Performance of a New Enhanced Topological Decision-Rule Map-Matching Algorithm for Transportation Applications

Indexación: Web of Science; ScieloMap-matching problems arise in numerous transportation-related applications when spatial data is collected using inaccurate GPS technology and integrated with a flawed digital roadway map in a GIS environment. This paper presents a new enhanced post-processing topological decision-rule map-matching algorithm in order to address relevant special cases that occur in the spatial mismatch resolution. The proposed map-matching algorithm includes simple algorithmic improvements: dynamic buffer that varies its size to snap GPS data points to at least one roadway centerline; a comparison between vehicle heading measurements and associated roadway centerline direction; and a new design of the sequence of steps in the algorithm architecture. The original and new versions of the algorithm were tested on different spatial data qualities collected in Canada and United States. Although both versions satisfactorily resolve complex spatial ambiguities, the comparative and statistical analysis indicates that the new algorithm with the simple algorithmic improvements outperformed the original version of the map-matching algorithm.El problema de la ambigüedad espacial ocurre en varias aplicaciones relacionadas con transporte, específicamente cuando existe inexactitud en los datos espaciales capturados con tecnología GPS o cuando son integrados con un mapa digital que posee errores en un ambiente SIG. Este artículo presenta un algoritmo nuevo y mejorado basado en reglas de decisión que es capaz de resolver casos especiales relevantes en modo post-proceso. El algoritmo propuesto incluye las siguientes mejoras algorítmicas: un área de búsqueda dinámica que varía su tamaño para asociar puntos GPS a al menos un eje de calzada, una comparación entre el rumbo del vehículo y la dirección del eje de calzada asignada, y un nuevo diseño de la secuencia de pasos del algoritmo. Tanto el algoritmo original como el propuesto fueron examinados con datos espaciales de diferentes calidades capturados en Canadá y Estados Unidos. Aunque ambas versiones resuelven satisfactoriamente el problema de ambigüedad espacial, el análisis comparativo y estadístico indica que la nueva versión del algoritmo con las mejoras algorítmicas entrega resultados superiores a la versión original del algoritmo.http://ref.scielo.org/9mt55

Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems

A new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is extended to a new problem setting in which objective function evaluations require sampling from a model of a stochastic system. The approach combines GPS with ranking and selection (R&S) statistical procedures to select new iterates. The derivative-free algorithms require only black-box simulation responses and are applicable over domains with mixed variables (continuous, discrete numeric, and discrete categorical) to include bound and linear constraints on the continuous variables. A convergence analysis for the general class of algorithms establishes almost sure convergence of an iteration subsequence to stationary points appropriately defined in the mixed-variable domain. Additionally, specific algorithm instances are implemented that provide computational enhancements to the basic algorithm. Implementation alternatives include the use modern R&S procedures designed to provide efficient sampling strategies and the use of surrogate functions that augment the search by approximating the unknown objective function with nonparametric response surfaces. In a computational evaluation, six variants of the algorithm are tested along with four competing methods on 26 standardized test problems. The numerical results validate the use of advanced implementations as a means to improve algorithm performance

Optimization of traffic simulation using GPS navigation records

Proyecto de Graduación (Maestría en Computación con énfasis en Ciencias de la Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2021A traffic simulation is a tool that permits constructing a virtual environment based on a real one, with the objective to perform analysis about the actual conditions and more important, apply changes to the virtual scene or to the driving rules to generate new scenarios and test solutions. However, the problem we found with simulations it that with incorrect parameters may not represent the traffic conditions we are looking for. In this work, we propose a method to calibrate the traffic simulations using data available for transportation in Costa Rica. This data comes from Global Position System (GPS) navigation records. The calibration algorithm search to represent those actual traffic conditions in a virtual environment, and after that, propose and design solutions to ease the complicated traffic situations. This thesis reflects the work of months to design and implemented an algorithm to calibrate simulations of five sectors of the country where we found difficult traffic conditions. The algorithm calculates a Measure of Performance to compare data from the simulation and the GPS records, and it searches iteratively for the best parameters. In the end, it validates the best solution found with a statistical test. As results, we achieved to calibrate the simulations for the five studied sectors, reaching a configuration of input parameters that reflects the traffic conditions extracted from the GPS records, as a portrait of the real-life conditions of the locations. The impact and applications of this work are plenty. For the computing part, we can dig more profound in using more techniques of calibration, and also exploit the data available for more general works. Moreover, it can become in a significant resource for analysis and decision making in urban mobility studies

Development and Simulation of a Pseudolite-Based Flight Reference System

Current flight reference systems are vulnerable to GPS jamming and also lack the accuracy required to test new systems. Pseudolites can augment flight reference systems by improving accuracy, especially in the presence of GPS jamming. This thesis evaluates a pseudolite-based flight reference system which applies and adapts carrier-phase differential GPS techniques. The algorithm developed in this thesis utilizes an extended Kalman filter along with carrier-phase ambiguity resolution techniques. A simulation of the pseudolite-based positioning system realistically models measurement noise, multipath, pseudolite position errors, and tropospheric delay. A comparative evaluation of the algorithms performance for single and widelane frequency measurements is conducted in addition to a sensitivity analysis for each measurement error source, in order to determine design tradeoffs. Other analyses included the use of optimal smoothing, non-linear filtering techniques, and code averaging. Specific emphasis is given to two alternate methods, both developed in this research, for handling the residual tropospheric error after applying a standard tropospheric model. Results indicate that the algorithm is capable of accurately resolving the pseudolite carrier-phase ambiguities, and providing a highly accurate (centimeter-level) navigation solution. The filter enhancements, particularly the optimal smoother and tropospheric error reduction methods, improved filter performance significantly

Development and Flight of a Robust Optical-Inertial Navigation System Using Low-Cost Sensors

This research develops and tests a precision navigation algorithm fusing optical and inertial measurements of unknown objects at unknown locations. It provides an alternative to the Global Positioning System (GPS) as a precision navigation source, enabling passive and low-cost navigation in situations where GPS is denied/unavailable. This paper describes two new contributions. First, a rigorous study of the fundamental nature of optical/inertial navigation is accomplished by examining the observability grammian of the underlying measurement equations. This analysis yields a set of design principles guiding the development of optical/inertial navigation algorithms. The second contribution of this research is the development and flight test of an optical-inertial navigation system using low-cost and passive sensors (including an inexpensive commercial-grade inertial sensor, which is unsuitable for navigation by itself). This prototype system was built and flight tested at the U.S. Air Force Test Pilot School. The algorithm that was implemented leveraged the design principles described above, and used images from a single camera. It was shown (and explained by the observability analysis) that the system gained significant performance by aiding it with a barometric altimeter and magnetic compass, and by using a digital terrain database (DTED). The (still) low-cost and passive system demonstrated performance comparable to high quality navigation-grade inertial navigation systems, which cost an order of magnitude more than this optical-inertial prototype. The resultant performance of the system tested provides a robust and practical navigation solution for Air Force aircraft

Resilient and Decentralized Control of Multi-level Cooperative Mobile Networks to Maintain Connectivity under Adversarial Environment

Network connectivity plays an important role in the information exchange between different agents in the multi-level networks. In this paper, we establish a game-theoretic framework to capture the uncoordinated nature of the decision-making at different layers of the multi-level networks. Specifically, we design a decentralized algorithm that aims to maximize the algebraic connectivity of the global network iteratively. In addition, we show that the designed algorithm converges to a Nash equilibrium asymptotically and yields an equilibrium network. To study the network resiliency, we introduce three adversarial attack models and characterize their worst-case impacts on the network performance. Case studies based on a two-layer mobile robotic network are used to corroborate the effectiveness and resiliency of the proposed algorithm and show the interdependency between different layers of the network during the recovery processes.Comment: 9 pages, 6 figure

SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone

The number of pedestrian accidents continues to keep climbing. Distraction from smartphone is one of the biggest causes for pedestrian fatalities. In this paper, we develop SaferCross, a mobile system based on the embedded sensors of smartphone to improve pedestrian safety by preventing distraction from smartphone. SaferCross adopts a holistic approach by identifying and developing essential system components that are missing in existing systems and integrating the system components into a "fully-functioning" mobile system for pedestrian safety. Specifically, we create algorithms for improving the accuracy and energy efficiency of pedestrian positioning, effectiveness of phone activity detection, and real-time risk assessment. We demonstrate that SaferCross, through systematic integration of the developed algorithms, performs situation awareness effectively and provides a timely warning to the pedestrian based on the information obtained from smartphone sensors and Direct Wi-Fi-based peer-to-peer communication with approaching cars. Extensive experiments are conducted in a department parking lot for both component-level and integrated testing. The results demonstrate that the energy efficiency and positioning accuracy of SaferCross are improved by 52% and 72% on average compared with existing solutions with missing support for positioning accuracy and energy efficiency, and the phone-viewing event detection accuracy is over 90%. The integrated test results show that SaferCross alerts the pedestrian timely with an average error of 1.6sec in comparison with the ground truth data, which can be easily compensated by configuring the system to fire an alert message a couple of seconds early.Comment: Published in IEEE Access, 202

Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.Comment: Proc. of IET Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms, to appea