Florian De Vuyst

Multilevel Assessment of the Impact of Rain on Drivers’ Behavior: Standardized Methodology and Empirical Analysis

For all road managers, inclement weather events are a source of uncertainty that can affect traffic operations and safety. Regarding safety, various studies reveal significant effects of adverse weather conditions on the frequency and severity of crashes. Regarding mobility, because of a lack of data, there are few comprehensive studies, although the quantification of the effects of adverse weather on traffic represents the first step toward the development of weather-responsive traffic management strategies. This study deals with the analysis of the impact of rain on drivers’ behavior and traffic operations. First, a generic methodology for assessing the effect of weather on traffic is proposed through a multilevel approach: from individual traffic data, the rain impact is assessed at a microscopic level (time headways, spacing). Next, the same data were used to extend the study to a mesoscopic and a macroscopic level. The mesoscopic level deals with the effects of rain on platoons, and the macroscopic level resides in the analysis of the impact of rain on the fundamental diagram enabling weather-responsive macroscopic traffic simulation. Second, following this approach, an empirical study is carried out from individual data collected on a French interurban motorway. Weather data were provided by a weather station located near the test site. The results exhibit a significant impact of rain on drivers’ behavior and traffic operations, which increases with the intensity of rainfall.

Integrating the Impact of Rain into Traffic Management: Online Traffic State Estimation Using Sequential Monte Carlo Techniques

A new approach to the integration of the effects of inclement weather into traffic management strategies is presented. Adverse weather conditions are a critical factor affecting traffic operations and safety. Previously, a methodology for the analysis of the impact of rain has been addressed, and this impact on key traffic indicators (e.g., free-flow speed, capacity) has been quantified. As a result of these quantification studies, a first parameterization of the fundamental diagram according to rain intensity has been proposed. Since the fundamental diagram represents the basis of many simulation tools, the goal is to develop weather-responsive traffic state estimation tools that can be useful for control applications and traffic management. More precisely, the online traffic state estimation takes place within a Bayesian framework with particle-filtering techniques (i.e., sequential Monte Carlo simulations) in combination with a parameterized first-order macroscopic model. This approach has already been validated for sensor diagnosis and accident detection. In this paper the goal is to show how the integration of the weather effects can improve this efficient tool. The approach is validated with real-world data from the ring road section in Lyon, France (eight sensors from a homogeneous section). The results from different scenarios show the benefits of integrating the impact of rain into traffic state estimation. Strategies to detect a rain event in time and space are also suggested.

Fast and Robust Computation of the Matrix Absolute Value Function: Application to Roe Solver for the Numerical Simulation of Two-Phase Flow Models

The application of the generalized Roe scheme to the numerical simulation of two-phase flow models requires a fast and robust computation of the absolute value of the system matrix. In several models such as the two-fluid model or a general multi-field model, this matrix has a non trivial eigenstructure and the eigen decomposition is often ill conditioned. We give two general algorithms avoiding the diagonalization process: an iterative computation, which turns out to be an exact computation, and an interpolation algorithm which is faster and can handle the case of complex eigenvalues. The knowledge of the characteristic polynomial gives us an easy access to the eigenvalues but however, the iterative scheme can be used with only estimates of the eigenvalues, using for example Gershgorin’s disk localization. We finally show some numerical results of two-fluid model simulations involving interfacial pressure and virtual mass force models.Copyright

Solid-Liquid Two-Phase Flow Measurement Using an Electromagnetically Induced Signal Measurement Method

An electromagnetically induced signal measurement method is presented to measure solid-liquid two-phase flows in the present study. The method is validated by comparing visualization results for three flow patterns of “pseudo-homogenous flow,” “heterogeneous flow” and “heterogeneous and sliding-bed flow.” The present method has demonstrated a promising capability of measuring concentration and velocity of solid particles simultaneously with good accuracy.

Bubble velocity measurement using magnetic fluid and electromagnetic induction

Bubble velocity is one of the important parameters in both fundamental studies and applications utilizing gas-liquid two-phase flow. However there are few models to estimate a bubble velocity. Even in existing models, the applicable range of a model is limited by flow condition and/or type of fluid. Another way to find a bubble velocity is measurement. A preferable measurement is a low cost and mechanically noncontact method with the fluid of interest to maintain the original bubble velocity. The method that satisfies these requirements is a measurement using optical visualization up to date. An optical visualization method is also seldom applied to the measurement in the case of complex two-phase flow. In the present paper, a measurement using magnetic fluid and electromagnetic induction is proposed. A setup, theory, and algorithm for the measurement are established. To evaluate the method, the measurements are carried out in real gas-liquid flows. One is an uprising single bubble (Taylor bubble) in a st...

Managing Sensor Data on Urban Traffic

Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embarked) sensors, generating large and complex spatio-temporal series. Research efforts in handling these data range from pattern matching and data mining techniques (for forecasting and trend analysis) to work on database queries (e.g., to construct scenarios). Work on embarked sensors also considers issues on trajectories and moving objects. This paper presents a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and database procedures to query these data. The first component is geared towards supporting pattern matching, whereas the second deals with spatio-temporal database issues. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with test conducted on 1000 sensors, during 3 years, in a large French city.

An innovating PDE model based on fluid flow paradigm for multithread systems

We propose traffic flow models of multithread systems. This can be straighforwardly extended to a network of such components. The leading equations form a system of hyperbolic equations coupled by a nonlocal term of current total load. PDEs are also used for the computation of the service times. A numerical stable and efficient method of discretization is then proposed. To illustrate the usefulness of such models, we numerically solve a problem of optimal control of quality of service (QoS) management and demonstrate the efficiency of the method.