and alternative genetic operators were evaluated on test networks. A traffic simulation model was developed for assessment purposes. Application of the CHC search algorithm with real crossover and mutation operators were found to offer improved optimization efficiency over the standard genetic algorithm with binary genetic operators. Computing resources are best utilized by using a single replication of the traffic simulation model with common random numbers for fitness evaluations. Combining the improvements, delay reductions between 13%-32% were obtained over the standard approaches. A coding scheme allowing for complete optimization of signal phasing is proposed and a statistical model for comparing genetic algorithm optimization efficiency on stochastic functions is also introduced. Alternative delay measurements, amendments to genetic operators and modifications to the CHC algorithm are also suggested.

Microscopic traffic simulation has proven to be a useful tool for analysis of varioustraffic systems. This thesis consider microscopic traffic simulation of rural roads andthe use of traffic simulation for evaluation of driver assistance systems. A traffic simulation modelling framework for rural roads, the Rural Traffic Simulator(RuTSim), is developed. RuTSim is designed for simulation of traffic on singlecarriageway two-lane rural roads and on rural roads with separated oncoming trafficlanes. The simulated traffic may be interrupted by vehicles entering and leaving themodelled road at intersections or roundabouts. The RuTSim model is applied for analysis of rural road design alternatives.Quality-of-service effects of three alternatives for oncoming lane separation of anexisting Swedish two-lane road are analysed. In another model application, RuTSimis used to simulate traffic on a Dutch two-lane rural road. This application illustratesthat the high level of model detail of traffic micro-simulation may call for use of differentmodelling assumptions regarding driver behaviour for different applications,e. g. for simulation of traffic in different cultural regions. The use of traffic simulation for studies of driver assistance systems facilitateimpact analyses already at early stages of the system development. New and additionalrequirements are however then placed on the traffic simulation model. It isnecessary to model both the system functionality of the considered driver assistancesystem and the driver behaviour in system equipped vehicles. Such requirements canbe analysed using RuTSim. In this thesis, requirements on a traffic simulation model to be used for analysisof road safety effects of driver assistance systems are formulated and investigatedusing RuTSim. RuTSim is also applied for analyses of centre line rumble stripson two-lane roads, of an overtaking assistant and of adaptive cruise control. Thesestudies establish that the assumptions made regarding driver behaviour are crucialfor traffic simulation based analyses of driver assistance systems.

Microscopic traffic simulation software have gained significant popularity and are widely used both in industry and research mainly because of the ability of these tools to reflect the dynamic nature of the transportation system in a stochastic fashion. To better utilize these software, it is necessary to understand the underlying logic and differences between them. A Car-following model is the core of every microscopic traffic simulation software. In the context of this research, the thesis develops procedures for calibrating the steady-state car-following models in a number of well known microscopic traffic simulation software including: CORSIM, AIMSUN, VISSIM, PARAMICS and INTEGRATION and then compares the VISSIM and INTEGRATION software for the modeling of traffic signalized approaches. The thesis presents two papers. The first paper develops procedures for calibrating the steady-state component of various car-following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car-following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways. Second, the study compares the logic used in both the VISSIM and INTEGRATION software, applies the software to some simple networks to highlight some of the differences/similarities in modeling traffic, and compares the various measures of effectiveness derived from the models. The study demonstrates that both the VISSIM and INTEGRATION software incorporate a psycho-physical car-following model which accounts for vehicle acceleration constraints. The INTEGRATION software, however uses a physical vehicle dynamics model while the VISSIM software requires the user to input a vehicle-specific speed-acceleration kinematics model. The use of a vehicle dynamics model has the advantage of allowing the model to account for the impact of roadway grades, pavement surface type, pavement surface condition, and type of vehicle tires on vehicle acceleration behavior. Both models capture a driver's willingness to run a yellow light if conditions warrant it. The VISSIM software incorporates a statistical stop/go probability model while current development of the INTEGRATION software includes a behavioral model as opposed to a statistical model for modeling driver stop/go decisions. Both software capture the loss in capacity associated with queue discharge using acceleration constraints. The losses produced by the INTEGRATION model are more consistent with field data (7% reduction in capacity). Both software demonstrate that the capacity loss is recovered as vehicles move downstream of the capacity bottleneck. With regards to fuel consumption and emission estimation the INTEGRATION software, unlike the VISSIM software, incorporates a microscopic model that captures transient vehicle effects on fuel consumption and emission rates. / Master of Science

Vehicles that today are searching for a place to stop impacts other vehicles in cities. It can also be seen that the number of vehicles that desire to conduct a pick-up or drop-off increases with an increased number of ride-hailing services. New technology routing advises for such vehicles could improve the overall performance of a traffic network. This thesis analyses therefore how a routing strategy, that incorporates curbside availability information, can impact the performance. To analyse the effects of how curbside availability information can impact a network, a microscopic traffic simulation model was constructed in PTV Vissim and two different routing strategies were developed and implemented in the model. One strategy that represents the scenario of today where vehicles searches the traffic network while attempting to make a stop at a pick-up and drop-off slot. The second strategy routes vehicles to a slot based on curbside availability information. This strategy directs vehicles to an available slot and therefore reduces the time a vehicle is cruising before a stop has been made. A simulation experiment was set-up to compare the strategies that were developed with different penetration rates of vehicles that desired to stop. The results shows that the average travel time can be reduced with up to 25.2\% when vehicles have information compared to the scenario with no information. Similar findings is identified for average delay per vehicle which is reduced with up to 49.0\% and average traveled distance decreased with up to 15.5\%. The results of this thesis needs however to be studied in a wider context in order to draw reliable conclusions. The thesis propose further investigations whether a strategy that incorporates availability information can be implemented in a real world scenario and further investigations whether an implementation of a strategy like this would be socioeconomic beneficial.

Automated shuttles are designed to provide a clean transportation and improve access to areas such as where travelers have to walk long distances to/from bus stops. The introduction of automated shuttles in the road network might affect the safety of pedestrians and cyclists as well as traffic performance of motorized vehicles. Several demonstration trials are being conducted to study how automated shuttles operate in real traffic conditions, but they are limited to few vehicles and evaluations of traffic effects at higher penetration rates are not possible. Traffic simulation is a tool that can be used to study effects on traffic performances at different penetration rates of e.g., automated shuttles. However, automated shuttles have not yet been modeled, calibrated, and validated in microscopic traffic simulation tools. Therefore, the objective of this thesis is to model, calibrate and validate automated shuttle’s behavior using the simulation tool SUMO and data collected from the demonstration trial on the area of campus Valla Linköping University, Sweden. The pilot study consists of two automated shuttles, and they operate on a 2.1 km fixed route. The collected data by one of the automated shuttles is analyzed with a focus on the free driving behavior. The analysis shows that the automated shuttle has different maximum operation speeds at different locations and defining one value for the maximum speed when setting up the simulation is not enough. Therefore, virtual speed limits are derived by mimicking the maximum operation speed of the shuttle from the data and used to define segment specific speed limits in the simulation. Additionally, the data is used to calibrate the acceleration and deceleration parameters. The Krauss and the IDM car-following models have been investigated by calibrating the acceleration and deceleration parameters for the free driving situation. The results indicate that both the Krauss and IDM car-following models follows the general trend of the speed and acceleration profiles. The speed profiles produced with the IDM model have smoother profiles at the start and end of acceleration and deceleration phases while in Krauss model the transition of the speed change is more direct and there are in principle no delays for reaction. Although the IDM model performs slightly better for the free driving situation, it can be of interest to consider both models for the calibration of interactions with other roads users since both models are able to capture the general trend of the speed and acceleration profiles. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

This dissertation seeks to advance the modeling of network flows over time for the purposes of improving evacuation planning. The devastation created by Hurricanes Katrina and Rita along the Gulf Coast of the United States in 2005 have recently emphasized the need to improve evacuation modeling and planning. The lessons learned from these events, and similar past emergencies, have highlighted the problem of congestion on the interstate and freeways during an evacuation. The intent of this research is to develop evacuation demand management strategies that can reduce congestion, delay, and ultimately save lives during regional evacuations. The primary focus of this research will concern short-notice evacuations, such as hurricane evacuations, conducted by automobiles. Additionally, this dissertation addresses some traffic flow and optimization deficiencies concerning the modeling of congested network flows. This dissertation is a compilation of three manuscripts. Chapters 3 and 4 examine modeling network flows over time with congestion. Chapter 3 demonstrates the effects of congestion on flows using a microscopic traffic simulation software package, INTEGRATION. The flow reductions from the simulation are consistent with those found in several empirical studies. The simulation allows for the examination of the various contributing factors to the flow reductions caused by congestion, including level of demand, roadway geometry and capacity, vehicle dynamics, traffic stream composition, and lane changing behavior. Chapter 4 addresses some of the modeling and implementation issues encountered in evacuation planning and presents an improved modeling framework that reduces network flows due to congestion. The framework uses a cell-based linear traffic flow model within a mixed integer linear program (MILP) to model network flows over time in order to produce sets of decisions for use within an evacuation plan. The traffic flow model is an improvement based upon the Cell Transmission Model (CTM) introduced in Daganzo (1994) and Daganzo (1995) by reducing network flows due to congestion. The flow reductions are calibrated according to the traffic simulation studies conducted in Chapter 3. The MILP is based upon the linear program developed in Ziliaskopoulos (2000); however, it eliminates the "traffic holding" phenomenon where it cannot be implemented realistically within a transportation network. This phenomenon is commonly found in mathematical programs used for dynamic traffic assignment where the traffic is unrealistically held back in order to determine an optimum solution. Lastly, we propose additional constraints for the MILP that improve the computational performance by over 90%. These constraints exploit the relation of the binary variables based on the network topology. Chapter 5 applies the improved modeling framework developed in Chapter 4 to implement a demand management strategy called group-level staging -- the practice of evacuating different groups of evacuees at different times in order to reduce the evacuation duration. This chapter evaluates the benefits of group-level staging, as compared to the current practice of simultaneous evacuation, and explores the behavior of the modeling framework under various objective functions. / Ph. D.

Traffic simulation is a safe and efficient tool to investigate infrastructural changes as well as traffic conditions. This master thesis aims to analyse a microscopic traffic simulation method for evaluation of bus terminal capacity. The evaluation is performed by investigating a case study of the bus terminal at Norrköping travel centre. The analysed method, referred to as terminal logic in the thesis, uses a combination of time based and event based simulation. Through the combination of time and event, it is possible to capture all movements within the terminal for individual vehicles. The simulation model is built in the software Vissim. A new travel centre for Norrköping is under development. Among the reasons for a new travel centre is the railway project Ostlänken in the eastern part of Sweden. An evaluation of the bus terminal is interesting due to a suspicion of overcapacity and the opportunity of redesigning. To investigate both the terminal capacity and the terminal logic, three scenarios were implemented. Scenario 1: Current design and frequency Scenario 2: Current design with higher frequency Scenario 3: Decreased number of bus stops with current frequency The results from the scenarios confirm the assumption of overcapacity. The capacity was evaluated based on several different measures, all indicating a low utilization. Even so, the utilization was uneven over time and congestion could still occur when several buses departed at the same time. This was also seen when studying the simulation, which showed congestions when several buses departed at the same time. The case study established the terminal logic to be useful when evaluating capacity at bus terminals. It provides a good understanding of how the terminal operates and captures the movements. However, it was time-consuming to adjust the logic to the studied terminal. This is a disadvantage when investigating more than one alternative. The thesis resulted in two main conclusions. Firstly, a more optimised planning of the buses at Norrköping bus terminal would probably be achievable and lead to less congestions at the exits. Secondly, the terminal logic is a good method to use when evaluating bus terminals but it is not straight forward to implement.

The dissertation studies traffic dispersion modeling in four parts. In the first part, the dissertation focuses on the Robertson platoon dispersion model which is the most widely used platoon dispersion model. The dissertation demonstrates the importance of the Yu and Van Aerde calibration procedure for the commonly accepted Robertson platoon dispersion model, which is implemented in the TRANSYT software. It demonstrates that the formulation results in an estimated downstream cyclic profile with a margin of error that increases as the size of the time step increases. In an attempt to address this shortcoming, the thesis proposes the use of three enhanced geometric distribution formulations that explicitly account for the time-step size within the modeling process. The proposed models are validated against field and simulated data. The second part focuses on implementation of the Robertson model inside the popular TRANSYT software. The dissertation first shows the importance of calibrating the recurrence platoon dispersion model. It is then demonstrated that the value of the travel time factor &#946; is critical in estimating appropriate signal-timing plans. Alternatively, the dissertation demonstrates that the value of the platoon dispersion factor &#945; does not significantly affect the estimated downstream cyclic flow profile; therefore, a unique value of &#945; provides the necessary precision. Unfortunately, the TRANSYT software only allows the user to calibrate the platoon dispersion factor but does not allow the user to calibrate the travel time factor. In an attempt to address this shortcoming, the document proposes a formulation using the basic properties of the recurrence relationship to enable the user to control the travel time factor indirectly by altering the link average travel time. In the third part of the dissertation, a more general study of platoon dispersion models is presented. The main objective of this part is to evaluate the effect of the underlying travel time distribution on the accuracy and efficiency of platoon dispersion models, through qualitative and quantitative analyses. Since the data used in this study are generated by the INTEGRATION microsimulator, the document first describes the ability of INTEGRATION in generating realistic traffic dispersion effects. The dissertation then uses the microsimulator generated data to evaluate the prediction precision and performance of seven different platoon dispersion models, as well as the effect of different traffic control characteristics on the important efficiency measures used in traffic engineering. The results demonstrate that in terms of prediction accuracy the resulting flow profiles from all the models are very close, and only the geometric distribution of travel times gives higher fit error than others. It also indicates that for all the models the prediction accuracy declines as the travel distance increases, with the flow profiles approaching normality. In terms of efficiency, the travel time distribution has minimum effect on the offset selection and resulting delay. The study also demonstrates that the efficiency is affected more by the distance of travel than the travel time distribution. Finally, in the fourth part of the dissertation, platoon dispersion is studied from a microscopic standpoint. From this perspective traffic dispersion is modeled as differences in desired speed selection, or speed variability. The dissertation first investigates the corresponding steady-state behavior of the car-following models used in popular commercially available traffic microsimulation software and classifies them based on their steady-state characteristics in the uncongested regime. It is illustrated that with one exception, INTEGRATION which uses the Van Aerde car-following model, all the software assume that the desired speed in the uncongested regime is insensitive to traffic conditions. The document then addresses the effect of speed variability on the steady-state characteristics of the car-following models. It is shown that speed variability has significant influence on the speed-at-capacity and alters the behavior of the model in the uncongested regime. A method is proposed to effectively consider the influence of speed variability in the calibration process in order to control the steady-state behavior of the model. Finally, the effectiveness and validity of the proposed method is demonstrated through an example application. / Ph. D.

O rápido crescimento das regiões urbanas tem impacto significativo nos sistemas de tráfego e transportes. Políticas de gerenciamento e estratégias de planejamento alternativas são claramente necessárias para o tratamento da capacidade limitada, e cada vez mais deficitária, das redes viárias. O conceito de Sistemas Inteligentes de Transportes (ITS) surge neste cenário; mais do que procurar aumentar a capacidade por meio de modificações físicas na infraestrutura, sua premissa baseia-se na utilização de tecnologias avançadas de comunicação e computação para melhor gerir os recursos de tráfego e transportes atuais. Influenciar o padrão do comportamento dos usuários é um desafio que tem estimulado muita pesquisa na área de ITS, onde fatores humanos passam a ter grande importância na modelagem, simulação e avaliação dessa abordagem inovadora. Este trabalho tem como foco a utilização de Sistemas Multiagentes (MAS) na representação dos sistemas de tráfego e transporte, com base nas novasmedidas de desempenho impostas pelas tecnologias ITS. As características de agentes têm grande potencial para representar componentes geográfica e funcionalmente distribuídos, como a maioria dos elementos no domínio da aplicação. Uma arquitetura BDI (beliefs, desires, intentions) é apresentada como alternativa a modelos tradicionais, usados para representar o comportamento do motorista em simulação microscópica, considerando-se a representação explícita dos estados mentais dos usuários. Os conceitos básicos de ITS e MAS são apresentados, assim como exemplos de aplicações relacionados com o tema do trabalho. Esta foi a motivação para a extensão de um simulador microscópico existente, no sentido de incorporar as características dos MAS para melhorar a representação dos motoristas. Assim, a demanda é gerada a partir de uma população de agentes, resultando da decisão sobre a rota e o tempo de partida ao longo de vários dias. O modelo estendido, que passa a suportar a interação de motoristas BDI, foi efetivamente implementado e foram executados diferentes experimentos para testar a abordagem em cenários de tráfego urbano. MAS permite uma abordagem direcionada a processos que facilita a construção de representações modulares, robustas, e extensíveis, características pouco presentes em abordagens voltadas ao resultado. Suas premissas de abstração permitem uma associação direta entre modelo e implementação. Incerteza e variabilidade são assim tratadas de maneira mais intuitiva, uma vez que arquiteturas cognitivas permitem uma fácil representação do comportamento humano na estrutura do motorista. Desta forma, MAS estende a simulação microscópica de tráfego no sentido de melhor representar a complexidade inerente às tecnologias ITS. / The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.