Control y simulación de tráfico urbano en Colombia: Estado del arte

Las condiciones actuales de la movilidad en Colombia generan interrogantes acerca de qué tan apropiadas son las estrategias de control de tráfico aplicadas en las redes urbanas del país. Con esto en mente, se plantea una revisión de las estrategias de control y plataformas de simulación de sistemas de tráfico más utilizadas en Colombia y en otras partes del mundo; con el propósito de caracterizar el nivel de desarrollo del país en el estudio e implementación de estrategias de control de tráfico urbano y, posteriormente, formular propuestas orientadas hacia la mejora de la movilidad urbana en el país./ The current mobility conditions in Colombia give place to questions about the suitability of the traffic control strategies applied on the Colombian urban networks. Therefore, a review of the control strategies and simulation platforms used in Colombia and around the world is shown. This is done to characterize the level of development of the country, in terms of research and implementation of such control strategies and, furthermore, to formulate proposals oriented towards the improvement of the Colombian urban mobility

Macroscopic Traffic Flow Model Calibration Using Different Optimization Algorithms

AbstractThis study tests and compares different optimization algorithms employed for the calibration of a macroscopic traffic flow model. In particular, the deterministic Nelder-Mead algorithm, a stochastic genetic algorithm and the stochastic cross-entropy method are utilized to estimate the parameter values of the METANET model for a particular freeway site, using real traffic data. The resulting models are validated using various traffic data sets and the optimization algorithms are evaluated and compared with respect to the accuracy of the produced models as well as the convergence speed and the required computation time

Doctor of Philosophy

dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations

Autonomous vehicles: From vehicular control to traffic control

International audienc

Multi-Sensor Data Fusion for Travel Time Estimation

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources

DYNAMIC ORIGIN-DESTINATION DEMAND ESTIMATION AND PREDICTION FOR OFF-LINE AND ON-LINE DYNAMIC TRAFFIC ASSIGNMENT OPERATION

Time-dependent Origin-Destination (OD) demand information is a fundamental input for Dynamic Traffic Assignment (DTA) models to describe and predict time-varying traffic network flow patterns, as well as to generate anticipatory and coordinated control and information supply strategies for intelligent traffic network management. This dissertation addresses a series of critical and challenging issues in estimating and predicting dynamic OD demand for off-line and on-line DTA operation in a large-scale traffic network with various information sources. Based on an iterative bi-level estimation framework, this dissertation aims to enhance the quality of OD demand estimates by combining available historical static demand information and time-varying traffic measurements into a multi-objective optimization framework that minimizes the overall sum of squared deviations. The multi-day link traffic counts are also utilized to estimate the variation in traffic demand over multiple days. To circumvent the difficulties of obtaining sampling rates in a demand population, this research proposes a novel OD demand estimation formulation to effectively exploit OD demand distribution information provided by emerging Automatic Vehicle Identification (AVI) sensor data, and presents several robust formulations to accommodate possible deviations from idealized conditions in the demand estimation process. A structural real-time OD demand estimation and prediction model and a polynomial trend filter are developed to systematically model regular demand pattern information, structural deviations and random fluctuations, so as to provide reliable prediction and capture the structural changes in time-varying demand. Based on a Kalman filtering framework, an optimal adaptive updating procedure is further presented to use the real-time demand estimates to obtain a priori estimates of the mean and variance of regular demand patterns. To maintain a representation of the network states which consistent with that of the real-world traffic system in a real-time operational environment, this research proposes a dynamic OD demand optimal adjustment model and efficient sub-optimal feedback controllers to regulate the demand input for the real-time DTA simulator while reducing the adjustment magnitude

Traffic Flow Modeling with Real-Time Data for On-Line Network Traffic Estimation and Prediction

This research addresses the problem of modeling time-dependent traffic flow with real-time traffic sensor data for the purpose of online traffic estimation and prediction to support ATMS/ATIS in an urban transportation network. The fundamental objectives of this study are to formulate and develop a dynamic traffic flow model driven by real-world observations, which is suitable for mesoscopic type dynamic traffic assignment simulation. A dynamic speed-density relation is identified by incorporating the physical concept in continuum and kinetic models, coupled with the structural formulation of the transfer function model which is used to represent dynamic relationship. The model recognizes the time-lagged response of speed to the influential factors (speed relaxation, speed convection and density anticipation) as well as the potential autocorrelated system noise. The procedures adapted from transfer function theory are presented for the model estimation and speed prediction using the real-time data. Speed prediction is performed by means of minimum mean square error and conditional on the past information. In the context of real-time dynamic traffic assignment simulation operation, a framework based on the rolling-horizon methodology is proposed for the adaptive calibration of dynamic speed-density relations to reflect more recent traffic trends. To deal with the different time scales in the data observation interval and the traffic simulation interval, an approximation procedure is proposed to derive proper impulse responses for traffic simulation. Short term correction procedures, based on feedback control theory, are formulated to identify discrepancies between simulation and real-world observation in order to adjust speed periodically. Numerical tests to evaluate the dynamic model are conducted in a standalone manner firstly and then by integrating the model into a real-time DTA system. The overall conclusion from the results is that the proposed dynamic model is preferable in the context of real-time application to the use of conventional static traffic flow models due to its higher responsiveness and accuracy, although many other aspects remain to be investigated in further steps

On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Ph.DDOCTOR OF PHILOSOPH