International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Frequently a requirement exists to track sounding rockets or balloons from remote locations which have no radar capability. Occasionally, there is also a requirement to provide an alternative to radar tracking at those locations where it exists. TRADAT VI satisfies both requirements by providing vehicle positional from telemetry. In addition, it also provides real-time trajectory plots by its graphical display.

Mobile robots are commonly used to achieve tasks involving tracking a desired trajectory and following a predefined path in different types of terrains that have different surface characteristics. A mobile robot can perform the same navigation task task over different surfaces if the tracking performance and accuracy are not essential. However, if the tracking performance is the main objective, due to changing the characteristics of wheel-ground interaction, a single set of controller parameters or an equation of motion might be easily failing to guarantee a desired performance and accuracy. The interaction occurring between the wheels and ground can be integrated into the system model so that the performance of the mobile robot can be enhanced on various surfaces. This modeling approach related to wheel-ground interaction can also be incorporated into the motion controller. In this thesis study, modeling studies for a two wheeled differential drive mobile robot and a steerable four-wheeled robot vehicle are carried out. A strategy to achieve better tracking performance for a differential drive mobile robot is developed by introducing a procedure including the effects of external wheel forces / i.e, traction, rolling and lateral. A new methodology to represent the effects of lateral wheel force is proposed. An estimation procedure to estimate the parameters of external wheel forces is also introduced. Moreover, a modeling study that is related to show the effects of surface inclination on tracking performance is performed and the system model of the differential drive mobile robot is updated accordingly. In order to accomplish better trajectory tracking performance and accuracy for a steerable four-wheeled mobile robot, a modeling work that includes a desired trajectory generator and trajectory tracking controller is implemented. The slippage is defined via the slip velocities of steerable front and motorized rear wheels of the mobile robot. These slip velocities are obtained by using the proposed slippage estimation procedure. The estimated slippage information is then comprised into the system model so as to increase the performance and accuracy of the trajectory tracking tasks. All the modeling studies proposed in this study are tested by using simulations and verified on experimental platforms.

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Vehicle trajectory data has played a critical role in the recent history of traffic flow and CAV operations-related studies. However, available trajectories have limited coverage, either spatial or temporal. The implementation of CAV technology is expected to produce a large-scale trajectory dataset. However, at the initial implementation level, the trajectory data produced is expected to have gaps in terms of completeness. This research develops a data model for large-scale trajectory data that can be built on CAV-collected trajectories and easily manipulated to produce traffic parameters for CAV control and operation research. A benchmarking process has been applied to test a trajectory reconstruction approach to develop a population database from partial trajectories to fill the expected data gap in CAV feedback. The large-scale trajectory data is then used in CAV operations-related studies focusing on CAV's integration with human drivers and developing performance matrices for CAV-controlled optimized trajectories. This research used large-scale vehicle trajectory data from Wide Area Motion Imagery (WAMI) developed by PVLabs for modeling and analyzing traffic characteristics as a surrogate of CAV-collected trajectories. This timestamped location data capture provides trajectory information at an interval of one second. Trajectories from an approximate area of four-square kilometers in downtown Hamilton, Canada, are used to develop a data model to extract and store traffic characteristics. The video data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like other moving object detection-based algorithms, this data suffers from false-positive detection, false-negative detection, and other positional inaccuracies caused by faulty image registration. A context-based trajectory filtering algorithm has been developed and validated against ten minutes of vehicle counts from actual WAMI images. The filtered data provides a sample of trajectories over the area, including complete and partial vehicle trajectories, excluding undetected ones. The missing trajectory reconstruction process using a dynamic state estimation process is developed to reconstruct partial and missing trajectories. A data analytics approach predicts the number of missing trajectories between two successive detections in the traffic stream on a roadway lane. A benchmarking test of the performance of the missing trajectory prediction algorithm is conducted using the NGSIM I80 database. A frame-by-frame learning method is developed to join the identified missing trajectories. This data analytics approach preserves the naturalistic property of the trajectory, which was a concern of previous traffic-flow model-based approaches. Joining partial/split trajectories provides a more comprehensive picture of the trajectory population. Due to data structure similarities, including the nature of the split and missing trajectories, the methods developed in this study to recover trajectories can be adopted for future CAV feedback data in a mixed traffic scenario. The applicability of using the large-scale trajectory data model is explored in two performance areas of CAV operations. The first is a scenario-based testing process, which evaluates the "intelligence" of a CAV in handling interactions with Human driven Vehicles (HV) by artificially replacing an HV in the traffic stream with a CAV. Scenario-based testing is conducted for a particular Operational Design Domain (ODD). The ODD is defined as operating conditions under which particular driver assistance or automated control systems are designed to function. Existing literature on scenario-based testing primarily focuses on CAV-HV interaction on highways as large-scale naturalistic trajectory data are available to facilitate such studies. This research explores car-following and lane-changing aspects of arterial CAV testing. The large-scale trajectory data model generates testing scenarios and calibrates the surrogate model for CAV operation. The modification to the trajectory data model to accommodate the scenario-based testing is illustrated. The second consists of using the large-scale trajectory data model to estimate a new trajectory smoothness parameter that can indicate the impact of intersection stop-and-go movement on the smoothness of the entire trajectory. This smoothness parameter can be applied as an optimization variable in future trajectory control-based intersection management. Long-duration trajectories from the large-scale trajectory data are used to estimate the spectral arc length parameter for trajectory smoothness. This research only estimates smoothness parameters for human-driven vehicles to illustrate its applicability for vehicle trajectories. This research developed a framework for applying expected partial trajectories from CAV technology in estimating near-complete trajectories. The large-scale data application process in two CAV operations-related studies is also provided. / Doctor of Philosophy / The decision-making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior for prevailing and future traffic conditions. The analytical tools for policy, design, decision-making, and safety analysis use aggregated and disaggregated traffic parameters. Traffic parameters are information about the dynamic state of the traffic. In the case of a vehicle, the dynamic state information can be location, speed, acceleration, heading, and spacing with other vehicles in the traffic stream. The sequence of these dynamic parameters is called vehicle trajectories in a broader term. The trajectory information is collected using several direct and indirect collection systems. The implementation of CAV technologies is expected to provide a new source of vehicle trajectory information. Trajectory data are integral to CAV safety, operational evaluation, and optimization control algorithms. Trajectory data are also used to develop, calibrate, and validate the models representing a particular aspect of human driver behavior, and the recent development of CAV has elevated the necessity and application of trajectory data. As a result, a significant demand exists in academia and industry for the procedure to create trajectories of the vehicle population in the traffic stream. The trajectory population represents the dynamic properties of all the vehicles moving over the data collection area. The primary goal of this research is to develop and apply a large-scale trajectory population database. Trajectories are typically stored in a Moving Object Database (MOD). This research leverages a MOD database collected by a new generation of Wide-Area Motion Imagery (WAMI). The WAMI system collects images from a high-altitude moving aerial platform with high-definition cameras at a fixed time interval, which captures the trajectories of vehicles in the collection area. However, validating the created trajectories for completeness and data noise revealed continuity and consistency gaps in trajectories. A multistep data mining process is undertaken to filter, process, and extract sample trajectories with reduced data noise. A trajectory reconstruction task is undertaken to reduce the data gap. A benchmarking performance test for trajectory reconstruction is conducted using NGSIM I80 data because it has been validated in multiple studies and contains trajectories of all vehicles during the collection period (i.e., trajectory population). The trajectory reconstruction methodology developed in this research can be adapted for future CAV-collected partial trajectory data. The development of the trajectory reconstruction methodology and training data created from NGSIM I80 is one of the main contributions of this research in the field of trajectory reconstruction. Several traffic flow measures are then estimated from the sample trajectories that outline the analytical requirements to integrate trajectory data with roadway infrastructure. A data model is developed to store and manipulate dynamic trajectory parameters efficiently. The resulting data processing and integration process can be applied to CAV-collected trajectories to create an analytical trajectory database. The large-scale trajectory database is used to illustrate its capability in evaluating CAV operating models, specifically the car-following and lane-changing models on an arterial network. The car-following model mimics the longitudinal movement of real-world drivers following another vehicle. The lane-changing model predicts lane-changing behavior due to path-planning requirements and navigating surrounding traffic conditions. The overall operational model evaluation process is called accelerated evaluation, in which the naturalistic vehicle movement data is used to measure CAV's operational and safety performance. For a second application of the large-scale trajectory data, long-duration trajectories are used to develop a trajectory smoothness performance measure that can be used to test different trajectory control approaches for intersection movement management. This research is one of the early attempts to leverage large-scale vehicle trajectory datasets in transportation engineering applications. Its primary contribution is the development of a comprehensive trajectory validation methodology that can be applied to future CAV feedback to produce a trajectory population database with enhanced analytical capability. The secondary output of this research is benchmarking results for different analytical methodologies to develop the trajectories that can be used in future research and development as a reference.

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.

Cross median crashes have become a serious problem in recent years. Most of the median cross sections used for divided highways have terrains with steep slopes. Traffic barriers, frequently used on slopes, are generally designed based on the findings obtained from crash tests performed on flat terrain. For barriers placed on roadside and median slopes, vehicle impact height varies depending on the trajectory of the vehicle along the ditch section and lateral offset of the barrier. Thus depending on the placement location on a relatively steep slope, a barrier can be impacted by an errant vehicle at height and orientation more critical compared to those considered during its design. Hence, detailed study of performance of barriers on roadside and median slopes is needed to achieve acceptable safety performance. In this study, performances of modified G4(1S) W-beam, Midwest Guardrail System (MGS), modified Thrie-beam, modified weak post W-beam, and box-beam guardrail systems on sloped terrains are investigated using numerical simulations. A procedure is developed that provide guidance for their placement on roadside and median slopes. The research approach consists of nonlinear finite element analyses and multi-rigid-body dynamic analyses approach. Detailed finite element representation for each of the barriers is developed using LS-DYNA. Model fidelity is assessed through comparison of simulated and measured responses reported in full scale crash test studies conducted on flat terrain. LS-DYNA simulations of vehicle impacts on barriers placed on flat terrain at different impact heights are performed to identify performance limits of the barriers in terms of acceptable vehicle impact heights. The performances of the barriers are evaluated following the guidelines provided in NCHRP Report 350. Multi-rigid-body dynamic analysis code, CARSIM, is used to identify trajectories of the vehicles traversing various roadside and median cross-slopes. After analyzing vehicle trajectories and barrier performance limits, a guideline has been prepared with recommendations for the placement of barriers along roadside and median slopes. This guideline is then verified and refined using the responses obtained from full-scale LS-DYNA simulations. These simulations capture the full encroachment event from departure of the vehicle off the traveled way through impact with the barrier.

The dissertation deals with application of simulated swept paths of vehicles to road designs. Using software which generates simulated swept paths makes new demands on the designer, but Czech regulations do not stipulate the appropriate method of applying swept paths. The theoretical part of the dissertation analyses the theory of how a vehicle moves when passing through a horizontal road curve and a method of calculating a necessary extent of widening the road. The practical part compares swept paths generated by various software programs and differences between them. In order to verify the shapes and dimensions of the swept paths generated, the swept path of a real vehicle was measured. This swept path was then compared with the one generated, and the differences between them were evaluated. One of the software programs was chosen as a reference program, and its output was applied to a road design pursuant to valid regulations. The results obtained within the dissertation were used to develop certified methodology titled: „Methodology of widening road lanes in horizontal curves and of application of vehicles’ swept paths “.