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The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.
Traffic simulation models are increasingly being used in the transportation engineering profession—often, to solve complex problems that may not lend themselves to traditional analysis techniques. The application of traffic simulation models has traditionally been at the individual vehicle (microscopic) level or aggregate traffic stream (macroscopic) level. Recently, the Virginia Department of Transportation and other agencies have shown interest in mesoscopic traffic simulation models, which allow for a level of detail higher than macroscopic models and model execution times better than those of microscopic models. This study proposed a procedure for mesoscopic simulation model calibration and validation. The proposed procedure was demonstrated on a test bed along I-95 in the City of Richmond and Chesterfield County, Virginia, using Aimsun Next. Results of the case study indicated that the proposed procedure appears to be properly calibrating and validating the mesoscopic simulation model of the test bed.
(Cont.) The estimation results are tested using a calibrated Microscopic Traffic Simulator (MITSIMLab). The results are compared to the base case of calibration using only the conventional point sensor data. The results indicate that the utilization of AVI data significantly improves the calibration accuracy.
In recent years, the transport simulation of large road networks has become far more rapid and detailed, and many exciting developments in this field have emerged. Within this volume, the authors describe the simulation of automobile, pedestrian, and rail traffic coupled to new applications, such as the embedding of traffic simulation into driving simulators, to give a more realistic environment of driver behavior surrounding the subject vehicle. New approaches to traffic simulation are described, including the hybrid mesoscopic-microscopic model and floor-field agent-based simulation. Written by an invited panel of experts, this book addresses students, engineers, and scholars, as well as anyone who needs a state-of-the-art overview of transport simulation today.
The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.
Large-scale Mesoscopic traffic simulation is a newly adopted tool due to recent advancements in traffic modeling as well as computer hardware. New studies show that modeling on a scale necessary to answer complicated questions such as diversion patterns around multi-corridor work zones is feasible. As with many research projects, the original objective of this project was adjusted to maximize the benefit from the final product. The initial objective was to create a framework and guidelines for the development of a Twin Cites Mesoscopic Dynamic Traffic Assignment (DTA) model. Discoveries during the course of the project as well as MnDOT priorities and urgent needs directed the project away from the development of guidelines and more toward the proof-of-concept and the development of the foundation for such a metro-wide model. In addition, a parallel MnDOT project, undertaken by a consulting group using the DynusT application, developed an almost metro-wide model. The project described in this report, changed its scope to treat this parallel project as a case study and identify its future utility beyond its immediate goals, which were to determine the most cost-effective construction phasing for several projects during the 2017-2020 construction seasons.
The objective of this research is to build and calibrate a DTA model for Northern Nevada (Reno Sparks Area) based on the network profile and travel demand information updated to date. The critical procedures include development of consistent and readily adaptable DTA model, model validation, and calibration based on observed field data. The DTA software package used to develop the DTA model in this project is NeXTA/DTALite. Major findings: (1) Capabilities and benefits of DTA. (1a) DTA is mesoscopic in nature, providing a connection between regional travel demand forecasting and micro-simulation models. It is one step further from the planning level travel forecasting towards the operating details of micro-simulation, i.e., DTA analyzes large networks as a travel demand forecasting tool and provides time-varying traffic network performance (e.g., queue formation, bottleneck identification) but not as much detailed as micro-simulation models. (1b) Comparing with micro-simulation models which normally represent known traffic flow patterns, DTA can both represent current traffic performance and evaluate near-term traffic flow impacts from network changes. It is particularly useful to model a regional level network to forecast traffic flow pattern changes and operational impacts due to incidents such as work zone, special events, and accidents. (2) Requirements for DTA Development and Applications (2a) Geometric data, traffic control data, traffic demand, OD demand data and transit demand are basic requirements for network development. (2b) For model calibration, the fidelity of a DTA model depends on more than link volumes. Typical types of data for calibration strategies can include: travel times, travel speeds, queue information, and transit operations. (2c) Transportation modeling techniques and various levels of efforts are needed depending on the model complexity and data availability. (3) Limitations of DTA Applications. (3a) For long-term planning, DTA may not be
Dynamic Traffic Assignment (DTA) models estimate and predict the evolution of congestion through detailed models and algorithms that capture travel demand, network supply and their complex interactions. The availability of rich time-varying traffic data spanning multiple days, collected by automatic surveillance technology, provides the opportunity to calibrate such a DTA model's many inputs and parameters so that its outputs reflect field conditions. DTA models are generally calibrated sequentially: supply model calibration (assuming known demand inputs) is followed by demand calibration with fixed supply parameters. This book develops an off-line DTA model calibration methodology for the simultaneous estimation of all demand and supply inputs and parameters, using sensor data. A complex, non-linear, stochastic optimization problem is solved, using any general traffic data. Case studies with DynaMIT, a DTA model with traffic estimation and prediction capabilities, indicate that the simultaneous approach significantly outperforms the sequential state of the art. This book is addressed to professionals and researchers who apply large-scale transportation models.