Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network (original) (raw)
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arXiv (Cornell University), 2021
Metropolitan scale vehicular traffic modeling is used by a variety of private and public sector urban mobility stakeholders to inform the design and operations of road networks. High-resolution stochastic traffic simulators are increasingly used to describe detailed demand-supply interactions. The design of efficient calibration techniques remains a major challenge. This paper considers a class of high-dimensional calibration problems known as origin-destination (OD) calibration. We formulate the problem as a continuous simulation-based optimization problem. Our proposed algorithm builds upon recent metamodel methods that tackle the simulation-based problem by solving a sequence of approximate analytical optimization problems, which rely on the use of analytical network models. In this paper, we formulate a network model defined as a system of linear equations, the dimension of which scales linearly with the number of roads with field data and independently of the dimension of the route choice set. This makes the approach suitable for large-scale metropolitan networks. The approach has enhanced efficiency compared with past metamodel formulations that are based on systems of nonlinear, rather than linear, equations. It also has enhanced efficiency compared to traditional calibration methods that resort to simulation-based estimates of traffic assignment matrices, while the proposed approach uses analytical approximations of these matrices. We benchmark the approach considering a peak period Salt Lake City case study and calibrate based on field vehicular count data. The new formulation yields solutions with good performance and is suitable for large-scale road networks.
Overview Of Application Of Traffic Simulation Model
MATEC Web of Conferences
Traffic simulation is a widely used method applied in the research on traffic modelling, planning and development of traffic networks and systems. From the literature study, a variety traffic simulation models were found in experiments and applications with aims to imaginary real traffic operations. The traffic simulation models can be categorised into three namely, microscopic modelling, macroscopic modelling and mesoscopic modelling. This report is aimed to overview these traffic simulation models, in term of its function, limitation and application.
Using Real Traffic Data for ITS Simulation: Procedure and Validation
The 13th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2016)
The high levels of urban traffic is becoming a main concern in our societies, generating problems such as excessive fuel consumption and CO 2 emissions. Recently, Intelligent Transportation Systems (ITS) have emerged as a way to mitigate these problems. However, traffic analysis and improvement typically rely on simulations, which should be as realistic as possible. Meeting this requirement can be complex when the actual traffic patterns, defined through an origin/destination (O-D) matrix for the vehicles in a city, remain unknown. In this paper we propose a novel technique in order to import traffic data to the SUMO mobility simulation tool. In particular, our approach starts from induction loop measurements available from traffic authorities, and then it uses the DFROUTER tool, along with a heuristic, to generate an O-D matrix for traffic that resembles the real traffic distribution. We apply our technique to the city of Valencia, and the obtained results are then compared through simulation to other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), demonstrating the validity of our approach.
The Art of the Utilization of Traffic Simulation Models: How Do We Make Them Reliable Tools
This paper, firstly, describes the current status of the utilization of traffic simulation models. Evaluation for the model’s application in Japan was based on a questionnaire survey. Secondly, the Best Practice Manual for Simulation Application, which is currently being developed, is discussed. In addition some exerts from the manual in regards to addressing the issues of simulation application are presented: i.e. i) understanding the models’ nature through verification and validation; ii) OD estimation from vehicle counts; iii) model parameter calibration; and iv) indices to measure the reproducibility. Finally this paper introduces the Clearing House of Traffic Simulation Models. These models promote simulation utilization.
Synopsis of Traffic Simulation Models
1996
Computer simulation modeling is an established tool for assessing traffic operations. Over the past three decades, a variety of traffic simulation models have been developed, and many experiments and applications of these traffic simulation models to imaginary and real traffic operations have been conducted. This paper is intended to review widely used and newly developed models, in terms of modeling mechanisms, characteristics, and applications. Traffic simulation theories and approaches are briefly described. Simulation models developed for different traffic systems are then reviewed, including those for urban networks, freeways and integrated urban street/freeway systems. Important issues on model application are discussed.
Traffic Simulation Model Calibration Framework using Aggregate Data
A number of traffic simulation models have been developed to date, ranging from detailed microscopic to mesoscopic models. These models are being used to support a variety of traffic operations applications, such as the evaluation of infrastructure design, traffic control systems and ITS deployment strategies. However, the effectiveness of such models hinges on how field conditions are replicated by the parameters in the simulation model. Calibration of the simulation model is required in order to achieve the best reproducibility of field conditions. Model parameters include parameters that capture travel behavior (such as route and departure time choice) as well as those that affect traffic dynamics. Microscopic simulation models typically employ acceleration, lane-changing and intersection models to capture traffic dynamics. Mesoscopic models, on the other hand, rely on capacities and speed-density functions to capture queues and spillback. In addition, origin-destination (OD) flows are an important input to simulation models. However, because of the spatial extent of the applications, OD matrices, let alone accurate, dynamic ones, are not readily available. Hence input OD flows need to be estimated as part of the calibration process.
Development and Calibration of a Large-Scale Microscopic Traffic Simulation Model
The development and the calibration of a microscopic traffic simulation model, using MITSIMLab, for the entire metropolitan area of Des Moines, Iowa, are presented. The primary contributions include the application of a microscopic model on such a large-scale network and an effort for joint calibration of the model parameters and estimation of origin–destination flows. The application of microscopic traffic simulation models to very large networks such as this poses a number of methodological and practical challenges that are not faced with smaller applications. Solutions to these problems are both heuristic and analytical. The solutions presented are generic and hence applicable to any large-scale microscopic traffic modeling. Microscopic traffic simulation models have drawn significant attention from both practitioners and academicians in recent years. However, their applications are limited to small to medium-sized networks. Furthermore, the calibration of the simulation model is limited to ad hoc changes in a few driving parameters to match field conditions. Although such calibration methods often result in satisfactory performance for small networks, a much more thorough calibration that includes both estimation of origin–destination (O-D) flows and route choice and driving behavior parameters is needed for large-scale applications. This paper presents the development and calibration of a large-scale microscopic traffic simulation model using MITSIMLab (1, 2) for the metropolitan area of Des Moines, Iowa, and derives insights from this application. Simulation models have been applied to perform operational analysis of highways for a number of decades. However, their application to complex networks is fairly recent. With the development of new traffic simulation models such as AIMSUN (3), MITSIMLab, PARAMICS (4), and VISSIM (5), it is now possible to simulate increasingly larger networks with complex scenarios that involve intelligent transportation system (ITS) elements, incident scenarios , highway construction, and such. Even though the simulation of large networks is similar to that of small ones at the abstract level, it poses a number of practical (and sometime theoretical) difficulties concerning the development and calibration of such models. Some of these difficulties have not been addressed in the literature so far and are therefore a significant obstacle to the application of microscopic traffic simulation models to large-scale networks. Researchers have long been concentrating their efforts toward the calibration of microscopic simulation tools to match the field conditions. Most studies have focused on either parameter calibration or O-D estimation, but not both. Some of the methodologies adopted for calibrating parameters include simple search techniques (6), genetic algorithms (7, 8), and a simplex-based approach (9). Approaches that have been adopted for O-D estimation include generalized least squares (GLS) (10, 11), maximum likelihood (12, 13), and entropy maximization or information minimization (14). It is only recently that O-D estimation and parameter calibration are being done jointly. Liu and Fricker (15) sequentially estimate O-D flows and route choice parameters for uncongested networks by first fixing route choice parameters and estimating O-D flows and then using the estimated O-D flows as inputs to estimate the route choice parameters. Toledo et al. (16) propose an iterative approach to calibrate model parameters jointly and estimate O-D flows with aggregate data and apply the method to calibrate MITSIMLab for a test network in Stockholm, Sweden, under congested traffic conditions. This approach is also applied in Darda (17) for a network in Irvine, California. The rest of this paper is organized as follows: the next section describes the project and input development followed by a brief description of our methodology for calibration and O-D estimation. Practical challenges that were faced in the development and calibration of large-scale models are described next, followed by presentation of calibration and validation results. Finally, we provide some concluding remarks concerning the future applications of such models. PROJECT DESCRIPTION The Des Moines Area Metropolitan Planning Organization (MPO) and Iowa Department of Transportation (DOT) jointly decided to develop a large-scale microscopic traffic simulation model using MITSIMLab for the entire Des Moines area. This model is intended to complement the existing regional planning model and would enable the agencies to perform detailed operational analyses of traffic ranging from studying the impact of a planned reconstruction project that would cause significant route diversions to evacuation planning. Traditionally, only regional models are used for both short-and long-term policy decisions. In the immediate application the