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Subramanian, Hariharan. "Estimation of car-following models." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10660.
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Wagner, Peter. "Analyzing fluctuations in car-following." Elsevier, 2012. https://publish.fid-move.qucosa.de/id/qucosa%3A33931.
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Many car-following models predict a stable car-following behavior with a very small fluctuation around an equilibrium value g* of the net headway g with zero speed-difference Δv between the following and the lead vehicle. However, it is well-known and additionally demonstrated by data in this paper, that the fluctuations are much larger than these models predict. Typically, the fluctuation in speed difference is around ±2m/s, while the fluctuation in the net time headway T=g/v can be as big as one or even two seconds, which is as large as the mean time headway itself. By analyzing data from loop detectors as well as data from vehicle trajectories, evidence is provided that this randomness is not due to driver heterogeneity, but can be attributed to an internal stochasticity of the driver itself. A final model-based analysis supports the hypothesis, that the preferred headway of the driver is the parameter that is not kept constant but fluctuates strongly, thus causing the even macroscopically observable randomness in traffic flow.
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Nerem, Sebastian. "Vehicle Weight in Gipps' Car-Following Model." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for bygg, anlegg og transport, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-21880.
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Car-following models are mathematical models, which describe the situation where vehicles drive behind each other on a single lane road section with no overtaking possibilities. The purpose of the models is to estimate how a vehicle reacts to the behavior of the vehicle ahead. A weakness in these models is that they do not take the weight of each vehicle into account. It can however be shown that a vehicle?s weight affects its driving behavior.The purpose of this master?s thesis is to investigate the ability of Gipps? car-following model to reproduce differences in driving behavior caused by differences in vehicles? weight. A modified version of the model where weight is included as a parameter is to be estimated and compared to the original model and field data.The method used was to make a Matlab script which simulates vehicle driving on a road section with Gipps? model. The model results were compared to data collected from a point detector equipped with Weigh-in-motion technology. Time-gap distributions were chosen as the measure against which the models were compared.In the estimated modified model the vehicles are assigned a gross weight, which is picked from an inputted weight distribution. The deceleration parameters of the vehicles are then varied according to vehicle weight.The conclusion of the study is that the original Gipps' model can be calibrated to produce accurate results in uncongested flow. However several vehicle types would need to be defined in order for this to hold for all vehicles. A modified version of the model where vehicle weight is included as a parameter rather than separating vehicles according to type, produced accurate time-gap distributions for all vehicles, with a lower number of input parameters than the original model.However there are aspects of the driving behavior and other traffic situation for which the modified model is not compared. The areas of application of this modified model are also limited today because it requires detailed data on the vehicle weight distribution, which is not widely available and of poor quality.
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Jamison, Sharon Linda. "Chaotic behaviour in looped car following models." Thesis, University of Ulster, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442372.
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Holland, Edward N. "Continuum and car-following models of road traffic." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266303.
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Appiah, Joseph. "Modelling and simulation of car following driving behaviour." Thesis, Edinburgh Napier University, 2018. http://researchrepository.napier.ac.uk/Output/1253614.
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Driver behaviour has become an important aspect of transport research and over the years a considerable number of car following models have been developed. However, many of these models do not accurately simulate actual driving behaviour due to a lack of suitable qualitative and quantitative data. Moreover, the inclusion of socioeconomic variables in the existing models to ascertain the effect on car following behaviour is lacking. This research underlines the need to further investigate driving behaviour and car following models and to develop techniques to provide a better understanding of driver-vehicle interactions during car following. It investigates data collection techniques and develop better techniques to enhance and improve the collection of microscopic driver behaviour and traffic flow data. This study developed a novel data collection technique which involved instrumenting a private vehicle with front and rear advanced radar sensors, both forward and rear facing video-audio recorders connected to GPS based time series speed and distance measurement devices, an in-vehicle computer logging vehicle speed and a CAN monitoring interface user program to provide real time monitoring and display of data. This system has been utilised to collect a more enhanced and reliable microscopic driver behaviour data in three consecutive vehicles movements which represents an improvement from previously used systems. Three different versions of the GHR car following model were produced for: car following car, truck following car and car following truck. Further analysis of the GHR model showed that in the case of car following car, car drivers responses to the lead car are more obviously stronger than in the case of truck following a car. A distance-based car following model and distance-based two-leader car following model that predict the safe following distance of following vehicles were developed to provide a better understanding of driver behaviour. An extension of these models to include gender, corridor (road) type and vehicle occupancy showed evidence of statistical significance of these variables on driver behaviour. A bus following model that predicts the “following distance” also has been calibrated to describe the interactions between a bus and a car within urban-rural driving conditions. In addition, data analysis showed that drivers were inconsistent with their driving behaviour and that there was variability in driving behaviour across the drivers observed in keeping a safe or desired following distance. This study provides a platform for a number of future research agendas including data collection techniques for collection of driver behaviour data; evaluation of different ITS technologies; impact assessment of ACC on driver safety and improvement of traffic microscopic simulation tools in order to strengthen their ability to simulate realistic transport problems for efficient and effective transportation systems.
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Mellodge, Patricia. "Feedback Control for a Path Following Robotic Car." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/32100.
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This thesis describes the current state of development of the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at the Virginia Tech Transportation Institute (VTTI). The FLASH lab and the scale model cars contained therein provide a testbed for the small scale development stage of intelligent transportation systems (ITS). In addition, the FLASH lab serves as a home to the prototype display being developed for an educational museum exhibit.
This thesis also gives details of the path following lateral controller implemented on the FLASH car. The controller was developed using the kinematic model for a wheeled robot. The global model is converted into the path coordinate model so that only local variables are needed. then the path coordinate model is converted into chained form and a controller is given to perform path following.
The path coordinate model introduces a new parameter to the system: the curvature of the path. Thus, it is necessary to provide the path's curvature value to the controller. Because of the environment in which the car is operating, the curvature values are known a priori. Several online methods for determining the curvature are developed.
A MATLAB simulation environment was created with which to test the above algorithms. The simulation uses the kinematic model to show the car's behavior and implements the sensors and controller as closely as possible to the actual system.
The implementation of the lateral controller in hardware is discussed. The vehicle platform is described and the harware and software architecture detailed. The car described is capable of operating manually and autonomously. In autonomous mode, several sensors are utilized including: infrared, magnetic, ultrasound, and image based technology. The operation of each sensor type is described and the information received by the processor from each is discussed.Master of Science
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Pasumarthy, Venkata Siva Praveen. "Formulations, Issues and Comparison of Car-Following Models." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/41129.
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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 vehicleâ 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 â 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 â acceleration and collision avoidance â 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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Sangster, John David. "Naturalistic Driving Data for the Analysis of Car-Following Models." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/76925.
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The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified.
This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation.Master of Science
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Flores, Carlos. "Architecture de contrôle pour le car-following adaptatif et coopératif." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEM048/document.
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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 réellesRecent 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
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Saifuzzaman, Mohammad. "Incorporating risk taking and driver errors in car-following models." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/100018/1/Mohammad_Saifuzzaman_Thesis.pdf.
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This thesis investigated the effect of human factors on car-following behavior and developed a novel methodology to incorporate those in car-following models. Application of the new method enables the car-following models to realistically reproduce the human factor induced behavior which can help researchers to better understand complex traffic problems caused by human errors, for example, road crashes and traffic jams. The method contains an innovative task difficulty formula, which captures the motivation behind driving decisions. The task difficulty offers a better explanation of human behavior in complex traffic conditions than the conventional measures, such as speed and headway.
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Bethonico, Felipe Costa. "Calibração de simuladores microscópicos de tráfego através de medidas macroscópicas." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18144/tde-18072016-135825/.
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Os simuladores de tráfego são programas computacionais que, através de diversos modelos, tentam simular o tráfego, o comportamento dos motoristas, o desempenho dos veículos, entre outros aspectos que envolvem uma rede viária. Estes modelos precisam ser calibrados para representar as condições de um determinado local. O objetivo da pesquisa foi propor um método de calibração de um microssimulador de tráfego através de dados coletados por estações de monitoramento. O estudo de caso foi realizado através do simulador VISSIM para um trecho do Rodoanel Mário Covas (SP-021), utilizando um algoritmo genético (AG). A calibração envolveu, além dos parâmetros comportamentais dos sub-modelos de car-following e lane-change, o ajuste das distribuições de velocidade desejada dos veículos e um método para simulação do congestionamento. A função fitness do AG foi baseada em três medidas de desempenho: uma que comparava gráficos de fluxo-velocidade simulados e observados e outras duas que comparavam a distribuição do volume de tráfego e o percentual de veículos comerciais por faixa de tráfego. Os resultados mostraram que a medida mais apropriada para a comparação dos gráficos foi a distância de Hausdorff modificada (MHD). A medida MHD também foi fundamental para garantir a ciência do método de simulação de congestionamento de tráfego proposto. O modelo calibrado foi validado usando dados de tráfego coletados em dias diferentes, pela mesma estação de monitoramento.Traffic simulators are computer programs that, through various models, try to simulate traffic, driver behavior, vehicle performance, and other aspects involved in a road network. These models need calibration to represent local conditions satisfactorily. The objective of the research was to propose a method for the calibration of a traffic microsimulator based on traffic data collected by monitoring stations. To demonstrate the feasibility of the proposed approach, a case study was performed calibrating the simulator VISSIM for a section of Rodoanel Mario Covas (SP-021) using a genetic algorithm (GA). The calibration focused on behavioral parameters for car-following and lane-change submodels, as well as on the desired speed distributions of vehicles and on a method to simulate congestion. The GA fitness function was based on three performance measures: one that compared simulated and observed speed-flow plots, and two that compared the distribution of traffic volume and truck volumes across traffic lanes, respectively. The results showed that the most appropriate measure for comparison of the graphs was the modified Hausdor distance (MHD). MHD was also important to ensure the efficiency of the method used to simulate traffic congestion. The calibrated model was validate using traffic data collected on different days, by the same monitoring station.
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Lin, Wenjie. "Path Following for a Car-like Mobile Robot based onFuzzy-logic." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-25726.
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The aim of this Master’s thesis is to investigate a path following approach for car-like robots using fuzzy logic. The approach takes into account the vehicle non-holonomic constraints. The thesis covers also the generation of a continuous path given a set of waypoints. The continuous path is modeled using uniform cubic splines. The method is evaluated using a simulated robot running in the 2D simulator Stage, part of the Player robot middleware. i
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Ehlert, Anett. "The identification of some requirements for an improved car following model." Thesis, University of Newcastle Upon Tyne, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399291.
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Chen, Rong. "Driver Behavior in Car Following - The Implications for Forward Collision Avoidance." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/71785.
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Forward Collision Avoidance Systems (FCAS) are a type of active safety system which have great potential for rear-end collision avoidance. These systems use either radar, lidar, or cameras to track objects in front of the vehicle. In the event of an imminent collision, the system will warn the driver, and, in some cases, can autonomously brake to avoid a crash. However, driver acceptance of the systems is paramount to the effectiveness of a FCAS system. Ideally, FCAS should only deliver an alert or intervene at the last possible moment to avoid nuisance alarms, and potentially have drivers disable the system. A better understanding of normal driving behavior can help designers predict when drivers would normally take avoidance action in different situations, and customize the timing of FCAS interventions accordingly. The overall research object of this dissertation was to characterize normal driver behavior in car following events based on naturalistic driving data.
The dissertation analyzed normal driver behavior in car-following during both braking and lane change maneuvers. This study was based on the analysis of data collected in the Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study which involved over 100 drivers operating instrumented vehicles in over 43,000 trips and 1.1 million miles of driving. Time to Collision in both braking and lane change were quantified as a function of vehicle speed and driver characteristics. In general, drivers were found to brake and change lanes more cautiously with increasing vehicle speed. Driver age and gender were found to have significant influence on both time to collision and maximum deceleration during braking. Drivers age 31-50 had a mean braking deceleration approximately 0.03 g greater than that of novice drivers (age 18-20), and female drivers had a marginal increase in mean braking deceleration as compared to male drivers. Lane change maneuvers were less frequent than braking maneuvers. Driver-specific models of TTC at braking and lane change were found to be well characterized by the Generalized Extreme Value distribution. Lastly, driver's intent to change lanes can be predicted using a bivariate normal distribution, characterizing the vehicle's distance to lane boundary and the lateral velocity of the vehicle.
This dissertation presents the first large scale study of its kind, based on naturalistic driving data to report driver behavior during various car-following events. The overall goal of this dissertation is to provide a better understanding of driver behavior in normal driving conditions, which can benefit automakers who seek to improve FCAS effectiveness, as well as regulatory agencies seeking to improve FCAS vehicle tests.Ph. D.
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Fadhloun, Karim. "Modeling Human And Machine-In-The-Loop In Car-Following Theory." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95208.
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Most phenomena in engineering fields involve physical variables that can potentially be predicted using simple or complex mathematical models. However, traffic engineers and researchers are faced with a complex challenge since they have to deal with the human element. For instance, it can be stated that the biggest challenge facing researchers in the area of car-following theory relates to accounting for the human-in-the-loop while modeling the longitudinal motion of the vehicles. In fact, a major drawback of existing car-following models is that the human-in-the-loop is not modeled explicitly. This is specifically important since the output from car-following models directly impacts several other factors and measures of effectiveness, such as vehicle emissions and fuel consumption levels.
The main contribution of this research relates to modeling and incorporating, in an explicit and independent manner, the human-in-the-loop component in car-following theory in such a way that it can be either activated or deactivated depending on if a human driver is in control of the vehicle. That would ensure that a car-following model is able to reflect the different control and autonomy levels that a vehicle could be operated under. Besides that, this thesis offers a better understanding of how humans behave and differ from each other. In fact, through the implementation of explicit parameters representing the human-in-the-loop element, the heterogeneity of human behavior, in terms of driving patterns and styles, is captured.
To achieve its contributions, the study starts by modifying the maximum acceleration vehicle-dynamics model by explicitly incorporating parameters that aim to model driver behavior in its expression making it suitable for the representation of typical acceleration behavior. The modified variant of the model is demonstrated to have a flexible shape that allows it to model different types of variations that drivers can generate, and to be superior to other similar models in that it predicts more accurate acceleration levels in all domains. The resulting model is then integrated in the Rakha-Pasumarthy-Adjerid car-following model, which uses a steady-state formulation along with acceleration and collision avoidance constraints to model the longitudinal motion of vehicles. The validation of the model using a naturalistic dataset found that the modified formulation successfully integrated the human behavior component in the model and that the new formulation decreases the modeling error.
Thereafter, this dissertation proposes a new car-following model, which we term the Fadhloun-Rakha model. Even though structurally different, the developed model incorporates the key components of the Rakha-Pasumarthy-Adjerid model in that it uses the same steady state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. Besides offering a better fit to empirical data, the Fadhloun-Rakha model is inclusive of the following characteristics: (1) it models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profile; and (5) it explicitly captures driver perception and control inaccuracies and errors. Through a quantitative and qualitative evaluation using naturalistic data, the new model is demonstrated to outperform other state-of-the-practice car-following models. In fact, the model is proved to result in a significant decrease in the modeling error, and to generate trajectories that are highly consistent with the observed car-following behavior.
The final part of this study investigates a case in which the driver is excluded and the vehicles are operating in a connected environment. This section aims to showcase a scenario in which the human-in-the-loop is deactivated through the development of a platooning strategy that governs the motion of connected cooperative multi-vehicle platoons.Doctor of Philosophy
Even though the study of the longitudinal motion of vehicles spanned over several decades leading to the development of more precise and complex car-following models, an important aspect was constantly overlooked in those models. In fact, due to the complexity of modeling the human-in-the-loop, the vehicle and the driver were almost always assumed to represent a single entity. More specifically, ignoring driver behavior and integrating it to the vehicle allowed avoiding to deal with the challenges related to modeling human behavior. The difficulty of mathematically modeling the vehicle and the driver as two independent components rather than one unique system is due to two main reasons. First, there are numerous car models and types that make it difficult to determine the different parameters impacting the performance of the vehicle as they differ from vehicle to vehicle. Second, different driving patterns exist and the fact that they are mostly dependent on human behavior and psychology makes them very difficult to replicate mathematically. The research presented in this thesis provides a comprehensive investigation of the human-in-the-loop component in car-following theory leading to a better understanding of the human-vehicle interaction. This study was initiated due to the noticeable overlooking of driver behavior in the existing literature which, as a result, fails to capture the effect of human control and perception errors.
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Higgs, Bryan James. "Emotional Impacts on Driver Behavior: An Emo-Psychophysical Car-Following Model." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64901.
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This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions.Ph. D.
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Higgs, Bryan James. "Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following Models." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/36089.
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This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Studyâ s (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Studyâ s (NTDS) data.Master of Science
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Patterson, Angela K. "Intelligent Cruise Control System Impact Analysis." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36966.
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Intelligent cruise control (ICC) has the potential to impact both roadway throughput and safety by assisting drivers in maintaining safe headways. This thesis explores this potential through comparisons of ICC to conventional cruise control (CCC) and manual driving. Accordingly, descriptions are given of both CCC and ICC systems. Furthermore, descriptions of ICC evaluation studies and car-following models are presented.
The evaluation of ICC is conducted using data collected as part of the Field Operational Test (FOT) performed in Ann Arbor, Michigan. Two levels of analysis are presented in this thesis. The first level of analysis compares the usage of ICC to CCC from a macro level. This study demonstrated that ICC was used more along similar trips. In addition, it was shown that there was no difference in usage of the ON, SET, CANCEL and RESUME buttons. ICC resulted in a higher usage of the ACCEL button and a lower usage of the COAST button compared to CCC. Furthermore, the number of brake interventions while ICC was engaged was higher than CCC. Lastly, the macro-level analysis indicated that there was no difference in the number of near encounters for ICC and CCC. The second analysis makes comparisons at a micro level. The most probable speed, acceleration and headway for each driving mode as well as the probability of using cruise control (based on speed) were determined. The probability of ICC use exceeded CCC use for every freeway speed bin and all but two high-speed arterial speed bins. Finally, a car-following behavior comparison was performed. Manual driving resulted in larger headway values for speeds less than 80 km/h. The ICC speed-headway curve was similar to the CCC speed-headway curve created from high-speed arterial data. The mean headway-speed charts, however, indicated that ICC was more similar to manual driving. Exploration into the specific differences is needed in order to determine the impact of ICC on system safety.
Master of Science
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Kim, Taehyung. "Analysis of variability in car-following behavior over long-term driving maneuvers." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3048.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.Thesis research directed by: Civil Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Shehab, Mahdi. "The effects of heavy goods vehicles (HGVs) on driver behaviour while car-following." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439347.
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Chen, Shih-Ken. "Estimation of car-following safety : application to the design of intelligent cruise control." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/28159.
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Peursum, Sarah. "A study on the behaviour of microscopic car-following models in urban settings." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/2187.
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This thesis carries out empirical analysis and compares four common continuous microscopic models in typical urban scenarios against each other and real human driver behaviour. The best performing model is found to be unstable travelling at the speed limit in a platoon, thus a variant of this model is proposed and implemented. A heterogeneous network uses this continuous model and a discrete microscopic model to identify a distinct density signature where the models switch over.
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Crowther, Brent C. "A Comparison of CORSIM and INTEGRATION for the Modeling of Stationary Bottlenecks." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/32479.
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Though comparisons of simulation models have been conducted, few investigations have examined in detail the logical differences between models. If the output measures of effectiveness are to be interpreted correctly, it is important that the analyst understand some of the underlying logic and assumptions upon which the results are based. An understanding of model logic and its inherent effect on the results will aid the transportation analyst in the application and calibration of a simulation model. In this thesis, the car-following behavior of the CORSIM and INTEGRATION simulation models are examined in significant detail, and its impact on output results explained. In addition, the thesis presents a calibration procedure for the CORSIM sub-model, FRESIM. Currently, FRESIM is calibrated by ad hoc trial-and-error, or by utilizing empirically developed cross-referencing tables. The literature reveals that the relationship between the microscopic input parameters of the CORSIM model, and the macroscopic parameters of capacity is not understood. The thesis addresses this concern. Finally, the thesis compares the INTEGRATION and CORSIM models in freeway and urban environments. The comparison is unique in that the simulated networks were configured such that differences in results could be identified, isolated, and explained. Additionally, the simplified nature of the test networks allowed for the formulation of analytical solutions. The thesis begins by relating steady-state car-following behavior to macroscopic traffic stream models. This is done so that a calibration procedure for the FRESIM (Pipes) car-following model could be developed. The proposed calibration procedure offers an avenue to calibrate microscopic car-following behavior using macroscopic field measurements that can be easily obtained from loop detectors. The calibration procedure, while it does not overcome the inherent shortcomings of the Pipes model, does provide an opportunity to better calibrate the network FRESIM car-following sensitivity factor to existing roadway conditions. The thesis then reports an observed inconsistency in the link-specific car-following sensitivity factor of the FRESIM model. Because calibration of a network on a link-specific basis is key to an accurate network representation, a correction factor was developed that should be applied to the analytically calculated link-specific car-following sensitivity factor. The application of the correction factor resulted in observed saturation flow rates that were within 5% of the desired saturation flow rates. The thesis concludes with a comparison of the CORSIM and INTEGRATION models for transient conditions. As a result of the various intricacies and subtleties that are involved in transient behavior, the comparisons were conducted by running the models on simple networks where analytical solutions to the problem could be formulated. In urban environments, it was observed that the models are consistent in estimates of delay and travel time, and inconsistent in estimates of vehicle stops, stopped delay, fuel consumption, and emissions. Specifically, it was observed that the NETSIM model underestimates the number of vehicle stops in comparison with INTEGRATION and the analytical formulation. It was also observed that the NETSIM vehicles speed and acceleration profiles are characterized by abrupt accelerations and decelerations. These abrupt movements significantly impact stopped time delay and vehicle emissions estimates. Inconsistencies in emissions estimates can also be attributed to differences in the embedded rate tables of each model. In freeway environments for under-saturated conditions, INTEGRATION returned higher values of travel time and delay, and lower values of average speed than the FRESIM model. These results are consistent with the analytical solution, and can be attributed to the speed-flow relationship of each model. In saturated conditions, when the capacity of the bottleneck is equal to the demand volume, the emergent vehicle behavior of the FRESIM model was observed to be inconsistent with the analytical solution. The FRESIM vehicles were observed to dramatically decelerate upon entering a lower-capacity link. This deceleration behavior led to higher travel time and delay time estimates in FRESIM than in INTEGRATION. In over-saturated conditions, longer queue lengths were observed in FRESIM than in INTEGRATION, resulting in slightly higher travel and delay estimates in the FRESIM model. The reason for the discrepancy in queue lengths is unclear, as the network jam density in each model was equivalent.Master of Science
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Chong, Linsen. "Modeling Naturalistic Driver Behavior in Traffic Using Machine Learning." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/76834.
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This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied to simulate driver naturalistic driving behavior including risk-taking behavior during an incident and lateral evasive behavior which have not yet been captured in existing literature. Two special machine learning approaches Backpropagation (BP) neural network and Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) are proposed to model driver behavior during car-following situation and safety critical events separately. In addition to that, as part of the research, state-of-the-art car-following models are also analyzed and compared to BP neural network approach. Also, driver heterogeneity analyzed by NFACRL method is discussed. Finally, it presents the findings and limitations drawn from each of the specific issues, along with recommendations for further research.Master of Science
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Nishiwaki, Yoshihiro, Chiyomi Miyajima, Norihide Kitaoka, Katsunobu Itou, and Kazuya Takeda. "Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control." IEEE, 2007. http://hdl.handle.net/2237/9597.
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Siebert, Felix Wilhelm [Verfasser], and Hans-Rüdiger [Akademischer Betreuer] Pfister. "Car-following in self-, assisted-, and autonomous driving / Felix Wilhelm Siebert ; Betreuer: Hans-Rüdiger Pfister." Lüneburg : Universitätsbibliothek der Leuphana Universität Lüneburg, 2017. http://d-nb.info/1138835137/34.
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Bevrani, Kaveh. "The development of a naturalistic car following model for assessing managed motorway systems' safety effects." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61499/6/Kaveh_Bevrani_Thesis.pdf.
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This thesis highlights the limitations of the existing car following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car following models emulating driver behaviour precise parameters such as headways and Time to Collisions. The comparison evaluates the robustness of each car following model for safety metric reproductions. A new car following model, based on the personal space concept and fish school model is proposed to simulate more precise traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit form VSL systems. This research facilitates assessing Intelligent Transportation Systems on motorways, using microscopic simulation.
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Gao, Yu. "Calibration and Comparison of the VISSIM and INTEGRATION Microscopic Traffic Simulation Models." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/35005.
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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 driverâ 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
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Benekohal, Rahim Farahnak. "Development and validation of a car following model for simulation of traffic flow and traffic wave studies at bottlenecks /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu148726669109498.
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Yang, Shu, and Shu Yang. "Estimating Freeway Travel Time Reliability for Traffic Operations and Planning." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/623003.
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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.
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Dodsworth, Joel Andrew. "The application of vehicle classification, vehicle-to-infrastructure communication and a car-following model to single intersection traffic signal control." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22741/.
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On-line responsive traffic signal optimization strategies most commonly use data received from loop detectors to feed information into an underlying traffic model. The limited data available from conventional detection systems has dictated the way that current 'state-of-the-art' traffic signal control systems have been developed. Such systems tend to consider traffic as having homogenous properties to avoid the requirement for more detailed knowledge of individual vehicle properties. However, a consequence of this simplification is to limit an optimizer in achieving its objectives. The first element of this study investigates whether additional data regarding vehicle type can be reliably extracted from conventional detection to improve optimizer performance using existing infrastructure. A single detector classification algorithm is developed and it is shown that, using a modification of an existing state-of-the-art optimization method, a modest improvement in performance can be achieved. The emergence of connected vehicle technology and, in particular, Vehicle-to-Infrastructure (V2I) communications promises more comprehensive data. V2I-based optimization methods proposed in literature require a minimum penetration rate of V2I equipped vehicles before performance matches existing systems. To address this problem, the second part of the study focuses on the development of a hybrid detection model that is capable of simultaneously using information from conventional and V2I detection. It is demonstrated that the hybrid detection model can begin to realise benefits as soon as V2I data becomes available. V2I-based vehicle classification is then applied to the developed hybrid model and significant benefits are demonstrated for HGVs. The final section of the thesis introduces the use of a more sophisticated internal traffic model and a new optimization method is developed to implement it. The car-following model based optimization method addresses the lack of modelled interaction between vehicles and is shown to be capable of reducing vehicle stops over and above the developed (vertical queue based) hybrid model.
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Taylor, Catherine Elizabeth. "Post-natal care and breastfeeding experiences : a qualitative investigation following a randomised trial of side-car crib use (NECOT Trial)." Thesis, Durham University, 2014. http://etheses.dur.ac.uk/10530/.
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The World Health Organization recommends that mothers should breastfeed their infants exclusively for six months and continue to breastfeed alongside complementary foods for two years or more (WHO 2003). In the UK breastfeeding initiation is high, however, duration falls significantly short of the WHO recommendations (McAndrew et al 2012). Preserving mother-baby contact throughout the post-natal stay is recommended to safeguard and support favourable breastfeeding outcomes (WHO 1989). The NECOT Trial (a randomised controlled trial involving 1204 mother-newborn dyads) examined whether the provision of side-car cribs during the post-natal stay (facilitating unrestricted contact) resulted in a longer duration of breastfeeding than rooming-in using stand-alone cots. The trial obtained weekly data on infant feeding and sleeping arrangements for 26 weeks post-partum. The use of side-car cribs on the post-natal ward did not improve the duration of any or exclusive breastfeeding in the sample overall (Ball et al 2011). This follow-up research aimed to contextualise the NECOT Trial results by adding a qualitative component to the existing quantitative protocol. Methods Interviews were conducted at approximately six months post-partum with a sub-sample of NECOT Trial participants (64) and a number of post-natal ward staff (19) involved in their care. Aims of the maternal interviews were to investigate mothers’ hospital and at-home experiences of infant feeding and sleeping behaviour and to explore their experiences of participating in the trial. Staff interviews were aimed at investigating perceptions of side-car crib usage and to examine attitudes towards post-natal care and breastfeeding support. Audio recordings of interviews were analysed using NVivo software. Findings were discussed within an authoritative knowledge (AK) theoretical framework. Results The interviews revealed that women randomised to receive the side-car cribs felt that they had made a positive difference to their experiences on the post-natal ward; women randomised to the control group felt a side-car crib would have been beneficial. Participants from both the NECOT intervention and control groups recommended continued use of the side-car cribs on post-natal wards. In particular, the advantage of issuing the side-car crib to women who have mobility issues (delivered via c-section or who had received epidural/spinal analgesics) were highlighted. The benefits of the side-car cribs for breastfeeding were deemed to be outweighed on the post-natal ward by other experiences undermining the establishment of breastfeeding such as the introduction of ‘top-up’ formula feeds, absence of skin-to-skin contact, periods of mother-infant separation, delayed breastfeeding initiation or initial breastfeeding difficulties. Staff identified difficulties working around the side-car cribs and discussed problems relating to their role in providing breastfeeding support on the post-natal ward. There were additional factors within the home environment that had a negative effect on breastfeeding duration beyond the initial post-natal period, such as the impact of caring for other children, returning to work, imposition of a feeding/sleeping routine, beliefs of insufficient milk and feelings that breastfeeding was too demanding/tiring. The results also indicated that the follow-up calls impacted upon mothers’ thoughts and actions regarding infant feeding and sleeping behaviour. An AK framework was shown to be a useful theoretical concept for helping to understand and interpret the research findings. Conclusion The overwhelming positive response to the side-car cribs and the benefits highlighted by NECOT Trial participants suggest that introduction of side-car cribs on post-natal wards will improve patient experience. However, any potential beneficial effects on breastfeeding appear to be easily offset by the various effects of other factors that served to reduce breastfeeding success and duration. This implies that the introduction of side-car cribs may be more effective if introduced in conjunction with other interventions addressing breastfeeding barriers on the post-natal ward and continued support in the community. Moreover, from a broader perspective, the findings of the research challenge the authoritative position of quantitative research and RCTs for informing evidence-based medicine.
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Islam, Md Rauful. "A Study on Use of Wide-Area Persistent Video Data for Modeling Traffic Characteristics." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/99464.
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This study explores the potential of vehicle trajectory data obtained from Wide Area Motion Imagery for modeling and analyzing traffic characteristics. The data in question is collected by PV Labs and also known as persistent wide-area video. This video, in combination with PVLab's integrated Tactical Content Management System's spatiotemporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information. This thesis explores the use of data collected by the PVLab's system for an approximate area of 4 square kilometers area in the CBD area of Hamilton, Canada for use in understanding traffic characteristics. The data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like any other computer vision algorithm, this data suffers from false detection, no detection, and other inaccuracies caused by faulty image registration. Data filtering requirements to remove noisy trajectories and reduce error is developed and presented. A methodology for extracting microscopic traffic data (gap, relative velocity, acceleration, speed) from the vehicle trajectories is presented in details.
This study includes the development of a data model for storing this type of large-scale spatiotemporal data. The proposed data model is a combination of two efficient trajectory data storing techniques, the 3-D schema and the network schema and was developed to store trajectory information along with associated microscopic traffic information. The data model is designed to run fast queries on trajectory information. A 15-minute sample of tracks was validated using manual extraction from imagery frames from the video. Microscopic traffic data is extracted from this trajectory data using customized GIS analysis. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. The final processed dataset includes vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over two continuous three-hour periods.
Two subsets of the data were extracted, cleaned, and processed for use in calibrating car-following sub-models used in microscopic simulations. The car-following model is one of the cornerstones of any simulation based traffic analysis. Calibrating and validating these models is essential for enhancing the ability of the model's capability of representing local traffic. Calibration efforts have previously been limited by the availability and accuracy of microscopic traffic data. Even datasets like the NGSIM data are restricted in either time or space. Trajectory data of all vehicles over a wide area during an extended period of time can provide new insight into microscopic models. Persistent wide-area imagery provides a source for this data. This study explores data smoothing required to handle measurement error and to prepare model input for calibration. Three car-following models : the GHR model, the linear Helly model, and the Intelligent Driver model are calibrated using this new data source. Two approaches were taken for calibrating model parameters. First, a least square method is used to estimate the best fit value for the model parameter that minimizes the global error between the observed and predicted values. The calibration results outline the limitation of both the WAMI data source and the models themselves. Existing model structures impose limitations on the parameter values. Models become unstable beyond these parameter values and these values may not be near global optima. Most of the car-following models were developed based upon some kinematic relation between driver reaction and expected stimuli of that response. For this reason, models in their current form are ill-suited for calibration with noisy microscopic data. On the other hand, the limitation of the WAMI data is the inability of obtaining an estimate of the measurement errors. With unknown measurement errors, any model development or calibration becomes questionable irrespective of the data smoothing or filtering technique undertaken. These findings indicate requirements for development of a new generation of car-following model that can accommodate noisy trajectory data for calibration of its parameters.MS
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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, and Fumitada Itakura. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.
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Santos, Paula Manoela dos. "Método de calibração de um modelo veículo seguidor para BRT e ônibus em corredor segregado." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/75917.
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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.
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Liu, Hao. "Synthesis of Quantified Impact of Connected Vehicles on Traffic Mobility, Safety, and Emission: Methodology and Simulated Effect for Freeway Facilities." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479816059720034.
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Ponnu, Devanarayanan Balaji. "Impact of Speed Differences between Lanes on the Empirical Fundamental Relationship." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534622048454184.
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Olstam, Johan. "Simulation of Surrounding Vehicles in Driving Simulators." Doctoral thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17453.
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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.
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Gugsa, Gebrehiwot Rihanna. "Traffic Simulation of Automated Shuttles in Linköping University Campus." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178105.
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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 shuttles 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.Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet
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Lanka, Venkata Raghava Ravi Teja Lanka. "VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.
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Qian, Gongbin. "Effectiveness of eco-driving during queue discharge at urban signalised intersections." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/65352/1/Gongbin_Qian_Thesis.pdf.
Full textAbstract:
This research investigated the effectiveness of using an eco-driving strategy at urban signalised intersections from both the individual driver and the traffic flow perspective. The project included a field driving experiment and a series of traffic simulation investigations. The study found that the prevailing eco-driving strategy has negative impacts on traffic mobility and environmental performance when the traffic is highly congested. An improved eco-driving strategy has been developed to mitigate these negative impacts.
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Sarkar, Nikhil Chandra. "Microscopic modelling of the area-based traffic flow." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/129869/9/Nikhil%20Chandra%20Sarkar%20Thesis.pdf.
Full textAbstract:
Area-based (i.e., non-lane based) heterogeneous traffic (as in developing countries) differs significantly from lane-based homogeneous traffic (as in developed countries). In area-based traffic, drivers generally ignore the lane markings and perceive the entire road space while progressing longitudinally. Traditional car-following and lane-changing models are not directly applicable to modelling such driving behaviour. This research aimed to microscopically model the dynamic of the subject vehicle in area-based traffic flow. The modelling was conducted in two steps. In Step 1, discrete choice-based modelling was conducted to identify the area-based movement direction of the subject vehicle. In Step 2, a vehicle-following behaviour model was developed to simulate the next position of the subject vehicle (along the direction of a selected alternative, as modelled in Step 1 of this modelling). The macroscopic validation of the model was performed to ensure the robustness of the model.
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Apeltauer, Jiří. "Statistické vlastnosti mikrostruktury dopravního proudu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2018. http://www.nusl.cz/ntk/nusl-390266.
Full textAbstract:
The actual traffic flow theory assumes interactions only between neighbouring vehicles within the traffic. This assumption is reasonable, but it is based on the possibilities of science and technology available decades ago, which are currently overcome. Obviously, in general, there is an interaction between vehicles at greater distances (or between multiple vehicles), but at the time, no procedure has been put forward to quantify the distance of this interaction. This work introdukce a method, which use mathematical statistics and precise measurement of time distances of individual vehicles, which allows to determine these interacting distances (between several vehicles) and its validation for narrow densities of traffic flow. It has been revealed that at high traffic flow densities there is an interaction between at least three consecutive vehicles and four and five vehicles at lower densities. Results could be applied in the development of new traffic flow models and its verification.
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Mohammadian, Saeed. "Freeway traffic flow dynamics and safety: A behavioural continuum framework." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/227209/1/Saeed_Mohammadian_Thesis.pdf.
Full textAbstract:
Congestion and rear-end crashes are two undesirable phenomena of freeway traffic flows, which are interrelated and highly affected by human psychological factors. Since congestion is an everyday problem, and crashes are rare events, congestion management and crash risk prevention strategies are often implemented through separate research directions. However, overwhelming evidence has underscored the inter-relation between rear-end crashes and freeway traffic flow dynamics in recent decades. This dissertation develops novel mathematical models for freeway traffic flow dynamics and safety to integrate them into a unifiable framework. The outcomes of this PhD can enable moving towards faster and safer roads.
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Schultz, Grant George. "Developing a methodology to account for commercial motor vehicles using microscopic traffic simulation models." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/111.
Full textAbstract:
The collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.
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Lennie, Sandra Christine. "Assessing the spatial impacts of multi-combination vehicles on an urban motorway." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16066/1/Sandra_Lennie_Thesis.pdf.
Full textAbstract:
Multi-combination vehicles (MCVs) in urban areas impact on productivity, safety, infrastructure, congestion and the environment. However, psychological effects of MCVs on other drivers may also influence the positioning of vehicles and congestion. A literature review revealed little information on the psychological effects of heavy vehicles on other road users. This research can be used to quantify some psychological impacts of MCVs.
A testing program was undertaken on the Gateway Motorway to observe passenger car behaviour around MCVs in a lateral and longitudinal sense. Video footage was collected on a four lane divided urban motorway section which was level, straight and away from any off/on ramps. It experiences high traffic volumes with a one-way AADT of approximately 33,500. The route is currently designated for B-doubles, which is the most common MCV in urban areas.
In a lateral sense, the research showed that passenger car behaviour changes around heavy vehicles (prime mover semi-trailer combination and B-doubles); however, there is no statistical difference in passenger car behaviour around semi-trailers and B-doubles. Longitudinally it was found that, even though passenger cars shy away from B-doubles more than semi-trailers, B-doubles are still more efficient in a spatial sense since they carry more freight.
The outcomes of this research indicate that there is no further psychological impact on passenger cars, when travelling around B-doubles compared with semi-trailers. Where the results identified longitudinal behaviour changes, it was still concluded that B-doubles were more efficient at transporting freight when the passenger car equivalent (PCE) per tonne of freight was considered.
Tracking ability testing was undertaken in a rural area to determine the lateral spatial requirements of three different MCVs. The rural testing was considered appropriate since parts of the urban network have similar characteristics to rural networks. A model was developed as a part of this project to process the data collected by Haldane (2002), but results could not be relied upon due to poor quality data.
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Lennie, Sandra Christine. "Assessing the spatial impacts of multi-combination vehicles on an urban motorway." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16066/.
Full textAbstract:
Multi-combination vehicles (MCVs) in urban areas impact on productivity, safety, infrastructure, congestion and the environment. However, psychological effects of MCVs on other drivers may also influence the positioning of vehicles and congestion. A literature review revealed little information on the psychological effects of heavy vehicles on other road users. This research can be used to quantify some psychological impacts of MCVs.
A testing program was undertaken on the Gateway Motorway to observe passenger car behaviour around MCVs in a lateral and longitudinal sense. Video footage was collected on a four lane divided urban motorway section which was level, straight and away from any off/on ramps. It experiences high traffic volumes with a one-way AADT of approximately 33,500. The route is currently designated for B-doubles, which is the most common MCV in urban areas.
In a lateral sense, the research showed that passenger car behaviour changes around heavy vehicles (prime mover semi-trailer combination and B-doubles); however, there is no statistical difference in passenger car behaviour around semi-trailers and B-doubles. Longitudinally it was found that, even though passenger cars shy away from B-doubles more than semi-trailers, B-doubles are still more efficient in a spatial sense since they carry more freight.
The outcomes of this research indicate that there is no further psychological impact on passenger cars, when travelling around B-doubles compared with semi-trailers. Where the results identified longitudinal behaviour changes, it was still concluded that B-doubles were more efficient at transporting freight when the passenger car equivalent (PCE) per tonne of freight was considered.
Tracking ability testing was undertaken in a rural area to determine the lateral spatial requirements of three different MCVs. The rural testing was considered appropriate since parts of the urban network have similar characteristics to rural networks. A model was developed as a part of this project to process the data collected by Haldane (2002), but results could not be relied upon due to poor quality data.
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Tordeux, Antoine. "Étude de processus en temps continu modélisant l'écoulement de flux de trafic routier." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00596941.
Full textAbstract:
Ce travail présente des modèles d'écoulement en temps continu de flux de trafic routier. En premier lieu, il s'agit de modèles microscopiques de poursuite. Un modèle par systèmes d'équations différentielles couplées est proposé, basé sur le temps inter-véhiculaire. Ce modèle intègre un temps de réaction et des possibilités d'anticipation pour chaque véhicule. Les paramètres sont estimés par maximum de vraisemblance dans un modèle statistique à deux niveaux. Des simulations permettent de caractériser le comportement d'une file de véhicules. Dans une approche stochastique, un modèle d'évolution de la distance inter-véhiculaire est étudié à l'aide du processus Markovien de saut zero-range. L'introduction d'un temps de réaction tend à produire des ondes cinématiques. D'autre part, un modèle d'écoulement de trafic par le processus Markovien de saut des misanthropes est proposé. Il s'agit d'une modélisation au niveau mésoscopique, adaptée à la simulation de flux de trafic sur un réseau
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Wu, Jia. "Utilisation de la conduite coopérative pour la régulation de trafic dans une intersection." Phd thesis, Université de Technologie de Belfort-Montbeliard, 2011. http://tel.archives-ouvertes.fr/tel-00703165.
Full textAbstract:
L'objectif de ce travail est d'exploiter les potentialités offertes par la conduite coopérative afin de fluidifier le trafic au niveau des intersections isolées. Pour ce faire, nous avons proposé un nouveau système de régulation au sein des intersections en s'inspirant du principe de l'intersection autonome. Nous avons appelé notre système : SVAC (système du véhicule-actionneur coopératif). Il repose sur la possibilité des échanges d'information entre le véhicule et son environnement de conduite.Le SVAC permet une régulation plus précise du trafic puisqu'il se base sur les requêtes de droit de passage envoyées par les véhicules réellement présents dans l'intersection. En outre, grâce à la signalisation à bord, la régulation consiste à définir les séquences de passage des véhicules, ce qui permet de personnaliser la signalisation. Le gain de précision soulève plusieurs obstacles. D'une part, nous nous heurtons systématiquement à l'absence de modèles mathématiques permettant d'aborder le problème. D'autre part, la simple énumération des séquences implique une explosion combinatoire, ce qui ne convient pas à l'application temps-réelle de la régulation des intersections. Pour s'affranchir des deux problématiques nous avons utilisé les réseaux de Petri P-temporisés. Le modèle nous a permis de décrire sous la forme d'équations mathématiques les compteurs des différents évènements observés par les véhicules. Deux objectifs de régulation ont été dégagés après avoir déduit le temps moyen d'attente basé sur la formule de Little. Le premier consiste à vider les intersections au plus tôt. Nous avons proposé un algorithme de programmation dynamique et deux heuristiques. La première heuristique est directement issue de l'analyse des propriétés du problème posé. La deuxième est basée sur l'algorithme de colonies de fourmis. En effet, le problème défini est un cas particulier du problème du voyageur de commerce. Le deuxième objectif de régulation consiste à minimiser instantanément la longueur de la file d'attente. Dans ce cadre, nous avons supposé le fonctionnement à vitesse maximale du réseau de Petri. L'utilisation des contraintes sur les ressources nous a permis de définir des règles simples de régulation en utilisant le mapping.Dans ce mémoire, nous avons utilisé la simulation microscopique basée sur les lois de poursuite pour s'approcher du comportement de conduite. La simulation a servi pour la comparaison des différentes approches proposées dans ce mémoire avec les régulateurs adaptatifs et les intersections autonomes. Dans tous les cas notre approche se distingue par un gain de capacité, ce qui nous a encouragé de reproduire le SVAC à travers un prototype de robots. Cette maquette montre la faisabilité du système au moins pour des applications industrielles.