The use of the ensemble smoother (ES) instead of the ensemble Kalman filter increases the nonlinearity of the update step during data assimilation and the need for iterative assimilation methods. A previous version of the iterative ensemble smoother based on Gauss–Newton formulation was able to match data relatively well but only after a large number of iterations. A multiple data assimilation method (MDA) was generally more efficient for large problems but lacked ability to continue “iterating” if the data mismatch was too large. In this paper, we develop an efficient, iterative ensemble smoother algorithm based on the Levenberg–Marquardt (LM) method of regularizing the update direction and choosing the step length. The incorporation of the LM damping parameter reduces the tendency to add model roughness at early iterations when the update step is highly nonlinear, as it often is when all data are assimilated simultaneously. In addition, the ensemble approximation of the Hessian is modified in a way that simplifies computation and increases stability. We also report on a simplified algorithm in which the model mismatch term in the updating equation is neglected. We thoroughly evaluated the new algorithm based on the modified LM method, LM-ensemble randomized maximum likelihood (LM-EnRML), and the simplified version of the algorithm, LM-EnRML (approx), on three test cases. The first is a highly nonlinear single-variable problem for which results can be compared against the true conditional pdf. The second test case is a one-dimensional two-phase flow problem in which the permeability of 31 grid cells is uncertain. In this case, Markov chain Monte Carlo results are available for comparison with ensemble-based results. The third test case is the Brugge benchmark case with both 10 and 20 years of history. The efficiency and quality of results of the new algorithms were compared with the standard ES (without iteration), the ensemble-based Gauss–Newton formulation, the standard ensemble-based LM formulation, and the MDA. Because of the high level of nonlinearity, the standard ES performed poorly on all test cases. The MDA often performed well, especially at early iterations where the reduction in data mismatch was quite rapid. The best results, however, were always achieved with the new iterative ensemble smoother algorithms, LM-EnRML and LM-EnRML (approx).

Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the vast majority of the data available on arterial roads. This paper proposes a probabilistic modeling framework for estimating and predicting arterial travel-time distributions using sparsely observed probe vehicles. We introduce a model based on hydrodynamic traffic theory to learn the density of vehicles on arterial road segments, illustrating the distribution of delay within a road segment. The characterization of this distribution is essentially to use probe vehicles for traffic estimation: Probe vehicles report their location at random locations, and the travel times between location reports must be properly scaled to match the map discretization. A dynamic Bayesian network represents the spatiotemporal dependence on the network and provides a flexible framework to learn traffic dynamics from historical data and to perform real-time estimation with streaming data. The model is evaluated using data from a fleet of 500 probe vehicles in San Francisco, CA, which send Global Positioning System (GPS) data to our server every minute. The numerical experiments analyze the learning and estimation capabilities on a subnetwork with more than 800 links. The sampling rate of the probe vehicles does not provide detailed information about the location where vehicles encountered delay or the reason for any delay (i.e., signal delay, congestion delay, etc.). The model provides an increase in estimation accuracy of 35% when compared with a baseline approach to process probe-vehicle data.

Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network

Survey on traffic prediction in smart cities

Due to the increase in vehicle transit and congestion in road networks, providing information about the state of the traffic to commuters has become a critical issue for Advanced Traveller Information Systems. These systems should assist users in making pre-trip and en-route decisions and, for this purpose, delivering travel time information is very useful because it is very intuitive and easily understood by all travellers. The aim of this paper is to present a global view of the literature on the modelling of travel time, introducing essential concepts and giving a thorough classification of the existing techniques. Most of the attention will focus on travel time estimation and travel time prediction, which are two of the most relevant challenges in travel time modelling. The definition and goals of these two modelling tasks along with the methodologies used to carry them out will be further explored and categorised.

A review of travel time estimation and forecasting for Advanced Traveller Information Systems

講座 highway capacity manual 2000(3)2車線道路と多車線道路

Emissions by road traffic can be reduced by optimising traffic control. The impact of this optimisation on emission can be analysed ex ante by simulation. The simulation programs used for this analysis should be valid with respect to the traffic characteristics that determine the emissions. Thus calibration of the parameters is a prerequisite. In most cases, volumes, travel times and queues are used to calibrate simulation models, rather than detailed driving characteristics such as speed and acceleration patterns. However, these driving behaviour parameters determine the vehicular emissions to a great extent.A study was carried out in which the driving behaviour parameters in a microscopic simulation model (VISSIM) were calibrated using real trajectories collected by image processing at an intersection in Rotterdam. The sensitivity of the simulation results for driving behaviour parameters was investigated. The most influential parameters were identified and adjusted to ensure that the simulation results were consistent with the observed traffic and could provide valid estimations of the total production of emissions. © 2012 Elsevier Ltd.

Calibration of a microscopic simulation model for emission calculation

Extended Brugge benchmark case for history matching and water flooding optimization

Cost issues have been an important concern in the development of Personal Rapid Transit (PRT) since the concept was developed several decades ago. The lightweight, computerguided electric vehicles operating the PRT system are generally a major part of the capital cost of the system, especially in larger network with high demand. A sufficient number of empty vehicles are needed to be moved to the stations where passengers are waiting or demand is expected. Generally a larger fleet size leads to a reduction in waiting time of passengers and thus a higher level of service given a specific demand, but an increased investment cost including capital cost per vehicle and additional operation and maintenance. So it requires a compromise between user cost (in terms of passenger waiting times) and operator cost (in terms of fleet sizedependent capital cost and operating/maintenance costs). There should be an optimal fleet size so that the sum of these two costs can be minimized while an expected level of service is achieved. This paper presents first the way to obtain the PRT demand, and then a prescription to determine the optimal fleet size using a cost-effectiveness analysis with traffic simulation. This prescription identifies the set of activities that are necessary to perform the optimization task. Each activity is regarded as a component in our general framework and this framework is illustrated by a case study in the Waal/ Eemshaven harbor area in the Port of Rotterdam, The Netherlands.

Optimizing the fleet size of a Personal Rapid Transit system: A case study in port of Rotterdam

Although there is an increasing demand for travel time prediction methods, the efforts put on urban streets are until now less than on freeways In this paper, a macroscopic model of urban link travel time prediction is developed based on measurements collected by single loop detectors This method divides travel times into two components: link cruising times and intersection delays For each component the mean prediction and prediction interval were investigated in order to provide a reliable prediction Prediction intervals around the mean prediction show the plausible range of travel times a vehicle is likely to encounter Based on simulation data, it is demonstrated that urban link travel time prediction can be executed by this promising method

Prediction of urban travel times with intersection delays

Probe Vehicle Data (PVD) is becoming more and more common for the collection of information about the traffic state In most cases, the information that can be obtained from a probe vehicle refers to the position, the speed and the direction of movement at certain time intervals Especially in urban networks, the raw GPS data needs a cleaning process to map the measured position to the road network The cleaned information about positions in the network at fixed moments where GPS signals are collected can be used to derive travel time along certain routes

Yusen Chen

Papers

Calibration of a microscopic simulation model for emission calculation

Extended Brugge benchmark case for history matching and water flooding optimization

Optimizing the fleet size of a Personal Rapid Transit system: A case study in port of Rotterdam

Prediction of urban travel times with intersection delays

Using Probe Vehicle Data for Traffic State Estimation in Signalized Urban Networks