The state-of-the-art traffic simulation packages model traffic on a microscopic level. This includes the use of several sets of models that dictate how traffic moves within a transportation network. These models include car-following, gap acceptance, lane-changing and route choice models. The aim of this thesis is to improve the treatment of vehicle dynamics in traffic simulation and, as a result, special attention was paid to car-following models. These models were highlighted because they are largely responsible for capturing a vehicle’s motion and its relevant dynamics in traffic simulation. In order to improve the treatment of vehicle dynamics in traffic simulation, a Dynamic-Interactive-Vehicle (DIV) model was developed. This vehicle model is calibrated with the use of essential vehicle performance specifications that are responsible for the movement of a vehicle in a transportation network. After the calibration process the model is able to accept three inputs from a driver – gas pedal, brake pedal and steering wheel positions. The model then outputs the corresponding longitudinal and latitudinal values which represent the movement of a vehicle along a roadway. The vehicle model will also account for most of the dominant external forces that affect an automobile’s performance along a roadway. This thesis will validate the proposed model by comparing its output from a few performance tests with the performance test results of three passenger cars. The DIV model was validated by comparing the acceleration, braking and steering performance test results of three passenger cars with the output from the DIV model upon performing similar tests. It was found that the DIV model was successful at replicating the two-dimensional vehicle motion.

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

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>

Microscopic simulation software use car-following models to capture the interaction of a vehicle and the preceding vehicle traveling in the same lane. In the literature, much research has been carried out in the field of car-following and traffic stream modeling. Microscopic car-following models have been characterized by using the relationship between a vehicle's desired speed and the distance headway (h) between the lead and follower vehicles. On the other hand, macroscopic traffic stream models describe the motion of a traffic stream by approximating for the flow of a continuous compressible fluid. This research work develops and compares three different formulations of car-following models — speed formulation, molecular acceleration, and fluid acceleration formulation. First, four state-of-the-art car-following models namely, Van Aerde, Greenshields, Greenberg and Pipes models, are selected for developing the three aforementioned formulations. Then a comprehensive car-following behavior encompassing steady-state conditions and two constraints — acceleration and collision avoidance — is presented. Specifically, the variable power vehicle dynamics model proposed by Rakha and Lucic (2002) is utilized for the acceleration constraint. Subsequently, the thesis describes the issues associated with car-following formulations. Recognizing that many different traffic flow conditions exist, three distinct scenarios are selected for comparison purposes. The results demonstrate that the speed formulation ensures that vehicles typically revert to steady-state conditions when vehicles experience a perturbation from steady-state conditions. On the other hand, both acceleration formulations are unable to converge to steady-state conditions when the system experiences a perturbation from a steady-state. The thesis also attempts to address the question of capacity drop associated with vehicles accelerating from congested conditions. Specifically, the capacity drop proposition is analyzed for the case of a backward recovery (typical of a signalized intersection) and stationary shockwave (typical of a capacity drop on a freeway). In the case of the backward recovery shockwave, the acceleration constraint results in a temporally and spatially confined capacity drop as vehicles accelerate to their desired steady-state speed. This temporally and spatially confined capacity drop results in what is typically termed the start loss of a signalized phase. Subsequently, vehicles attain steady-state conditions, in the case of the speed and molecular acceleration formulations, at the traffic signal stop bar after the initial five vehicle departures. The analysis also demonstrates that after attaining steady-state conditions the capacity may drop for the initial vehicle departures as a result of traffic stream dispersion. This traffic dispersion capacity drop increases as vehicles travel further downstream. Alternatively, in the case of a stationary bottleneck the aggressiveness of vehicle accelerations plays a major role in defining the capacity drop downstream of a bottleneck. The study demonstrates that any temporal headways that may be lost while vehicles accelerate to steady-state conditions may not be recuperated and thus result in capacity drops downstream of a bottleneck. A typical example of this scenario is the traffic stream flow rate downstream of a stop sign, which is significantly less than the roadway capacity. The reduction in capacity is caused by losses in temporal headways between successive vehicles which are not recuperated. The study also demonstrates that the ability to model such a capacity drop does not require the use of a dual-regime traffic stream model as is proposed in the Highway Capacity Manual (HCM). Instead, the use of a single-regime model captures the observed capacity with the introduction of an acceleration constraint to the car-following system of equations. / Master of Science

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Le monde industriel, et en particulier l’industrie automobile, cherche à représenter au mieux le réel pour concevoir des outils et produits les plus adaptés aux enjeux et marchés actuels. Dans cette optique, le groupe Volvo a développé de puissants outils pour la simulation de la dynamique des véhicules industriels. Ces outils permettent notamment l’optimisation de composants véhicules ou de stratégies de contrôle. De nombreuses activités de recherche portent sur des technologies innovantes permettant de réduire la consommation des véhicules industriels et d’accroitre la sécurité de leurs usages dans différents environnements. En particulier, le développement des systèmes d’aide à la conduite automobile ITS et ADAS. Afin de pouvoir développer ces systèmes, un environnement de simulation permettant de prendre en compte les différents facteurs pouvant influencer la conduite d’un véhicule doit être mis en place. L’étude se concentre sur la simulation de l’environnement du véhicule et des interactions entre le véhicule et son environnement direct, i.e. le véhicule qui le précède. Les interactions entre le véhicule étudié et le véhicule qui le précède sont modélisées à l’aide de modèles mathématiques, nommés lois de poursuites. De nombreux modèles existent dans la littérature mais peu concernent le comportement des véhicules industriels. Une étude détaillée de ces modèles et des méthodes de calage est réalisée. L’environnement du véhicule peut être représenté par deux catégories de paramètres : statiques (intersections, nombre de voies…) et dynamiques (état du réseau). A partir d’une base de données de trajets usuels, ces paramètres sont calculés, puis utilisés pour générer de manière automatisée des scénarios de simulation réalistes. / The industrial world, and in particular the automotive industry, is seeking to best represent the real world in order to design tools and products that are best adapted to current challenges and markets, by reducing development times and prototyping costs. With this in mind, the Volvo Group has developed powerful tools to simulate the dynamics of industrial vehicles. These tools allow the optimization of vehicle components or control strategies. Many research activities focus on innovative technologies to reduce the consumption of industrial vehicles and increase the safety of their use in different environments. Particularly, the development of ITS and ADAS is booming. In order to be able to develop these systems, a simulation environment must be set up to take into account the various factors that can influence the driving of a vehicle. The work focuses on simulating the vehicle environment and the interactions between the vehicle and its direct environment, i.e. the vehicle in front of it. The interactions between the vehicle under study and the vehicle in front of it are modelled using mathematical models, called car-following models. Many models exist in the literature, but few of them deals specifically with heavy duty vehicles. A specific focus on these models and their calibration is realized. The vehicle environment can be represented by two categories of parameters: static (intersections, number of lanes) and dynamic parameters (state of the network). From a database of usuals roads, these parameters are computed, then, they are used to automatically generate realist traffic simulation scenarios.