Study On Surveillance Method Of Freeway Network Based On Lagrangian System

There are two fundamental tasks in the freeway surveillance:one is a reasonable traffic detector layout scheme, the other is traffic flow estimation for the stretches where there is no detector yet. These two task make it possible to estimating traffic flow variables (flows, mean speeds and densities, etc.), along freeway stretches, with an adequate spatial resolution at each time instant based on a limited amount of real-time traffic measurements. These two interactive task are the key component of any freeway traffic surveillance and control method or intelligent system. The accident identification, traffic state forcasting and optimizing of the traffic control strategy can not put into implement without the result of the traffic state estimation.Generally, a model-based traffic state estimators consist of a dynamic model for the state variables (e.g., a first-or secondorder macroscopic traffic flow model), a set of observation equations relating sensor observations to the system state (e.g., the fundamental diagrams), and a data-assimilation technique to combine the model predictions with the sensor observations (e.g., the Extended Kalman Filter). Commonly, both process and observation models are formulated in Eulerian (space-time) coordinates. This study have shown that this model can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates.The first aim of this paper is to formulating the macroscopic traffic flow model in the Lagrangian coordinate system (vehicle number-time). These Lagrangian coordinates are fixed to agiven fluid particle and move with it in space-time. In this new coordinate system, the purpose is no longer to determine the local density but the position X(n,t) of vehicle number n. The kinematic wave models are formulated in lagrangian coordinates as a conservation law and as a variational principle, and a new lagrangian real-time state estimator based on the Extended-Kalman-Filter technique will be proposed in this study, in which the discretized lagrangian kinematic wave models are used as the process equation, and in which observation models for both eulerian and lagrangian sensor data are incorporated. The overall traffic flow model of a freeway network can be built upon the lagrangian node models, which express the sources and sinks of the vehicle and make it possible to extend the application to a network-wide level. This Lagrangian state estimator is validated and compared with a Eulerian state estimator based on the same LWR model using an empirical microscopic traffic data set. The results indicate that the Lagrangian estimator is significantly more accurate and offers computational and theoretical benefits over the Eulerian approach.In the Lagrangian coordinates system, different data-assimilation technique were compared in this thesis. This article presents a new technique to incorporate mobile probe measurements into highway traffic flow models, named Newtonian Relaxation method, and compares it to a Kalman Filtering approach by reconstructing traffic density. The NR technique modifies the LWR partial differential equation to incorporate a correction term which reduces the discrepancy between the measurements (from the probe vehicles) and the estimated state (from the model). This technique, called Newtonian relaxation, "nudges" the model to the measurements. The KF technique is based on Kalman filtering and the framework of hybrid systems, which implements an observer equation into a linearized flow model. Both techniques assume the knowledge of the fundamental diagram and the conditions at both boundaries of the section of interest. The techniques are designed in a way in which does not require the knowledge of on-and off-ramp detector counts, which in practice are rarely available. The differences between both techniques are assessed in the context of the field data and simulation data. The results are promising, showing that the proposed methods successfully incorporate the GPS data in the estimation of traffic.This article presents a new technique of Traffic Detector Layout in Expressway Network which considers the demand of traffic state estimation and operations.The requirement of traffic information detection can be split into three hierarchy:transportation planning, operations management and traffic control. Based on which, two hierarchy of detector layout are defined in this paper:the minimum detector layout to be used for the flow detection and the optimal detector layout to be used for balancing the detection accuracy with investment. The geometric model of the expressway network is substantially equivalent a directed graph, the minimum detector quantity is the different between the number of arcs and the nodes. The minimum detector layout can be obtained by using the minimum edge control set. Estimate the traffic state of different detector layout by using the Extended Kalman Filter method, calculate the estimation deviation, and the detector configuration is optimized. A series of simulation is conducted to test the proposed method in a regional expressway network, and attest to the effectiveness of the method.In the environment of the integration vehicle infrastructure, the lagrangian estimator is more accurate and universal, offers computational and theoretical benefits over the eulerian approach. This project will greatly promote the development of traffic management and control method, and has important academic value and a broad application prospects.