L'adoption récente et généralisée des systèmes d'automatisation des véhicules, avec l’incorporation de la connectivité entre voitures, a encouragé l’utilisation des techniques comme le Contrôle Croisière Adaptatif Coopératif (CACC) et la conduite en convoi. Ces techniques ont prouvé l’amélioration du flux de trafic et la sécurité de la conduite, tout en réduisant la consommation d’énergie et les émissions CO_2. Néanmoins, la robustesse et la stabilité stricte du convoi, malgré les délais de communication et l’hétérogénéité des convois, restent des sujets de recherche en cours. Cette thèse a pour sujet la conception, l’analyse et validation de systèmes de contrôle pour le car-following automatisé et coopératif, en ciblant l’augmentation de ses avantages et son usage, en se concentrant sur la robustesse et la stabilité du convoi même sur des séries de véhicules hétérogènes avec des retards de communication. Une structure feedforward/feedback est développée, dont sa modularité est fondamentale pour la mise au point des approches avec des objectifs différents mais complémentaires. L’architecture permet non seulement l’adoption d’une stratégie d’espacement pour la range entière de vitesse, mais elle peut aussi être employée dans le cadre d’un CACC basé sur une machine d’état pour la conduite en convoi sur des environnements urbains avec des capacités de freinage d’urgence et de rejoint du convoi. Des différents algorithmes pour la conception de systèmes de contrôle feedback pour la régulation des distances sont présentés, pour quoi le calcul d’ordre fractionnaire démontre fournir des réponses fréquentielles de boucle fermé plus précises et satisfaire des besoins plus exigeantes. La performance est assurée malgré l’hétérogénéité avec la proposition de deux approches feedforward différents. Le premier est basé sur une topologie en considérant que le véhicule précédent dans la boucle, tandis que le deuxième inclut le véhicule leader pour améliorer la performance de suivi. Les algorithmes proposés sont validés avec des études de stabilité dans le domaine du temps et fréquence, ainsi que simulations et expérimentations réelles. / Recent widespread adoption of vehicle automation and introduction of vehicle-to-vehicle connectivity has opened the doors for techniques as Cooperative Adaptive Cruise Control (CACC) and platooning, showing promising results in terms of traffic capacity and safety improvement, while reducing fuel consumption and CO_2 emissions. However, robustness and strict string stability, despite communication delays and string heterogeneity is still an on-going research field. This thesis deals with the design, study and validation of control systems for cooperative automated car-following, with the purpose of extending their benefits and encourage their employment, focusing on robustness and string stability, despite possible V2V communication delays and string heterogeneity. A feedforward/feedback hierarchical control structure is developed, which modularity is fundamental for the proposal of approaches that target different but complementary performance objectives. The architecture not only permits the adoption of a full speed range spacing policy that target multiple criteria, but can also be employed in a state machine-based CACC framework for urban environments with emergency braking and platoon re-joining capabilities in case of pedestrian interaction. Different feedback control design algorithms are presented for the gap-regulation, for which the fractional-order calculus is demonstrated to provide more accurate closed loop frequency responses and satisfy more demanding requirements. Desired performance is ensured in spite of string heterogeneity through the proposal of two feedforward methods : one based on predecessor-only topology, while the second includes the leader vehicle information on feedforward to gain tracking capabilities. Proposed control algorithms are validated through time and frequency-domain stability studies, simulation and real platforms experiments.

Travel time reliability (TTR) has attracted increasing attention in recent years, and is often listed as one of the major roadway performance and service quality measures for both traffic engineers and travelers. Measuring travel time reliability is the first step towards improving travel time reliability, ensuring on-time arrivals, and reducing travel costs. Four components may be primarily considered, including travel time estimation/collection, quantity of travel time selection, probability distribution selection, and TTR measure selection. Travel time is a key transportation performance measure because of its diverse applications and it also serves the foundation of estimating travel time reliability. Various modelling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a micro-simulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macro-scale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in St. Louis, Missouri was used to verify the estimated travel times produced by the proposed General Motors Travel Time Estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times. Most travel time reliability measures are derived directly from continuous probability distributions and applied to the traffic data directly. However, little previous research shows a consensus of probability distribution family selection for travel time reliability. Different probability distribution families could yield different values for the same travel time reliability measure (e.g. standard deviation). It is believe that the specific selection of probability distribution families has few effects on measuring travel time reliability. Therefore, two hypotheses are proposed in hope of accurately measuring travel time reliability. An experiment is designed to prove the two hypotheses. The first hypothesis is proven by conducting the Kolmogorov–Smirnov test and checking log-likelihoods, and Akaike information criterion with a correction for finite sample sizes (AICc) and Bayesian information criterion (BIC) convergences; and the second hypothesis is proven by examining both moment-based and percentile-based travel time reliability measures. The results from the two hypotheses testing suggest that 1) underfitting may cause disagreement in distribution selection, 2) travel time can be precisely fitted using mixture models with higher value of the number of mixture distributions (K), regardless of the distribution family, and 3) the travel time reliability measures are insensitive to the selection of distribution family. Findings of this research allows researchers and practitioners to avoid the work of testing various distributions, and travel time reliability can be more accurately measured using mixture models due to higher value of log-likelihoods. As with travel time collection, the accuracy of the observed travel time and the optimal travel time data quantity should be determined before using the TTR data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the travel time reliability for a specific freeway corridor, time of day (TOD), and day of week (DOW)? A framework for answering this question has not been proposed in the past. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to fully understand the accuracy of TTR measures under different traffic conditions. The results of the accuracy measurements primarily showed that: 1) the proposed framework can facilitate assessment of the accuracy of TTR, and 2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is enough for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions. A considerable number of studies have contributed to measuring travel time reliability. Travel time distribution estimation is considered as an important starting input of measuring travel time reliability. Kernel density estimation (KDE) is used to estimate travel time distribution, instead of parametric probability distributions, e.g. Lognormal distribution, the two state models. The Hasofer Lind - Rackwitz Fiessler (HL-RF) algorithm, widely used in the field of reliability engineering, is applied to this work. It is used to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway is first introduced. Network travel time reliability is developed afterwards. Given probability distributions estimated by the KDE technique, and an anticipated travel time from travelers, the two equations of the corridor and network travel time reliability can be used to address the question, "How reliable is my perceived travel time?" The definition of travel time reliability is in the sense of "on time performance", and it is conducted inherently from the perspective of travelers. Further, the major advantages of the proposed method are: 1) The proposed method demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain; 2) the proposed method shows its flexibility for being applied onto different levels of roadways (e.g. individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can utilize the computed travel time reliability information to plan their trips in advance, in order to better manage trip time, reduce cost, and avoid frustration.

Driving simulators and microscopic traffic simulation are important tools for making evaluations of driving and traffic. A driving simulator is de-signed to imitate real driving and is used to conduct experiments on driver behavior. Traffic simulation is commonly used to evaluate the quality of service of different infrastructure designs. This thesis considers a different application of traffic simulation, namely the simulation of surrounding vehicles in driving simulators. The surrounding traffic is one of several factors that influence a driver's mental load and ability to drive a vehicle. The representation of the surrounding vehicles in a driving simulator plays an important role in the striving to create an illusion of real driving. If the illusion of real driving is not good enough, there is an risk that drivers will behave differently than in real world driving, implying that the results and conclusions reached from simulations may not be transferable to real driving. This thesis has two main objectives. The first objective is to develop a model for generating and simulating autonomous surrounding vehicles in a driving simulator. The approach used by the model developed is to only simulate the closest area of the driving simulator vehicle. This area is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to a microscopic model which includes sub-models for driving behavior, while vehicles in the outer regions are updated according to a less time-consuming mesoscopic model. The second objective is to develop an algorithm for combining autonomous vehicles and controlled events. Driving simulators are often used to study situations that rarely occur in the real traffic system. In order to create the same situations for each subject, the behavior of the surrounding vehicles has traditionally been strictly controlled. This often leads to less realistic surrounding traffic. The algorithm developed makes it possible to use autonomous traffic between the predefined controlled situations, and thereby get both realistic traffc and controlled events. The model and the algorithm developed have been implemented and tested in the VTI driving simulator with promising results.

No description available.

O modelo veículo seguidor – ou car-following – é o coração dos softwares de simulação microscópica de tráfego. Quando bem calibrados, esses softwares são capazes de replicar a realidade em ambiente controlado. Ainda hoje há uma resistência quanto à calibração do modelo veículo seguidor e, mesmo que muitos trabalhos relatem formas de realizá-la, são escassas as referências na literatura sobre calibração utilizando dados de sistemas ônibus. Este trabalho consiste na elaboração de um método de calibração do modelo veículo seguidor de Gipps, combinado ao modelo de aceleração linear, para a replicação da operação de ônibus em corredores exclusivos. A elaboração do método iniciou com uma revisão dos principais modelos veículo seguidor e uma posterior avaliação dos modelos GHR e de Gipps para manobras típicas de sistemas ônibus. A seguir elaborou-se o procedimento de calibração utilizando coleta de dados por meio de filmagens da operação dos ônibus em corredores e da extração dos dados utilizando uma ferramenta de reconhecimento de imagem. O método das coordenadas retangulares foi utilizado para corrigir a paralaxe. Concomitante às filmagens analisou-se visualmente a ocupação dos ônibus para que as taxas de aceleração e desaceleração dos ônibus pudessem ser diferenciadas conforme o nível de ocupação. A calibração foi realizada através da comparação da distância percorrida pelos veículos ao longo do tempo e as correspondentes modeladas. Os resultados para taxas de aceleração e desaceleração obtidas a partir de dados coletados em Curitiba evidenciam a validade do procedimento. A simplicidade do método desenvolvido é uma característica importante, pois permite a replicação em outros ambientes sem a necessidade de equipamentos sofisticados. / The car-following model is the heart of the traffic simulation software and it is able to replicate real traffic conditions in a controlled environment when properly calibrated. Still today there is resistance on the car-following model calibration and, even though many papers report calibration forms of this model, there are scarce references in the literature about calibration using bus systems data. This work is the development of a method for calibrating the Gipps car-following model, combined with the free linear acceleration model, for replication of buses operation in exclusive lanes. We initiated the method planning with a review of the main car-following model and evaluation of GHR and Gipps for typical bus systems maneuvers. In the next step we developed the calibration procedure using data collection through filming bus operation and drawing out data using a tool for image recognition. We used the rectangular coordinates method to parallax correction. We also visually analyzed the buses occupation simultaneously to filming, so bus acceleration and deceleration rates could be differentiated according to the occupancy level. Calibration was achieved by comparing the vehicle distance traveled over time and corresponding modeled. The results for acceleration and deceleration rates and speed desired values obtained from data collected in Curitiba demonstrate the validity of the procedure. An important feature of this method is the plainness, as it enables replication in other environments without the need for sophisticated equipment.

O modelo veículo seguidor – ou car-following – é o coração dos softwares de simulação microscópica de tráfego. Quando bem calibrados, esses softwares são capazes de replicar a realidade em ambiente controlado. Ainda hoje há uma resistência quanto à calibração do modelo veículo seguidor e, mesmo que muitos trabalhos relatem formas de realizá-la, são escassas as referências na literatura sobre calibração utilizando dados de sistemas ônibus. Este trabalho consiste na elaboração de um método de calibração do modelo veículo seguidor de Gipps, combinado ao modelo de aceleração linear, para a replicação da operação de ônibus em corredores exclusivos. A elaboração do método iniciou com uma revisão dos principais modelos veículo seguidor e uma posterior avaliação dos modelos GHR e de Gipps para manobras típicas de sistemas ônibus. A seguir elaborou-se o procedimento de calibração utilizando coleta de dados por meio de filmagens da operação dos ônibus em corredores e da extração dos dados utilizando uma ferramenta de reconhecimento de imagem. O método das coordenadas retangulares foi utilizado para corrigir a paralaxe. Concomitante às filmagens analisou-se visualmente a ocupação dos ônibus para que as taxas de aceleração e desaceleração dos ônibus pudessem ser diferenciadas conforme o nível de ocupação. A calibração foi realizada através da comparação da distância percorrida pelos veículos ao longo do tempo e as correspondentes modeladas. Os resultados para taxas de aceleração e desaceleração obtidas a partir de dados coletados em Curitiba evidenciam a validade do procedimento. A simplicidade do método desenvolvido é uma característica importante, pois permite a replicação em outros ambientes sem a necessidade de equipamentos sofisticados. / The car-following model is the heart of the traffic simulation software and it is able to replicate real traffic conditions in a controlled environment when properly calibrated. Still today there is resistance on the car-following model calibration and, even though many papers report calibration forms of this model, there are scarce references in the literature about calibration using bus systems data. This work is the development of a method for calibrating the Gipps car-following model, combined with the free linear acceleration model, for replication of buses operation in exclusive lanes. We initiated the method planning with a review of the main car-following model and evaluation of GHR and Gipps for typical bus systems maneuvers. In the next step we developed the calibration procedure using data collection through filming bus operation and drawing out data using a tool for image recognition. We used the rectangular coordinates method to parallax correction. We also visually analyzed the buses occupation simultaneously to filming, so bus acceleration and deceleration rates could be differentiated according to the occupancy level. Calibration was achieved by comparing the vehicle distance traveled over time and corresponding modeled. The results for acceleration and deceleration rates and speed desired values obtained from data collected in Curitiba demonstrate the validity of the procedure. An important feature of this method is the plainness, as it enables replication in other environments without the need for sophisticated equipment.

O modelo veículo seguidor – ou car-following – é o coração dos softwares de simulação microscópica de tráfego. Quando bem calibrados, esses softwares são capazes de replicar a realidade em ambiente controlado. Ainda hoje há uma resistência quanto à calibração do modelo veículo seguidor e, mesmo que muitos trabalhos relatem formas de realizá-la, são escassas as referências na literatura sobre calibração utilizando dados de sistemas ônibus. Este trabalho consiste na elaboração de um método de calibração do modelo veículo seguidor de Gipps, combinado ao modelo de aceleração linear, para a replicação da operação de ônibus em corredores exclusivos. A elaboração do método iniciou com uma revisão dos principais modelos veículo seguidor e uma posterior avaliação dos modelos GHR e de Gipps para manobras típicas de sistemas ônibus. A seguir elaborou-se o procedimento de calibração utilizando coleta de dados por meio de filmagens da operação dos ônibus em corredores e da extração dos dados utilizando uma ferramenta de reconhecimento de imagem. O método das coordenadas retangulares foi utilizado para corrigir a paralaxe. Concomitante às filmagens analisou-se visualmente a ocupação dos ônibus para que as taxas de aceleração e desaceleração dos ônibus pudessem ser diferenciadas conforme o nível de ocupação. A calibração foi realizada através da comparação da distância percorrida pelos veículos ao longo do tempo e as correspondentes modeladas. Os resultados para taxas de aceleração e desaceleração obtidas a partir de dados coletados em Curitiba evidenciam a validade do procedimento. A simplicidade do método desenvolvido é uma característica importante, pois permite a replicação em outros ambientes sem a necessidade de equipamentos sofisticados. / The car-following model is the heart of the traffic simulation software and it is able to replicate real traffic conditions in a controlled environment when properly calibrated. Still today there is resistance on the car-following model calibration and, even though many papers report calibration forms of this model, there are scarce references in the literature about calibration using bus systems data. This work is the development of a method for calibrating the Gipps car-following model, combined with the free linear acceleration model, for replication of buses operation in exclusive lanes. We initiated the method planning with a review of the main car-following model and evaluation of GHR and Gipps for typical bus systems maneuvers. In the next step we developed the calibration procedure using data collection through filming bus operation and drawing out data using a tool for image recognition. We used the rectangular coordinates method to parallax correction. We also visually analyzed the buses occupation simultaneously to filming, so bus acceleration and deceleration rates could be differentiated according to the occupancy level. Calibration was achieved by comparing the vehicle distance traveled over time and corresponding modeled. The results for acceleration and deceleration rates and speed desired values obtained from data collected in Curitiba demonstrate the validity of the procedure. An important feature of this method is the plainness, as it enables replication in other environments without the need for sophisticated equipment.

No description available.

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>

No description available.