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.

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

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

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

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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 / The decision making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior in both aggregated and disaggregated manner. Different computational tools relying on representative models of aggregate traffic flow measures and/or driver behavior are used in the decision support system tools. Field data is used not only as an input for the computational tools but also to develop, calibrate, and validate the models representing a particular aspect of traffic and driver behavior. Different approaches have been undertaken to collect the data required for analyzing traffic and driver behavior. One of the applied approach is to collect trajectory (i.e. position, speed, acceleration) information of vehicles in the analysis zone. However, this data collection approach is often limited to relatively small stretch of a roadway for short duration due to high cost of collection and limitation of technology. As a result, the models developed and calibrated using these data often lack generalization power for different situation. This study explores the potential of a new data source to address the aforementioned limitations. The data used in this study collects the trajectory information for the whole population of vehicles in the study area by collecting wide-area (WAMI) video data. The data is collected by Canada based imaging solution company PV Labs. The collection area is relatively large to cover wide range of roadway types and traffic operation system. A framework has been developed to extract traffic flow measures from the trajectory data. The extracted traffic flow measures are then applied to calibrate the car-following model. The car-following model attempts to mimic the longitudinal movement of real-world drivers following another vehicle in front of them. The calibration results outline the limitations of the WAMI data. Although, this dataset is capable of capturing traffic measures for different driving condition, the lack of information about measurement error imposes limits on the direct application of the data for model calibration. Findings of this study can be applied for refinement of the video data capture technology and subsequent application in modelling traffic characteristics as well as development of new and calibration of existing driver behavior model.

Driver psychology is one of the most difficult phenomena to model in the realm of traffic flow theory because mathematics often cannot capture the human factors involved with driving a car. Over the past several decades, many models have attempted to model driver aggressiveness with varied results. The recently proposed Longitudinal Control Model (LCM) makes such an attempt, and this paper offers evidence of the LCM's usefulness in modeling road dynamics by analyzing deceleration rates that are commonly associated with various levels of aggression displayed by drivers. The paper is roughly divided into three sections, one outlining the LCM's ability to quantify forces between passive and aggressive drivers on a microscopic level, one describing the LCM's ability to measure aggressiveness of platoons of drivers, and the last explaining the meaning of the model’s derivative. The paper references some attempts to capture driver aggressiveness made by classic car-following models, and endeavors to offer some new ideas in study of driver characteristics and traffic flow theory.

In this thesis, multiple on-road vehicle tracking problem is explored, with greater consideration of road constraints and interactions between vehicles. A comprehensive method for tracking multiple on-road vehicles is proposed by making use of domain knowledge of on-road vehicle motion. Starting with raw measurements provided by sensors, bias correction methods for sensors commonly used in vehicle tracking are briefly introduced and a fast but effective bias correction method for airborne video sensor is proposed. In the proposed method, by assuming errors in sensor parameter measurements are close to zero, the bias is separately addressed in converted measurements of target position by a linear term of errors in sensor parameter measurements. Based on this model, the bias is efficiently estimated by addressing it while tracking or using measurements of targets that are observed by multiple airborne video sensors simultaneously. The proposed method is compared with other airborne video bias correction methods through simulations. The numerical results demonstrate the effectiveness of the proposed method for correcting bias as well as its high computational efficiency. Then, a novel tracking algorithm that utilizes domain knowledge of on-road vehicle motion, i.e., road-map information and interactions among vehicles, by integrating a car-following model into a road coordinate system, is proposed for tracking multiple vehicles on single-lane roads. This algorithm is extended for tracking multiple vehicles on multi-lane roads: The road coordinate system is extended to two-dimension to express lanes on roads and a lane-changing model is integrated for modeling lane-changing behavior of vehicles. Since the longitudinal and lateral motions are mutually dependent, the longitudinal and lateral states of vehicles are estimated sequentially in a recursive manner. Two estimation strategies are proposed: a) The unscented Kalman filter combined with the multiple hypothesis tracking framework to estimate longitudinal and lateral states of vehicles, respectively. b) A unified particle filter framework with a specifically designed computationally-efficient joint sampling method to estimate longitudinal and lateral states of vehicles jointly. Both of two estimation methods can handle unknown parameters in motion models. A posterior Cramer-Rao lower bound is derived for quantifying achievable estimation accuracy in both single-lane and multi-lane cases, respectively. Numerical results show that the proposed algorithms achieve better track accuracy and consistency than conventional multi-vehicle tracking algorithms, which assumes that vehicles move independently of one another. / Thesis / Doctor of Philosophy (PhD)

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 / This work presents different continuous-time traffic flow models. Microscopic models are considered first. A model by coupled differential equation system is proposed, based on the time gap. It incorporates a reaction time parameter and some anticipation possibilities, for each vehicle. The parameters are estimated by maximum likelihood over a two-level statistical model. Simulations allow to characterise the behaviour of a vehicles line. In a stochastic approach, a model of the distance gap evolution is studied with a zero-range process. The introduction of a reaction time parameter produces kinematics waves. On the other hand, traffic flow model by a misanthropes process is proposed. It is a mesoscopic approach, adapted to the simulation of traffic flow on a network

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.